diff --git a/2-Performance_Counters/Handson/.master/Hands-On-Performance-Counters.ipynb b/2-Performance_Counters/Handson/.master/Hands-On-Performance-Counters.ipynb
index 5c1e55d50c03cbbaa46744b94cecc912b0db53f7..4bbe06ac9a84212432b42fec70930134d8ae5461 100644
--- a/2-Performance_Counters/Handson/.master/Hands-On-Performance-Counters.ipynb
+++ b/2-Performance_Counters/Handson/.master/Hands-On-Performance-Counters.ipynb
@@ -48,7 +48,7 @@
"\n",
"For the first task, we will measure quantities often used to characterize an application: cycles and instructions.\n",
"\n",
- "**TASK**: Please measure counters for completed instructions and run cycles. See the TODOs in [`poisson2d.ins_cyc.c`](/edit/Tasks/poisson2d.ins_cyc.c). You can either edit the files with Jupyter capabilities by clicking on the link of the file or use a dedicated editor (`vim` is available). The names of the counters to be implemented are `PM_INST_CMPL` and `PM_RUN_CYC`.\n",
+ "**TASK**: Please measure counters for completed instructions and run cycles. See the TODOs in file [`poisson2d.ins_cyc.c`](poisson2d.ins_cyc.c). You can either edit the files with Jupyter capabilities by clicking on the link of the file or selecting it in the file drawer on the left; or use a dedicated editor on the system(`vim` is available). The names of the counters to be implemented are `PM_INST_CMPL` and `PM_RUN_CYC`.\n",
"\n",
"After changing the source code, compile it with `make task1` or by executing the following cell (we need to change directories first, though). \n",
"*(Using the `Makefile` we have hidden quite a few intricacies from you in order to focus on the relevant content at hand. Don't worry too much about it right now – we'll un-hide it gradually during the course of the tutorial.)*\n",
@@ -65,7 +65,24 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "/autofs/nccsopen-svm1_home/aherten/OpenPOWER-SC19/2-PAPI/Compiling/Solutions\n"
+ "/autofs/nccsopen-svm1_home/aherten/OpenPOWER-SC19/Prototyping/2-Performance_Counters/Handson/Solutions\n"
+ ]
+ }
+ ],
+ "source": [
+ "!pwd"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "/autofs/nccsopen-svm1_home/aherten/OpenPOWER-SC18/2-PAPI/Compiling/Solutions\n"
]
}
],
@@ -76,14 +93,14 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "gcc -DUSE_DOUBLE -Ofast -std=c99 -lm -lpapi poisson2d.ins_cyc.c -o poisson2d.ins_cyc.bin\r\n"
+ "gcc -DUSE_DOUBLE -Ofast -std=c99 -lm -lpapi poisson2d.ins_cyc.c -o poisson2d.ins_cyc.bin\n"
]
}
],
@@ -100,17 +117,21 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 1,
"metadata": {},
"outputs": [
{
- "evalue": "Error: Failed to connect to Jupyter notebook. \r\nhttp://localhost:8888/\r\nError: Invalid response: 500 Internal Server Error",
- "output_type": "error"
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
+ "100,64,32,0.0011,3324225,33235,33960,1859440,18357,25033\n"
+ ]
}
],
"source": [
"!./poisson2d.ins_cyc.bin 100 64 32\n",
- "# alternatively call !make run_task1, one of our shortcutts"
+ "# alternatively call !make run_task1"
]
},
{
@@ -126,7 +147,7 @@
},
{
"cell_type": "code",
- "execution_count": 80,
+ "execution_count": 2,
"metadata": {
"scrolled": true
},
@@ -135,554 +156,523 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "gcc -DUSE_DOUBLE -Ofast -std=c99 -lm -lpapi poisson2d.ins_cyc.c -o poisson2d.ins_cyc.bin\n",
- "bsub -W 60 -nnodes 1 -Is -P GEN110 jsrun -n 1 -c 1 -g ALL_GPUS ./bench.sh poisson2d.ins_cyc.bin /gpfs/wolf/gen110/scratch/aherten//poisson2d.ins_cyc.bin.csv\n",
- "Job <4318> is submitted to default queue <batch>.\n",
+ "bsub -W 60 -nnodes 1 -Is -P TRN003 jsrun -n 1 -c 1 -g ALL_GPUS ./bench.sh poisson2d.ins_cyc.bin /gpfs/wolf/trn003/scratch/aherten//poisson2d.ins_cyc.bin.csv\n",
+ "Job <24059> is submitted to default queue <batch>.\n",
"<<Waiting for dispatch ...>>\n",
"<<Starting on login1>>\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,4,0.0012,548153,2735,3888,266504,1243,4753\n",
+ "200,32,4,0.0012,572978,2861,3639,261330,1235,4684\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,8,0.0014,1082153,5405,6558,668070,3227,6573\n",
+ "200,32,8,0.0014,1082978,5411,6189,601962,2914,5099\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,12,0.0014,1442153,7205,8358,872094,4181,12974\n",
+ "200,32,12,0.0014,1442978,7211,7989,811603,3992,5761\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,16,0.0015,1802153,9005,10158,1074585,5230,7975\n",
+ "200,32,16,0.0014,1802978,9011,9789,1017305,4988,7017\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,20,0.0015,2162153,10805,11958,1281118,6236,14107\n",
+ "200,32,20,0.0015,2162978,10811,11589,1221559,6002,7999\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,24,0.0016,2522153,12605,13758,1479347,7222,10037\n",
+ "200,32,24,0.0016,2522978,12611,13389,1435167,7037,9259\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,28,0.0019,2882153,14405,15558,1682827,8251,11219\n",
+ "200,32,28,0.0016,2882978,14411,15189,1633061,8054,9789\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,32,0.0017,3242153,16205,17358,1871170,9210,12109\n",
+ "200,32,32,0.0017,3242978,16211,16989,1842895,9092,10889\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,36,0.0018,3602153,18005,19158,2075730,10193,13063\n",
+ "200,32,36,0.0018,3602978,18011,18789,2042894,10108,12457\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,40,0.0019,3962153,19805,20958,2272736,11258,14491\n",
+ "200,32,40,0.0019,3962978,19811,20589,2261332,11191,14233\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,44,0.0019,4322153,21605,22758,2491982,12249,17554\n",
+ "200,32,44,0.0020,4322978,21611,22389,2458267,12112,14375\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,48,0.0020,4682153,23405,24558,2692600,13292,16003\n",
+ "200,32,48,0.0020,4682978,23411,24189,2658621,13164,15613\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,52,0.0020,5042153,25205,26358,2878730,14277,17055\n",
+ "200,32,52,0.0020,5042978,25211,25989,2866175,14190,16864\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,56,0.0021,5402153,27005,28158,3084915,15295,18583\n",
+ "200,32,56,0.0021,5402978,27011,27789,3080357,15237,21565\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,60,0.0022,5762153,28805,29958,3291836,16330,19233\n",
+ "200,32,60,0.0022,5762978,28811,29589,3283103,16278,18799\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,64,0.0023,6122153,30605,31758,3622134,17946,20887\n",
+ "200,32,64,0.0022,6122978,30611,31389,3587582,17820,19681\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,68,0.0024,6482153,32405,33558,3930512,19200,22297\n",
+ "200,32,68,0.0025,6482978,32411,33189,3893368,19284,20847\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,72,0.0027,6842153,34205,35358,4270649,20402,22797\n",
+ "200,32,72,0.0025,6842978,34211,34989,4289441,21278,22715\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,76,0.0025,7202153,36005,37158,4209408,20894,24035\n",
+ "200,32,76,0.0024,7202978,36011,36789,4208700,20936,22677\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,80,0.0025,7562153,37805,38958,4410712,21911,24986\n",
+ "200,32,80,0.0025,7562978,37811,38589,4409613,21897,23855\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,84,0.0026,7922153,39605,40758,4631259,23020,25649\n",
+ "200,32,84,0.0026,7922978,39611,40389,4611755,22921,24910\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,88,0.0027,8282153,41405,42558,4814218,23914,26743\n",
+ "200,32,88,0.0026,8282978,41411,42189,4821904,23974,26087\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,92,0.0027,8642153,43205,44358,5039020,24944,37612\n",
+ "200,32,92,0.0028,8642978,43211,43989,5104722,25036,38488\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,96,0.0030,9002153,45005,46158,5247046,26072,29012\n",
+ "200,32,96,0.0028,9002978,45011,45789,5238952,26060,27927\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,100,0.0029,9362153,46805,47958,5426721,26963,29831\n",
+ "200,32,100,0.0028,9362978,46811,47589,5441545,27049,29275\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,104,0.0029,9722153,48605,49758,5619647,27963,31679\n",
+ "200,32,104,0.0030,9722978,48611,49389,5920763,28136,72679\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,108,0.0030,10082153,50405,51558,5828776,28956,31626\n",
+ "200,32,108,0.0030,10082978,50411,51189,5853554,29106,31403\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,112,0.0031,10442153,52205,53358,6033005,30029,32674\n",
+ "200,32,112,0.0030,10442978,52211,52989,6053498,30123,32279\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,116,0.0031,10802153,54005,55158,6244763,30994,35257\n",
+ "200,32,116,0.0031,10802978,54011,54789,6296056,31338,33377\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,120,0.0032,11162153,55805,56958,6425499,31972,34572\n",
+ "200,32,120,0.0033,11162978,55811,56589,6468115,32146,33869\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,124,0.0033,11522153,57605,58758,6654149,33094,35931\n",
+ "200,32,124,0.0032,11522978,57611,58389,6675248,33233,35075\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,128,0.0033,11882153,59405,60558,6851733,34090,36755\n",
+ "200,32,128,0.0033,11882978,59411,60189,6894325,34338,36207\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,132,0.0034,12242153,61205,62358,7052529,35058,39834\n",
+ "200,32,132,0.0034,12242978,61211,61989,7093543,35299,37463\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,136,0.0035,12602153,63005,64158,7241645,36039,38957\n",
+ "200,32,136,0.0034,12602978,63011,63789,7312105,36353,48105\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,140,0.0035,12962153,64805,65958,7438548,37024,39702\n",
+ "200,32,140,0.0035,12962978,64811,65589,7503757,37375,39247\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,144,0.0036,13322153,66605,67758,7649807,38039,46041\n",
+ "200,32,144,0.0036,13322978,66611,67389,7692611,38277,40419\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,148,0.0037,13682153,68405,69558,7837686,39006,41671\n",
+ "200,32,148,0.0037,13682978,68411,69189,7968094,39656,42113\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,152,0.0037,14042153,70205,71358,8039582,40031,42707\n",
+ "200,32,152,0.0037,14042978,70211,70989,8122466,40468,42706\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,156,0.0038,14402153,72005,73158,8272212,41195,43645\n",
+ "200,32,156,0.0038,14402978,72011,72789,8328043,41484,45104\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,160,0.0040,14762153,73805,74958,8471858,42200,44594\n",
+ "200,32,160,0.0040,14762978,73811,74589,8547674,42493,54216\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,164,0.0039,15122153,75605,76758,8657085,43103,45699\n",
+ "200,32,164,0.0039,15122978,75611,76389,8738805,43542,45427\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,168,0.0039,15482153,77405,78558,8856462,44110,46863\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
+ "200,32,168,0.0040,15482978,77411,78189,8948025,44560,46819\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,172,0.0040,15842153,79205,80358,9050337,45084,47600\n",
+ "200,32,172,0.0040,15842978,79211,79989,9186567,45735,47659\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,176,0.0041,16202153,81005,82158,9267755,46142,55546\n",
+ "200,32,176,0.0041,16202978,81011,81789,9391949,46573,70131\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,180,0.0042,16562153,82805,83958,9452041,47058,49763\n",
+ "200,32,180,0.0042,16562978,82811,83589,9549568,47559,54271\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,184,0.0042,16922153,84605,85758,9655929,48043,50875\n",
+ "200,32,184,0.0042,16922978,84611,85389,9766306,48609,58645\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,188,0.0043,17282153,86405,87558,9906002,49331,52491\n",
+ "200,32,188,0.0043,17282978,86411,87189,9974165,49613,56721\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,192,0.0043,17642153,88205,89358,10089481,50268,52937\n",
+ "200,32,192,0.0044,17642978,88211,88989,10187263,50734,52953\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,196,0.0044,18002153,90005,91158,10292606,51256,54507\n",
+ "200,32,196,0.0044,18002978,90011,90789,10386920,51763,53773\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,200,0.0045,18362153,91805,92958,10466174,52144,54851\n",
+ "200,32,200,0.0045,18362978,91811,92589,10593326,52744,54962\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,204,0.0045,18722153,93605,94758,10710242,53145,77999\n",
+ "200,32,204,0.0045,18722978,93611,94389,10791966,53796,55775\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,208,0.0046,19082153,95405,96558,10872705,54177,57081\n",
+ "200,32,208,0.0046,19082978,95411,96189,10993938,54691,56692\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,212,0.0047,19442153,97205,98358,11284063,56244,58937\n",
+ "200,32,212,0.0047,19442978,97211,97989,11183564,55716,57663\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,216,0.0047,19802153,99005,100158,11267668,56162,58869\n",
+ "200,32,216,0.0047,19802978,99011,99789,11413409,56842,65317\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,220,0.0048,20162153,100805,101958,11510801,57350,60362\n",
+ "200,32,220,0.0049,20162978,100811,101589,11747337,57952,85917\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,224,0.0051,20522153,102605,103758,11730908,58406,61013\n",
+ "200,32,224,0.0049,20522978,102611,103389,11967444,58993,147575\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,228,0.0050,20882153,104405,105558,11891323,59260,62051\n",
+ "200,32,228,0.0050,20882978,104411,105189,12176974,59986,107137\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,232,0.0050,21242153,106205,107358,12083458,60220,63113\n",
+ "200,32,232,0.0051,21242978,106211,106989,12243039,61011,62843\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,236,0.0050,21602153,108005,109158,12290078,61234,68599\n",
+ "200,32,236,0.0051,21602978,108011,108789,12454738,61985,74677\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,240,0.0051,21962153,109805,110958,12547828,62267,88616\n",
+ "200,32,240,0.0051,21962978,109811,110589,12632612,62912,64911\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,244,0.0052,22322153,111605,112758,12674066,63146,66333\n",
+ "200,32,244,0.0052,22322978,111611,112389,12844679,63954,74316\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,248,0.0052,22682153,113405,114558,12882346,64155,67081\n",
+ "200,32,248,0.0053,22682978,113411,114189,13049050,65048,67067\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,252,0.0053,23042153,115205,116358,13140221,65490,68231\n",
+ "200,32,252,0.0054,23042978,115211,115989,13274577,66113,68093\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,256,0.0054,23402153,117005,118158,13331460,66431,69187\n",
+ "200,32,256,0.0054,23402978,117011,117789,13479975,67191,69232\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,260,0.0054,23762153,118805,119958,13531478,67456,70141\n",
+ "200,32,260,0.0055,23762978,118811,119589,13702476,68321,70257\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,264,0.0055,24122153,120605,121758,13710546,68246,81094\n",
+ "200,32,264,0.0055,24122978,120611,121389,13885554,69178,71473\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,268,0.0055,24482153,122405,123558,13890638,69208,72412\n",
+ "200,32,268,0.0056,24482978,122411,123189,14091173,70236,72538\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,272,0.0056,24842153,124205,125358,14130816,70366,88752\n",
+ "200,32,272,0.0057,24842978,124211,124989,14277355,71142,73153\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,276,0.0057,25202153,126005,127158,14355067,71208,93990\n",
+ "200,32,276,0.0057,25202978,126011,126789,14477479,72149,74585\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,280,0.0057,25562153,127805,128958,14513593,72251,85857\n",
+ "200,32,280,0.0058,25562978,127811,128589,14807542,73365,106386\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,284,0.0059,25922153,129605,130758,14800806,73802,76775\n",
+ "200,32,284,0.0059,25922978,129611,130389,14919273,74349,83988\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,288,0.0059,26282153,131405,132558,14959572,74579,77267\n",
+ "200,32,288,0.0060,26282978,131411,132189,15262342,75369,108903\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,292,0.0059,26642153,133205,134358,15130033,75389,78361\n",
+ "200,32,292,0.0061,26642978,133211,133989,15457489,76550,112579\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,296,0.0060,27002153,135005,136158,15314583,76370,79151\n",
+ "200,32,296,0.0061,27002978,135011,135789,15587890,77470,113796\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,300,0.0061,27362153,136805,137958,15515700,77373,80055\n",
+ "200,32,300,0.0063,27362978,136811,137589,15736737,78474,80976\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,304,0.0061,27722153,138605,139758,15739536,78395,81351\n",
+ "200,32,304,0.0062,27722978,138611,139389,15931699,79424,85309\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,308,0.0062,28082153,140405,141558,15910915,79341,82085\n",
+ "200,32,308,0.0064,28082978,140411,141189,16127895,80426,82181\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,312,0.0063,28442153,142205,143358,16119259,80297,83271\n",
+ "200,32,312,0.0063,28442978,142211,142989,16353667,81487,91316\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,316,0.0063,28802153,144005,145158,16376727,81668,84481\n",
+ "200,32,316,0.0064,28802978,144011,144789,16544730,82526,84583\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,320,0.0064,29162153,145805,146958,16575917,82685,85800\n",
+ "200,32,320,0.0064,29162978,145811,146589,16778054,83692,85621\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,324,0.0065,29522153,147605,148758,16752101,83529,86861\n",
+ "200,32,324,0.0065,29522978,147611,148389,16975790,84670,86933\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,328,0.0065,29882153,149405,150558,16931954,84456,87199\n",
+ "200,32,328,0.0066,29882978,149411,150189,17193806,85651,95908\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,332,0.0066,30242153,151205,152358,17129562,85462,88022\n",
+ "200,32,332,0.0067,30242978,151211,151989,17391042,86658,92746\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,336,0.0067,30602153,153005,154158,17522378,87337,90235\n",
+ "200,32,336,0.0067,30602978,153011,153789,17579650,87566,101073\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,340,0.0067,30962153,154805,155958,17525540,87379,89947\n",
+ "200,32,340,0.0068,30962978,154811,155589,17823659,88601,131503\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,344,0.0068,31322153,156605,157758,17811817,88413,169057\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
+ "200,32,344,0.0069,31322978,156611,157389,18045749,89720,131352\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,348,0.0069,31682153,158405,159558,17999372,89772,92601\n",
+ "200,32,348,0.0069,31682978,158411,159189,18233228,90790,129666\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,352,0.0069,32042153,160205,161358,18204371,90776,101494\n",
+ "200,32,352,0.0070,32042978,160211,160989,18429938,91908,93827\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,356,0.0070,32402153,162005,163158,18393456,91621,107055\n",
+ "200,32,356,0.0071,32402978,162011,162789,18723870,92891,169000\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,360,0.0070,32762153,163805,164958,18567077,92476,114024\n",
+ "200,32,360,0.0071,32762978,163811,164589,18839189,93872,104313\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,364,0.0072,33122153,165605,166758,18749614,93562,96291\n",
+ "200,32,364,0.0072,33122978,165611,166389,19052230,94828,108456\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,368,0.0073,33482153,167405,168558,18957503,94465,97467\n",
+ "200,32,368,0.0072,33482978,167411,168189,19224348,95828,106832\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,372,0.0072,33842153,169205,170358,19137907,95471,98421\n",
+ "200,32,372,0.0073,33842978,169211,169989,19409746,96825,98825\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,376,0.0073,34202153,171005,172158,19350029,96457,99505\n",
+ "200,32,376,0.0074,34202978,171011,171789,19635914,97934,100015\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,380,0.0075,34562153,172805,173958,19657158,97897,122483\n",
+ "200,32,380,0.0075,34562978,172811,173589,19901265,99194,108856\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,384,0.0075,34922153,174605,175758,20019224,98872,199167\n",
+ "200,32,384,0.0075,34922978,174611,175389,20087150,100132,113306\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,388,0.0075,35282153,176405,177558,19999785,99747,102911\n",
+ "200,32,388,0.0076,35282978,176411,177189,20289560,101187,111225\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,392,0.0077,35642153,178205,179358,20188679,100586,121054\n",
+ "200,32,392,0.0076,35642978,178211,178989,20478069,102158,104431\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,396,0.0076,36002153,180005,181158,20368637,101583,105060\n",
+ "200,32,396,0.0077,36002978,180011,180789,20703541,103136,118462\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,400,0.0077,36362153,181805,182958,20628698,102607,152896\n",
+ "200,32,400,0.0078,36362978,181811,182589,20889687,104097,116051\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,404,0.0078,36722153,183605,184758,20759711,103503,111551\n",
+ "200,32,404,0.0078,36722978,183611,184389,21103371,105019,150497\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,408,0.0078,37082153,185405,186558,21008339,104552,136230\n",
+ "200,32,408,0.0079,37082978,185411,186189,21343392,106235,146574\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,412,0.0080,37442153,187205,188358,21248565,105961,109252\n",
+ "200,32,412,0.0080,37442978,187211,187989,21499750,107213,116228\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,416,0.0080,37802153,189005,190158,21446394,106998,110446\n",
+ "200,32,416,0.0081,37802978,189011,189789,21769516,108354,153304\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,420,0.0081,38162153,190805,191958,21618503,107795,119989\n",
+ "200,32,420,0.0082,38162978,190811,191589,22016040,109333,166344\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,424,0.0081,38522153,192605,193758,21778142,108604,112064\n",
+ "200,32,424,0.0082,38522978,192611,193389,22124948,110298,112586\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,428,0.0081,38882153,194405,195558,21989784,109653,120306\n",
+ "200,32,428,0.0083,38882978,194411,195189,22375892,111391,164691\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,432,0.0082,39242153,196205,197358,22191881,110730,113916\n",
+ "200,32,432,0.0083,39242978,196211,196989,22605417,112244,161120\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,436,0.0083,39602153,198005,199158,22373426,111587,115657\n",
+ "200,32,436,0.0084,39602978,198011,198789,22698406,113231,115888\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,440,0.0084,39962153,199805,200958,22596402,112638,130342\n",
+ "200,32,440,0.0084,39962978,199811,200589,22946025,114347,124840\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,444,0.0084,40322153,201605,202758,22868323,114041,124888\n",
+ "200,32,444,0.0085,40322978,201611,202389,23138571,115404,122324\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,448,0.0085,40682153,203405,204558,23084361,115132,128588\n",
+ "200,32,448,0.0086,40682978,203411,204189,23382319,116666,118990\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,452,0.0086,41042153,205205,206358,23255449,115787,156348\n",
+ "200,32,452,0.0086,41042978,205211,205989,23582320,117634,123005\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,456,0.0088,41402153,207005,208158,23400730,116742,119985\n",
+ "200,32,456,0.0087,41402978,207011,207789,23777586,118606,121054\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,460,0.0087,41762153,208805,209958,23616057,117782,125672\n",
+ "200,32,460,0.0088,41762978,208811,209589,24021078,119638,157473\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,464,0.0088,42122153,210605,211758,23845815,118769,150383\n",
+ "200,32,464,0.0089,42122978,210611,211389,24177273,120536,137152\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,468,0.0089,42482153,212405,213558,23982677,119580,123029\n",
+ "200,32,468,0.0089,42482978,212411,213189,24354431,121510,124378\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,472,0.0090,42842153,214205,215358,24183894,120688,124270\n",
+ "200,32,472,0.0090,42842978,214211,214989,24680874,122798,163001\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,476,0.0090,43202153,216005,217158,24479273,122149,125974\n",
+ "200,32,476,0.0092,43202978,216011,216789,24806941,123695,126112\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,480,0.0091,43562153,217805,218958,24768939,123125,164217\n",
+ "200,32,480,0.0091,43562978,217811,218589,25036974,124855,131240\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,484,0.0092,43922153,219605,220758,24828983,123895,127390\n",
+ "200,32,484,0.0092,43922978,219611,220389,25277560,125834,159926\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,488,0.0091,44282153,221405,222558,25011559,124768,128788\n",
+ "200,32,488,0.0093,44282978,221411,222189,25492002,126931,169890\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,492,0.0092,44642153,223205,224358,25219550,125760,132732\n",
+ "200,32,492,0.0094,44642978,223211,223989,25799993,127811,292316\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,496,0.0093,45002153,225005,226158,25447017,126853,140428\n",
+ "200,32,496,0.0094,45002978,225011,225789,25879076,128748,186367\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,500,0.0093,45362153,226805,227958,25586059,127650,131094\n",
+ "200,32,500,0.0094,45362978,226811,227589,26021482,129705,143377\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,504,0.0094,45722153,228605,229758,25796559,128739,131932\n",
+ "200,32,504,0.0095,45722978,228611,229389,26309697,130875,185497\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,508,0.0095,46082153,230405,231558,26122261,130275,141242\n",
+ "200,32,508,0.0096,46082978,230411,231189,26445482,131853,134810\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,512,0.0095,46442153,232205,233358,26303806,130890,135216\n",
+ "200,32,512,0.0097,46442978,232211,232989,26722882,133313,135480\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,516,0.0096,46802153,234005,235158,26441241,131860,137807\n",
+ "200,32,516,0.0097,46802978,234011,234789,26902984,134116,143429\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,520,0.0097,47162153,235805,236958,26620814,132726,144193\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
+ "200,32,520,0.0098,47162978,235811,236589,27143327,135173,182663\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,524,0.0097,47522153,237605,238758,26895547,133979,180810\n",
+ "200,32,524,0.0101,47522978,237611,238389,27899728,139067,143412\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,528,0.0098,47882153,239405,240558,27103175,134594,195038\n",
+ "200,32,528,0.0099,47882978,239411,240189,27539695,137281,153792\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,532,0.0099,48242153,241205,242358,27216804,135653,148537\n",
+ "200,32,532,0.0100,48242978,241211,241989,27665652,137957,156345\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,536,0.0100,48602153,243005,244158,27609711,137157,225927\n",
+ "200,32,536,0.0102,48602978,243011,243789,27888664,139123,142069\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,540,0.0101,48962153,244805,245958,27856165,138525,222412\n",
+ "200,32,540,0.0102,48962978,244811,245589,28116288,140162,167093\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,544,0.0101,49322153,246605,247758,27949313,139206,146089\n",
+ "200,32,544,0.0102,49322978,246611,247389,28395864,141365,191687\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,548,0.0102,49682153,248405,249558,28071639,140106,144061\n",
+ "200,32,548,0.0105,49682978,248411,249189,28539300,142352,144923\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,552,0.0102,50042153,250205,251358,28221254,140771,147826\n",
+ "200,32,552,0.0104,50042978,250211,250989,28772000,143499,153080\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,556,0.0103,50402153,252005,253158,28466442,141994,145849\n",
+ "200,32,556,0.0104,50402978,252011,252789,28943938,144344,160802\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,560,0.0105,50762153,253805,254958,28785863,142904,194917\n",
+ "200,32,560,0.0105,50762978,253811,254589,29192011,145318,205574\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,564,0.0105,51122153,255605,256758,28851831,143902,156411\n",
+ "200,32,564,0.0106,51122978,255611,256389,29371768,146296,173660\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,568,0.0106,51482153,257405,258558,29223120,145608,162476\n",
+ "200,32,568,0.0107,51482978,257411,258189,29607085,147402,185216\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,572,0.0108,51842153,259205,260358,29438332,146788,151895\n",
+ "200,32,572,0.0109,51842978,259211,259989,29760468,148529,150992\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,576,0.0108,52202153,261005,262158,29557331,147210,151262\n",
+ "200,32,576,0.0108,52202978,261011,261789,30001693,149671,152448\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,580,0.0108,52562153,262805,263958,29704990,148198,158557\n",
+ "200,32,580,0.0109,52562978,262811,263589,30194219,150474,161954\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,584,0.0108,52922153,264605,265758,29996452,149016,250006\n",
+ "200,32,584,0.0110,52922978,264611,265389,30465237,151575,196784\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,588,0.0109,53282153,266405,267558,30123135,150270,154069\n",
+ "200,32,588,0.0112,53282978,266411,267189,30866027,152658,345805\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,592,0.0110,53642153,268205,269358,30283611,150978,165439\n",
+ "200,32,592,0.0112,53642978,268211,268989,30806266,153631,162459\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,596,0.0110,54002153,270005,271158,30512807,152128,156216\n",
+ "200,32,596,0.0112,54002978,270011,270789,31013348,154624,161083\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,600,0.0111,54362153,271805,272958,30713954,153227,157015\n",
+ "200,32,600,0.0113,54362978,271811,272589,31227644,155782,158034\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,604,0.0113,54722153,273605,274758,31116246,155098,162946\n",
+ "200,32,604,0.0115,54722978,273611,274389,31534633,156837,219588\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,608,0.0113,55082153,275405,276558,31292429,155792,166047\n",
+ "200,32,608,0.0114,55082978,275411,276189,31675474,157869,168332\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,612,0.0113,55442153,277205,278358,31367681,156312,187819\n",
+ "200,32,612,0.0115,55442978,277211,277989,31953436,158989,218652\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,616,0.0114,55802153,279005,280158,31509163,156923,173955\n",
+ "200,32,616,0.0116,55802978,279011,279789,32108644,160138,180416\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,620,0.0115,56162153,280805,281958,31751550,158349,162413\n",
+ "200,32,620,0.0116,56162978,280811,281589,32277424,160849,182393\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,624,0.0116,56522153,282605,283758,32010052,159426,164990\n",
+ "200,32,624,0.0118,56522978,282611,283389,32423394,161797,164245\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,628,0.0116,56882153,284405,285558,32270071,160471,206182\n",
+ "200,32,628,0.0117,56882978,284411,285189,32609412,162678,167394\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,632,0.0118,57242153,286205,287358,32379821,161317,166154\n",
+ "200,32,632,0.0118,57242978,286211,286989,32869379,163975,168634\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,636,0.0118,57602153,288005,289158,32621237,162719,174455\n",
+ "200,32,636,0.0119,57602978,288011,288789,33151217,165037,223167\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,640,0.0118,57962153,289805,290958,32760054,163283,174727\n",
+ "200,32,640,0.0119,57962978,289811,290589,33341299,166215,181218\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,644,0.0119,58322153,291605,292758,32895462,163973,168568\n",
+ "200,32,644,0.0121,58322978,291611,292389,33649260,167751,199967\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,648,0.0119,58682153,293405,294558,33046462,164805,176098\n",
+ "200,32,648,0.0121,58682978,293411,294189,33719599,168221,178799\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,652,0.0120,59042153,295205,296358,33305627,166069,179927\n",
+ "200,32,652,0.0122,59042978,295211,295989,34067206,169536,235514\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,656,0.0121,59402153,297005,298158,33611780,166989,248127\n",
+ "200,32,656,0.0122,59402978,297011,297789,34164102,170144,235618\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,660,0.0121,59762153,298805,299958,33791922,168433,184984\n",
+ "200,32,660,0.0123,59762978,298811,299589,34456636,171594,235316\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,664,0.0121,60122153,300605,301758,33927065,169140,182483\n",
+ "200,32,664,0.0124,60122978,300611,301389,34541178,172177,211827\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,668,0.0124,60482153,302405,303558,34476798,171567,188679\n",
+ "200,32,668,0.0124,60482978,302411,303189,34905159,173832,222673\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,672,0.0123,60842153,304205,305358,34350802,171240,175365\n",
+ "200,32,672,0.0126,60842978,304211,304989,34988298,174422,188003\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,676,0.0123,61202153,306005,307158,34529315,172118,202239\n",
+ "200,32,676,0.0126,61202978,306011,306789,35263092,175911,185984\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,680,0.0124,61562153,307805,308958,34716545,172878,244909\n",
+ "200,32,680,0.0127,61562978,307811,308589,35503073,176323,305860\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,684,0.0126,61922153,309605,310758,35111667,174820,186347\n",
+ "200,32,684,0.0128,61922978,309611,310389,35672483,178036,180851\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,688,0.0126,62282153,311405,312558,35200811,175517,179013\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
+ "200,32,688,0.0128,62282978,311411,312189,35790039,178289,217803\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,692,0.0126,62642153,313205,314358,35391859,176015,252609\n",
+ "200,32,692,0.0128,62642978,313211,313989,36045752,179866,188983\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,696,0.0127,63002153,315005,316158,35696188,177815,200506\n",
+ "200,32,696,0.0130,63002978,315011,315789,36175144,180438,195986\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,700,0.0128,63362153,316805,317958,35825556,178736,191521\n",
+ "200,32,700,0.0131,63362978,316811,317589,36529049,182248,184897\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,704,0.0129,63722153,318605,319758,36008866,179237,218743\n",
+ "200,32,704,0.0130,63722978,318611,319389,36611747,182765,185703\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,708,0.0129,64082153,320405,321558,36282257,180511,214158\n",
+ "200,32,708,0.0130,64082978,320411,321189,36811496,183626,191140\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,712,0.0129,64442153,322205,323358,36251857,180793,191833\n",
+ "200,32,712,0.0131,64442978,322211,322989,37060383,184588,255521\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,716,0.0131,64802153,324005,325158,36828270,182903,229477\n",
+ "200,32,716,0.0132,64802978,324011,324789,37267356,185684,240236\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,720,0.0130,65162153,325805,326958,36775140,183107,213910\n",
+ "200,32,720,0.0132,65162978,325811,326589,37393434,186562,204926\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,724,0.0131,65522153,327605,328758,36946255,184028,240244\n",
+ "200,32,724,0.0133,65522978,327611,328389,37611724,187635,203956\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,728,0.0132,65882153,329405,330558,37189420,185485,206103\n",
+ "200,32,728,0.0135,65882978,329411,330189,37844476,188685,217329\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,732,0.0133,66242153,331205,332358,37526856,187108,192940\n",
+ "200,32,732,0.0136,66242978,331211,331989,38097715,189879,238003\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,736,0.0134,66602153,333005,334158,37747623,188004,201070\n",
+ "200,32,736,0.0136,66602978,333011,333789,38249665,190960,193797\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,740,0.0134,66962153,334805,335958,37844347,188709,198675\n",
+ "200,32,740,0.0137,66962978,334811,335589,38496135,191882,202980\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,744,0.0134,67322153,336605,337758,37874634,189009,203611\n",
+ "200,32,744,0.0136,67322978,336611,337389,38643004,192776,211409\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,748,0.0136,67682153,338405,339558,38360815,190893,193995\n",
+ "200,32,748,0.0138,67682978,338411,339189,38834497,193752,204307\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,752,0.0137,68042153,340205,341358,38702052,192377,222451\n",
+ "200,32,752,0.0139,68042978,340211,340989,39026422,194674,207102\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,756,0.0136,68402153,342005,343158,38548177,192033,249435\n",
+ "200,32,756,0.0139,68402978,342011,342789,39292510,195755,242534\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,760,0.0138,68762153,343805,344958,39152996,194437,272148\n",
+ "200,32,760,0.0140,68762978,343811,344589,39445808,196904,199749\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,764,0.0138,69122153,345605,346758,39070056,194876,204988\n",
+ "200,32,764,0.0140,69122978,345611,346389,39707448,198140,208159\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,768,0.0138,69482153,347405,348558,39192485,195337,208507\n",
+ "200,32,768,0.0141,69482978,347411,348189,39961335,199314,213386\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,772,0.0139,69842153,349205,350358,39509976,197063,216644\n",
+ "200,32,772,0.0142,69842978,349211,349989,40195551,200268,262442\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,776,0.0140,70202153,351005,352158,39643299,197720,238164\n",
+ "200,32,776,0.0143,70202978,351011,351789,40369481,201262,243178\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,780,0.0141,70562153,352805,353958,40047395,199611,212284\n",
+ "200,32,780,0.0143,70562978,352811,353589,40454251,201889,204769\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,784,0.0142,70922153,354605,355758,40474213,201350,218018\n",
+ "200,32,784,0.0143,70922978,354611,355389,40804167,203132,292206\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,788,0.0143,71282153,356405,357558,40369690,200941,270257\n",
+ "200,32,788,0.0144,71282978,356411,357189,40880258,203888,220805\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,792,0.0143,71642153,358205,359358,40667289,202430,244792\n",
+ "200,32,792,0.0145,71642978,358211,358989,41141375,205195,222680\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,796,0.0145,72002153,360005,361158,41245212,205315,244622\n",
+ "200,32,796,0.0145,72002978,360011,360789,41346667,205890,276619\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,800,0.0144,72362153,361805,362958,41042713,204407,249254\n",
+ "200,32,800,0.0146,72362978,361811,362589,41586665,207290,248916\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,804,0.0145,72722153,363605,364758,41137099,205254,211445\n",
+ "200,32,804,0.0147,72722978,363611,364389,41696398,208106,211465\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,808,0.0145,73082153,365405,366558,41267168,205869,210553\n",
+ "200,32,808,0.0148,73082978,365411,366189,41978951,209272,255137\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,812,0.0146,73442153,367205,368358,41538016,207083,242270\n",
+ "200,32,812,0.0148,73442978,367211,367989,42187366,209918,283393\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,816,0.0147,73802153,369005,370158,41856937,208198,257079\n",
+ "200,32,816,0.0149,73802978,369011,369789,42482639,211214,322437\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,820,0.0149,74162153,370805,371958,42581251,211598,220361\n",
+ "200,32,820,0.0149,74162978,370811,371589,42512865,212010,227823\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,824,0.0148,74522153,372605,373758,42106929,210144,214780\n",
+ "200,32,824,0.0151,74522978,372611,373389,42861251,213412,278868\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,828,0.0151,74882153,374405,375558,42954101,213100,216189\n",
+ "200,32,828,0.0151,74882978,374411,375189,42979335,214191,262439\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,832,0.0150,75242153,376205,377358,42591682,212393,217281\n",
+ "200,32,832,0.0152,75242978,376211,376989,43402619,215543,296991\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,836,0.0150,75602153,378005,379158,42833889,213607,225147\n",
+ "200,32,836,0.0152,75602978,378011,378789,43382253,216450,232179\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,840,0.0151,75962153,379805,380958,42888365,213833,258282\n",
+ "200,32,840,0.0154,75962978,379811,380589,43665001,217538,261020\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,844,0.0151,76322153,381605,382758,43234463,215605,228741\n",
+ "200,32,844,0.0154,76322978,381611,382389,43762162,218196,232967\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,848,0.0152,76682153,383405,384558,43340508,216058,240778\n",
+ "200,32,848,0.0156,76682978,383411,384189,44077885,219619,233562\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,852,0.0154,77042153,385205,386358,43964132,218702,263707\n",
+ "200,32,852,0.0155,77042978,385211,385989,44269902,220266,357562\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,856,0.0155,77402153,387005,388158,43738562,218168,230126\n",
+ "200,32,856,0.0156,77402978,387011,387789,44458368,221658,275183\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,860,0.0154,77762153,388805,389958,44071523,219837,238185\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
+ "200,32,860,0.0156,77762978,388811,389589,44599845,222530,244104\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,864,0.0155,78122153,390605,391758,44411093,221177,232408\n",
+ "200,32,864,0.0158,78122978,390611,391389,44856987,223898,229495\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,868,0.0157,78482153,392405,393558,44526424,222013,237960\n",
+ "200,32,868,0.0157,78482978,392411,393189,45070339,224667,268426\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,872,0.0158,78842153,394205,395358,45188815,224084,346189\n",
+ "200,32,872,0.0158,78842978,394211,394989,45243346,225686,238504\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,876,0.0156,79202153,396005,397158,44700630,222996,237268\n",
+ "200,32,876,0.0160,79202978,396011,396789,45425044,226467,285843\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,880,0.0158,79562153,397805,398958,45208957,224813,328325\n",
+ "200,32,880,0.0160,79562978,397811,398589,45637897,227585,255503\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,884,0.0159,79922153,399605,400758,45474656,226439,239215\n",
+ "200,32,884,0.0163,79922978,399611,400389,45922301,228540,294854\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,888,0.0160,80282153,401405,402558,45766475,227867,240911\n",
+ "200,32,888,0.0161,80282978,401411,402189,46210377,229936,317062\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,892,0.0160,80642153,403205,404358,45940503,228819,243891\n",
+ "200,32,892,0.0161,80642978,403211,403989,46224897,230736,244030\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,896,0.0161,81002153,405005,406158,45973712,229111,241548\n",
+ "200,32,896,0.0163,81002978,405011,405789,46706945,232252,393574\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,900,0.0162,81362153,406805,407958,46447521,230613,346027\n",
+ "200,32,900,0.0163,81362978,406811,407589,46846573,233803,243774\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,904,0.0163,81722153,408605,409758,46859527,233117,305572\n",
+ "200,32,904,0.0165,81722978,408611,409389,47211102,235424,247115\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,908,0.0164,82082153,410405,411558,47123610,234871,284329\n",
+ "200,32,908,0.0165,82082978,410411,411189,47420647,236067,308146\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,912,0.0166,82442153,412205,413358,47816182,237201,366650\n",
+ "200,32,912,0.0167,82442978,412211,412989,47664515,237299,252663\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,916,0.0166,82802153,414005,415158,47456504,236767,248921\n",
+ "200,32,916,0.0166,82802978,414011,414789,47825500,238210,307878\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,920,0.0165,83162153,415805,416958,47592162,237459,265738\n",
+ "200,32,920,0.0168,83162978,415811,416589,48024315,239591,249230\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,924,0.0167,83522153,417605,418758,48057683,239541,276783\n",
+ "200,32,924,0.0168,83522978,417611,418389,48204506,240348,286103\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,928,0.0167,83882153,419405,420558,48171706,239841,277682\n",
+ "200,32,928,0.0168,83882978,419411,420189,48474452,241766,272232\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,932,0.0170,84242153,421205,422358,48721591,242883,245719\n",
+ "200,32,932,0.0169,84242978,421211,421989,48643328,242408,310910\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,936,0.0169,84602153,423005,424158,48377712,241387,254877\n",
+ "200,32,936,0.0170,84602978,423011,423789,49041567,243670,350571\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,940,0.0169,84962153,424805,425958,48721762,242855,255300\n",
+ "200,32,940,0.0171,84962978,424811,425589,49009612,244295,313509\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,944,0.0170,85322153,426605,427758,49035991,243372,370914\n",
+ "200,32,944,0.0171,85322978,426611,427389,49257311,245620,259650\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,948,0.0171,85682153,428405,429558,49070436,244800,262067\n",
+ "200,32,948,0.0172,85682978,428411,429189,49415667,246533,254714\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,952,0.0171,86042153,430205,431358,49234273,245636,258683\n",
+ "200,32,952,0.0172,86042978,430211,430989,49711139,247671,319628\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,956,0.0172,86402153,432005,433158,49586922,247001,316148\n",
+ "200,32,956,0.0174,86402978,432011,432789,49856592,248552,271876\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,960,0.0172,86762153,433805,434958,49640943,247637,284307\n",
+ "200,32,960,0.0174,86762978,433811,434589,50136102,249978,265617\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,964,0.0177,87122153,435605,436758,51436885,256453,266477\n",
+ "200,32,964,0.0176,87122978,435611,436389,50925446,253713,295499\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,968,0.0178,87482153,437405,438558,51146832,254991,267861\n",
+ "200,32,968,0.0178,87482978,437411,438189,51035835,253858,318894\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,972,0.0177,87842153,439205,440358,51377929,256333,274159\n",
+ "200,32,972,0.0177,87842978,439211,439989,51188317,255334,306288\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,976,0.0179,88202153,441005,442158,51360933,256336,265049\n",
+ "200,32,976,0.0178,88202978,441011,441789,51436023,256205,289239\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,980,0.0179,88562153,442805,443958,51845435,258521,293602\n",
+ "200,32,980,0.0179,88562978,442811,443589,51703656,257814,300077\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,984,0.0180,88922153,444605,445758,52129373,259818,262711\n",
+ "200,32,984,0.0179,88922978,444611,445389,51801305,257947,349721\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,988,0.0181,89282153,446405,447558,52262963,260903,278224\n",
+ "200,32,988,0.0181,89282978,446411,447189,52056854,259676,262216\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,992,0.0182,89642153,448205,449358,52407317,261432,272849\n",
+ "200,32,992,0.0182,89642978,448211,448989,52237864,260535,269494\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,996,0.0184,90002153,450005,451158,53286503,265403,275404\n",
+ "200,32,996,0.0183,90002978,450011,450789,52526126,262024,274178\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,1000,0.0182,90362153,451805,452958,53051777,264487,273734\n",
+ "200,32,1000,0.0182,90362978,451811,452589,52578843,262284,265526\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,1004,0.0183,90722153,453605,454758,53153647,264834,340140\n",
+ "200,32,1004,0.0183,90722978,453611,454389,52896370,263840,273834\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,1008,0.0183,91082153,455405,456558,53025643,264711,274578\n",
+ "200,32,1008,0.0183,91082978,455411,456189,53074476,264385,308471\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,1012,0.0185,91442153,457205,458358,53709439,267192,353247\n",
+ "200,32,1012,0.0184,91442978,457211,457989,53382079,266422,284446\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,1016,0.0186,91802153,459005,460158,54036527,268786,339099\n",
+ "200,32,1016,0.0186,91802978,459011,459789,53434221,266486,275700\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,1020,0.0186,92162153,460805,461958,54154888,269844,327020\n",
+ "200,32,1020,0.0186,92162978,460811,461589,53712164,268036,277528\n",
"iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,1024,0.0183,92522153,462605,463758,52875104,262839,332332\n",
- "mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.ins_cyc.bin.csv .\n"
+ "200,32,1024,0.0187,92522978,462611,463389,53754294,268076,276795\n",
+ "mv /gpfs/wolf/trn003/scratch/aherten//poisson2d.ins_cyc.bin.csv .\n"
]
}
],
@@ -694,17 +684,18 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Once the run is completed, let's have a look at the data!\n",
+ "Once the run is completed, let's study the data!\n",
"\n",
"This can be done best in the interactive version of the Jupyter Notebook. In case this version of the description is unavailable to you, call the Makefile target `make graph_task1` (either with X forwarding, or download the resulting PDF)."
]
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
+ "import numpy as np\n",
"import seaborn as sns\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
@@ -714,9 +705,25 @@
"plt.rcParams['figure.figsize'] = [14, 6]"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Execute the following cell if you want to switch to color-blind-safer colors"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sns.set_palette(\"colorblind\")"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 77,
+ "execution_count": 2,
"metadata": {},
"outputs": [
{
@@ -750,8 +757,7 @@
" <th>PM_RUN_CYC (total)</th>\n",
" <th>PM_RUN_CYC (min)</th>\n",
" <th>PM_RUN_CYC (max)</th>\n",
- " <th>Instructions / Loop Iteration</th>\n",
- " <th>Cycles / Loop Iteration</th>\n",
+ " <th>Grid Points</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
@@ -761,14 +767,13 @@
" <td>32</td>\n",
" <td>4</td>\n",
" <td>0.0012</td>\n",
- " <td>548153</td>\n",
- " <td>2735</td>\n",
- " <td>3888</td>\n",
- " <td>266883</td>\n",
- " <td>1237</td>\n",
- " <td>4793</td>\n",
- " <td>21.367188</td>\n",
- " <td>9.664062</td>\n",
+ " <td>572978</td>\n",
+ " <td>2861</td>\n",
+ " <td>3639</td>\n",
+ " <td>261330</td>\n",
+ " <td>1235</td>\n",
+ " <td>4684</td>\n",
+ " <td>128</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
@@ -776,14 +781,13 @@
" <td>32</td>\n",
" <td>8</td>\n",
" <td>0.0014</td>\n",
- " <td>1082153</td>\n",
- " <td>5405</td>\n",
- " <td>6558</td>\n",
- " <td>668819</td>\n",
- " <td>3214</td>\n",
- " <td>6623</td>\n",
- " <td>21.113281</td>\n",
- " <td>12.554688</td>\n",
+ " <td>1082978</td>\n",
+ " <td>5411</td>\n",
+ " <td>6189</td>\n",
+ " <td>601962</td>\n",
+ " <td>2914</td>\n",
+ " <td>5099</td>\n",
+ " <td>256</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
@@ -791,44 +795,41 @@
" <td>32</td>\n",
" <td>12</td>\n",
" <td>0.0014</td>\n",
- " <td>1442153</td>\n",
- " <td>7205</td>\n",
- " <td>8358</td>\n",
- " <td>872913</td>\n",
- " <td>4187</td>\n",
- " <td>11640</td>\n",
- " <td>18.763021</td>\n",
- " <td>10.903646</td>\n",
+ " <td>1442978</td>\n",
+ " <td>7211</td>\n",
+ " <td>7989</td>\n",
+ " <td>811603</td>\n",
+ " <td>3992</td>\n",
+ " <td>5761</td>\n",
+ " <td>384</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>200</td>\n",
" <td>32</td>\n",
" <td>16</td>\n",
- " <td>0.0015</td>\n",
- " <td>1802153</td>\n",
- " <td>9005</td>\n",
- " <td>10158</td>\n",
- " <td>1077532</td>\n",
- " <td>5254</td>\n",
- " <td>8147</td>\n",
- " <td>17.587891</td>\n",
- " <td>10.261719</td>\n",
+ " <td>0.0014</td>\n",
+ " <td>1802978</td>\n",
+ " <td>9011</td>\n",
+ " <td>9789</td>\n",
+ " <td>1017305</td>\n",
+ " <td>4988</td>\n",
+ " <td>7017</td>\n",
+ " <td>512</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>200</td>\n",
" <td>32</td>\n",
" <td>20</td>\n",
- " <td>0.0016</td>\n",
- " <td>2162153</td>\n",
- " <td>10805</td>\n",
- " <td>11958</td>\n",
- " <td>1277957</td>\n",
- " <td>6209</td>\n",
- " <td>9015</td>\n",
- " <td>16.882812</td>\n",
- " <td>9.701562</td>\n",
+ " <td>0.0015</td>\n",
+ " <td>2162978</td>\n",
+ " <td>10811</td>\n",
+ " <td>11589</td>\n",
+ " <td>1221559</td>\n",
+ " <td>6002</td>\n",
+ " <td>7999</td>\n",
+ " <td>640</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
@@ -836,28 +837,28 @@
],
"text/plain": [
" iter ny nx Runtime PM_INST_CMPL (total) PM_INST_CMPL (min) \\\n",
- "0 200 32 4 0.0012 548153 2735 \n",
- "1 200 32 8 0.0014 1082153 5405 \n",
- "2 200 32 12 0.0014 1442153 7205 \n",
- "3 200 32 16 0.0015 1802153 9005 \n",
- "4 200 32 20 0.0016 2162153 10805 \n",
+ "0 200 32 4 0.0012 572978 2861 \n",
+ "1 200 32 8 0.0014 1082978 5411 \n",
+ "2 200 32 12 0.0014 1442978 7211 \n",
+ "3 200 32 16 0.0014 1802978 9011 \n",
+ "4 200 32 20 0.0015 2162978 10811 \n",
"\n",
" PM_INST_CMPL (max) PM_RUN_CYC (total) PM_RUN_CYC (min) \\\n",
- "0 3888 266883 1237 \n",
- "1 6558 668819 3214 \n",
- "2 8358 872913 4187 \n",
- "3 10158 1077532 5254 \n",
- "4 11958 1277957 6209 \n",
+ "0 3639 261330 1235 \n",
+ "1 6189 601962 2914 \n",
+ "2 7989 811603 3992 \n",
+ "3 9789 1017305 4988 \n",
+ "4 11589 1221559 6002 \n",
"\n",
- " PM_RUN_CYC (max) Instructions / Loop Iteration Cycles / Loop Iteration \n",
- "0 4793 21.367188 9.664062 \n",
- "1 6623 21.113281 12.554688 \n",
- "2 11640 18.763021 10.903646 \n",
- "3 8147 17.587891 10.261719 \n",
- "4 9015 16.882812 9.701562 "
+ " PM_RUN_CYC (max) Grid Points \n",
+ "0 4684 128 \n",
+ "1 5099 256 \n",
+ "2 5761 384 \n",
+ "3 7017 512 \n",
+ "4 7999 640 "
]
},
- "execution_count": 77,
+ "execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
@@ -865,40 +866,171 @@
"source": [
"plt.rcParams['figure.figsize'] = [14, 6]\n",
"df = pd.read_csv(\"poisson2d.ins_cyc.bin.csv\", skiprows=range(2, 50000, 2)) # Read in the CSV file from the bench run; parse with Pandas\n",
- "common.normalize(df, \"PM_INST_CMPL (min)\", \"Instructions / Loop Iteration\") # Normalize to each grid cell\n",
- "common.normalize(df, \"PM_RUN_CYC (min)\", \"Cycles / Loop Iteration\")\n",
+ "df[\"Grid Points\"] = df[\"nx\"] * df[\"ny\"] # Add a new column of the number of grid points (the product of nx and ny)\n",
"df.head() # Display the head of the Pandas dataframe"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's have a look at the counters we've just measured and see how they scaling with increasing number of grid points.\n",
+ "\n",
+ "*In the following, we are always using the minimal value of the counter (indicated by »(min)«) as this should give us an estimate of the best achievable result of the architecture.*"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 78,
+ "execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
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\n",
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HBxOMJqaBwwGqPeBhz85mTutsorPZi2OR/xgs5aNQtYrcc883l/2ae/e+hr17X7Ps1xUREREph8npLE8fGmVgJM3wxBRDY1N0DUyQnJwBwGG3EWqsZXN7PUakga2djTQ3uXVf9iqnUEXhrwv6RpdTtYaW0IqIiKw5lmXRP5zmsT8O89izw+zvGSNXvJXEU+Okqa6GMzcF2NhWx4bWelr8bpxqZb7mrPlQ5XS6SKUm8HjqFKxk3izLIpWawOl0lXsoIiIisghmsjm6Y0kODiR4tn+C/T1jDE9MAdAW9HD5ORF2bvDT2exT8wgpWfPfCY2NQUZH4ySTY0t+LbvdTj6vRhWVZC41czpdNDYGl2lEIiIisljyeYuBkTRd/RN0DRQ+emLJ0kxUvcfFhtY6XnFBJ6dv8NNUV1PmEctKteZDlcPhJBBoWZZrqZNL5VHNREREVgfLshiZmC6Fp66BCboGE0xnCk2+alwO1rfUcfkLImxoqSvtA6WVTDIXaz5UiYiIiMjqYVkW46kMPbEkvfEk0ZFJYqNp+ofTTKQyADgdNiIhLxfuaGZ9MUA1+93YFaDkFClUiYiIiEhFyuXzHBxMsL97jJ54kvhoYS+o2U58AD53FeFGNzs3NLGuuY4NrXW0B71UOdVMQhaPQpWIiIiIrHjZXJ5DgwkO9I4zMJxiYCRNTyxZWr7nr6sm1Ohm15YgbQEPHWEv7SEvnpqqMo9c1gKFKhERERFZUSbSGfZ3jzEwkmZkYor42CR/7J8oBag6dxUtfg8X7mjG6GhkS6SBeo868Ur5KFSJiIiISFnkLYuRiSkGhtMMDKXoH07zbP8EvfFk6Ryfu4qmuhou2NHMacUAVacAJSuMQpWIiIiILLnk5AwHesfoi6foH04xMJRmYCRFZubw1iXe2io6wl5ed9oGtnY2Egl5qa5ylHHUInOjUCUiIiIii2pobJJD0QSx0Umio7OzT6nS8aa6alr8Hl4YaaXV76HF76Yl4KHOrRkoqUwKVSIiIiJySizLIjY6ycBImthImt54iqe7RxkanyqdU+euIhLycs5pYYxIA5GQl9pq/Qoqq4u+o0VERETkpCbSGQaH0wxPTDE0PkVX/wTP9I0f1b7cW1vFlkgDl58TYVN7PeFGtwKUrAn6LhcRERGRklw+T188xeBImmhx9qlrYOKo2SeAUGMtZ2zys7m9gdaAh3BjLd7aKmzaQFfWoJOGKsMw/MA3gY3ANPAMcK1pmnHDMM4D9gG1wEHgzaZpxorPW9ZjIiIiIjI/k9NZoqNphsamiI6mOdA7zv6eMaaKrcsBAvU1rG+p49Jd7bQHPfjra2jy1VDtUgMJkVlzmamygNtN0/w5gGEYnwJuMwzjfwD3AW81TfMBwzBuAW4DrjYMw7acxxbriyEiIiKyGlmWxWhimvjYJLHRSbqjSfb3jtEbS2IdcV5zk5vztjezJVJPW8BLqLFW3fdE5uCkoco0zRHg50c89BvgemA3MGWa5gPFx++mMHt0dRmOiYiIiKx5lmUxkcoQH58qbZrbO5Tmya5hEunD9z5VVznY2FbHq/asJxLyEqivIVBfg7umqoyjF6lc87qnyjAMO4VA9QOgAzg0e8w0zSHDMOyGYTQt97Fi8BMRERFZM2Y773UNTnBwIEFPLElPLHlU4wiA1oCH0zf4Wd9aR7jRTbCxFn9dNQ67vUwjF1l95tuo4vNAErgLeO3iD2fp+f3esl4/GPSV9foyf6pZ5VHNKo9qVnlUs+VlWRbx0UkO9I7xTM8YB3pGeaZ3nFQxQLmcdjpb6rjg9FY6W3y0+D0EGmoJNrrx1mr2qRLpZ6yyzDlUGYbxaWAzsNc0zbxhGN1A5xHHA4BlmubIch+bzxseHk6Sz1snP3EJBIM+4vFEWa4tp0Y1qzyqWeVRzSqPara0LMtieGKKnliSQ4MJugYSHBycKC3fc9httAe9nGMEWddSx7pmH60BD07HsTNPk8kpvLVVqleF0c9Y+djttlOahJlTqDIM4+PA2cArTNOcLj78MFBrGMae4n1O1wHfLdMxERERkYozOZ2lbyhFbyxJTzxJbyxJbzzJ5HSh+57NVli+d8bGAOtafKxvqaM96KHKqeYRIivJXFqqbwfeD+wHfmUYBkCXaZqvNQzjKmCfYRg1FFucAxRnspbtmIiIiMhKNtt9rzuapDuWoKf4b3zs8N5PtdUO2oNeztveTCTopT3oJRLyqnW5SAWwWVZ5lsKVwTqgS8v/ZD5Us8qjmlUe1azyqGbPL5vLMziSpjuaoDtaaB7RHU2QmsoCYANCTW4ioUJoigS9tIc8+OtqlmTjXNWr8qhm5XPE8r/1FCZx5mS+jSpEREREpGhyOlvqutcdTdAdS9IXT5HN5QGoctppD3o42wjRGfYSCftoD3qocelXMJHVRD/RIiIiIieRtyyGxibpiaXoiydLQSo2Nlk6x1tbRWfYy2W72+kIFQJUc1OtWpeLrAEKVSIiIiJFs/c+9Q+lCh/DKfriKXrjKaZnis0jgGBjLR1hLxee3kJHyEtH2EeD17Uky/dEZOVTqBIREZE1KTaa5pH9Q8RG04ynMowlpxkYTjOVyZXO8bmraAt4uOj0FtpDheYRbQGPmkeIyFEUqkRERGTVi46m6eqfYDQ5zWhimv3dY3THkkAhONV7XNR7XFy4o4XWgJvWgIeWgIc6t6vMIxeRSqBQJSIiIqvGRDrDk10jTKRnmJ7JMZac5omuEWKjh+99qq5y0B7y8IZLN3G2ESRQX1vGEYvIaqBQJSIiIhUnb1n0xpL0DaVITs6QSM+wv3uUA33jHLlbTHWVA6OjgRfvjmB0NOCvq6G2Wr/+iMji0n9VREREZEWbnsnxx75xhsenGE0Wmkg8dWiURHqmdI4NaA952XvBOs7cHCDYUEt1lQOnQ533RGTpKVSJiIjIipGeyhIdTTOamGZ4fIonD47w5KFRZrL50jmNvmp2rG9i27omNrTW4XO7cFc7sdvVeU9EykOhSkRERJadZVmMTEwzkphiZGKa3niSJw+OcnBw4qjle4H6Gl54Riunb/TT3OSmwVtNlVOzTyKysihUiYiIyJKxLIv4+BRDY5NMpDOMJTL8sW+cA71jTByxfM9ht7G+tY69F6yjM+yjwVdNo6+aeo/2fhKRlU+hSkRERBaNZVkMjU/RNTCB2T3G413DxMemjjonUF/D9vV+NrXVEWiopdFXTbC+Vns/iUjFUqgSERGRUzaeyvBM7zgHByc4OJjg4MAEqaksUOi8d1pnI5ef00F70IPP7aLO48JbW1XmUYuILC6FKhERETmpvGUxMj5F/3CK8cejmAeH+WPfONHi/k8Ou422gIezjSDrmutY1+KjPehV9z0RWRMUqkREROQoyckZemLJ4keCvniKgeE00zO50jn1XhcbWuq4+Mw2NrXX0xHy4qrS8j0RWZsUqkRERNaofN4iNjZZCk890SQ98SQjE9Olc+rcVbQFvVx0egutQQ+tfg+nbw0zlZp+nlcWEVlbFKpERETWgKlM9ojZp8JHbzxJZqaw/5PdZqPF72ZLewORkLf0Ue+tPua1fG6XQpWIyBHmFKoMw/g08HpgHbDTNM3Hi48fBKaKHwA3mab54+Kx84B9QC1wEHizaZqxpTomIiIiBampGboHExyKJjkUTXBoMEF0JM3s9k+eGieRkJcXntFKJOSlI+SjNeCmyqnleyIip2KuM1X/AnwOuP84x66YDVmzDMOwAfcBbzVN8wHDMG4BbgOuXopj833TIiIiq4FlWYwmpumOJumOJjgUTdAdTTI8cbiFub+umo6wj/O2h+kI+egIe2n0VWvvJxGRRTSnUGWa5gMAhmHM9XV3A1OzzwPupjCzdPUSHRMREVnVEukM0ZFJoqNp+odSxRCVJDlZ2EDXBoSa3Gxsq+NPdrXRGS4EKJ/bVd6Bi4isAYtxT9W3irNIDwDvN01zDOgADs2eYJrmkGEYdsMwmpbimGmaI4vwPkRERFaE1NQMBwcSdA0U9n7qGphgNHH4Hianw0ZbwMtZmwN0hH10hn20hzzUuHSrtIhIOSz0v74XmabZYxhGNfD3wF3Amxc+rKXj93vLev1g0FfW68v8qWaVRzWrPGu1Zvm8RWw0TVf/OF39ExwcmKCrf5zB4XTpnNaAh52bAmxqbyAS9tEa8BBqcpd9/6e1WrNKpXpVHtWssiwoVJmm2VP8d9owjC8CPyge6gY6Z88zDCMAWKZpjhiGsejH5jPm4eEk+bx18hOXQDDoIx5PlOXacmpUs8qjmlWetVKzqUyW3niq0HXviO57U5nC3k+zy/ciIS8X7mhmXUsd65p9eGqqnvNKFqMjqWUf/5HWSs1WC9Wr8qhm5WO3205pEuaUQ5VhGB7AaZrmeHH53xuBR4uHHwZqDcPYU7wH6jrgu0t4TEREZEWwLIuh8amjwlNPLElsbLJ0Tm21k0jQw4U7WmgPeYiEfLQFPVRr81wRkYo015bqdwKvA5qB/zQMYxjYC3zPMAwH4ACeBN4JYJpm3jCMq4B9hmHUUGx/vlTHREREysGyLOLjUxwcmCjdA9UdSzA5fcTsU2MtHWEvF+xsLu395K+rUfc9EZFVxGZZ5VkKVwbrgC4t/5P5UM0qj2pWeVZizX77VJTHnx1h27pGztgUoLbaSS6fZzQxTU80SddgohCkBhOl7ntOh41IyMe6Zh+RsJdI0EtbcHU2j1iJNZMTU70qj2pWPkcs/1tPYRJnTlbff+lFREROUXoqy30/NfnNE1FcTjsPPDaA02Gj3lPNaGKafPEPkXabjbagh7M2B1jfUsf6ljragp6yN48QEZHyUKgSEZE1a3I6S2x0ku7iprm/fybOWCLDa/as5+Xnd9I1MMHDZpyJdAZ/XQ3++hraA14iYa/ufxIRkRKFKhERWRPyeYueWJInD47w5KFRemNJxlOZ0vFql4P1zT6uf/UONrbVA7C5vYHN7Q3lGrKIiFQIhSoREVl1Jqez9MSSHIom6Ikm6YknGRhKkcnmAWgLeti5wU+4qZZwo5v2kJdQYy12NY8QEZFToFAlIiIVy7IsxlOZUtvy7miCQ9EksZE0sy2JfO4qIiEvl5zVRmezj9M6G2nwVpd13CIisrooVImISEXI5vIMDqfpjiVKIaonliSRnimdE6ivoSPs4/ztYTrDPjrCPhq8LrUvFxGRJaVQJSIiK05ycoae6NHhqX84RTZXmH9yOuy0BT2csSlAJOSlI+SlPeTFU1NV5pGLiMhapFAlIiJlk89b9EQT/MGMHhWgRhPTpXPqPS4iIS/b1zcVNs8N+2huqsVhV/tyERFZGRSqRERkWcw2jyh8FGah+uKHm0c47DZa/G62djQQCfkKASrkpc7jKvPIRUREnp9ClYiILCrLshiZmOZQNEH3EUv4hsanSud4apyl5hHbNgZoqHXS4vdQ5dTsk4iIVB6FKhEROWV5yyI+NsmhwUQhRA0Wuu8lJwvNI2xAuMnN+pY6XnhGa2n2qdFXXWoeEQz6iMcTZXwXIiIiC6NQJSIic5LPWwyMpIvBKcGhwQTdsQST0zmgsHyvPehl15ZAofNes4/2gJdql6PMIxcREVlaClUiInIUy7KYSM/QH0/SN5SifyhVWsI3e/+Ty2knEvJy3vZmOsM+OsM+2oIenA4t3xMRkbVHoUpEZA3L5vIMjqQLy/cGE3THkvTFk6SmsqVz3NVO2oMeLj6zjc5mL51hH81+t7rviYiIFClUiYisEampGXqihRmn2Q10+4cO7/1UXeUgEvKye2uI1oCHtoCH1oCHeo82zxUREXk+ClUiIquMZVnExibpiRbDUzRJTzzJyMThvZ/qins/Xba7sPdTZ9hHc5Mbu13hSUREZL5OGqoMw/g08HpgHbDTNM3Hi49vAb4O+IFh4C9M0zxQjmMiImvVbPe93liK3niSroEJ/tg3Xlq+Z7cV9n7a0t5Q6rwXCXmp91aXeeQiIiKrx1xmqv4F+Bxw/3Mevxv4gmma9xmG8WZgH3BpmY6JiKx6E6kMvfEkvfFCgOorNpLIzBSaR9iAloCHXVuCbGyrpyPspS3gocqp7nsiIiJL6aShyjTNBwAMwyg9ZhhGCNgFvLj40D8AdxmGEaTw/+ulFSo2AAAgAElEQVTLdsw0zfg837OIyIo2PZOjf6gQnGZnoPriSSbSM6VzfO4q2oNeXnhGK+1BL+3BQoBS+3IREZHld6r3VEWAPtM0cwCmaeYMw+gvPm5b5mMKVSJSsaYzOboGJjjQN073YILeeJLY6CRW8XiV005rwMPOjf5CeAoVAlS9x1XWcYuIiMhha65Rhd/vLev1g0FfWa8v86eaVZ6VWrOJVIau/vHixwTP9o3TE02QyxciVGvAw4b2Bi49p4POljrWtdTR7PfgWAPNI1ZqzeTEVLPKonpVHtWsspxqqOoB2gzDcBRnjRxAa/Fx2zIfm5fh4ST5vHXyE5dAMOgjHk+U5dpyalSzyrMSajbbPOLI7nvdsSSjicPd9+q9LjpCPl56bgeb2+vZ0FqPt7bqOa9kMTKcXN7Bl8FKqJnMj2pWWVSvyqOalY/dbjulSZhTClWmacYMw3gUuBK4r/jv72fvb1ruYyIi5TI9k6MvnjrcujxWaF8+nckBh7vvGR0NdIR8pe57dVq+JyIismrMpaX6ncDrgGbgPw3DGDZNcztwHfB1wzA+CIwCf3HE05b7mIjIkrIsi/FUprBxbrSwcW5PLMngSBqrOPldW+0gEvSyZ2cLHSEvEXXfExERWRNsllWepXBlsA7o0vI/mQ/VrPIsRs1y+TyDw+lCgCqGp55o4qjue4H6mtKsU0e4MAMVqK/BZlv99z8tNv2cVR7VrLKoXpVHNSufI5b/rQcOzvV5a65RhYjIkdJTWXrjyaNmoPqGUsxkC3s/OR022gJeTt8YIBL2FmagQl7cNc+9/0lERETWKoUqEVkTLMtieGKqOOs0OwOVID42VTrHW1tFR9jLpbvaSvc/NfvdOB32Mo5cREREVjqFKhFZdWayefqHUsXle4cbSKSns0ChnWioyU1ncx0Xnd5KR9hLJOSjwevS8j0RERGZN4UqEaloiXSm1DSiO5pkYCR91N5Prio7kaCXF5wWIhL20RHy0hb0UOPSf/5ERERkcei3ChGpCHnLIj46WVq2112cfTpy76cGr4tNkUa2r2ssNZAINdRiXwOb54qIiEj5KFSJyIozPZMrNY+Y3UC3N5ZieuaIvZ8CbrZ2NBAJ+YiEi3s/uV3qmCQiIiLLTqFKRMpmdu+nwqxTorSELzr6nL2fQj4uOr2lNPvUGnBr7ycRERFZMRSqRGRZzO79dOS+T92xJInj7P30gtNCdBTvf/Jr7ycRERFZ4RSqRGTRze79NLvvU3csSV88RTZ39N5PZ2wKFGaftPeTiIiIVDCFKhE5ZaW9n4pNI7qLG+gOjR+799NlZ7cTCXmJhL00N2nvJxEREVk9FKpEZE5m937qLt77dLy9n8JNbta31HHxma2FAKW9n0RERGQNUKgSkWPM7v0027a8J5ZgYDh97N5P28KlpXvtQS/VLjWPEBERkbVHoUpkDTty76fZ+5+eu/dTo6+aSKhw/1NH2Eck5NXeTyIiIiJHUKgSWSOmMzl6h2b3fSrMPj1376fWE+z9JCIiIiInplAlsgpNpDJ0F1uWd0cThb2fRtIUt36ittpJR8hb2Psp7KUjpL2fRERERE6VQpVIBctbFkNjk3RHk3THCuHpUDTBeDJTOmd276dzj7j/SXs/iYiIiCyeBYcqwzAOAlPFD4CbTNP8sWEY5wH7gFrgIPBm0zRjxecs+jGR1W56JkdfPEVPLHF4A91YkunM4eV7LQE32zqb6Ax7iYR9dIS9eLT3k4iIiMiSWqyZqitM03x89hPDMGzAfcBbTdN8wDCMW4DbgKuX4tgivQeRFWM8OV0KTrMNJAZH0ljF9Xs1LgeRkJcLdzQXNs8N+2gPerR8T0RERKQMlmr5325gyjTNB4qf301hZunqJTomUpFy+TyDw+nSxrmF/Z8STKRnSuf46wrL987ZGipunusjUF+DXcv3RERERFaExQpV3yrOJD0AvB/oAA7NHjRNc8gwDLthGE1Lccw0zZFFeh8iSyY9laU3fnj2qTuWpC+eIpvLA+B02GgNeDh9Y6A4++SlPaTleyIiIiIr3WKEqotM0+wxDKMa+HvgLuCfF+F1l4Tf7y3r9YNBX1mvL/M335pZlkVsdJKu/nG6+sbpGpjg2b5xoiPp0jk+t4sNbXWcZYRY31rPhrZ62kNenA77Yg9/TdLPWeVRzSqPalZZVK/Ko5pVlgWHKtM0e4r/ThuG8UXgB8DngM7ZcwzDCACWaZojhmF0L/ax+Yx3eDhJPm+d/MQlEAz6iMcTZbm2nJqT1Wwmm6d/KEV3LEFP9HDziPR0FgAbEGpy01G8/6kj7CUS8tHgdR3TfW90JLWUb2XN0M9Z5VHNKo9qVllUr8qjmpWP3W47pUmYBYUqwzA8gNM0zfHi8r83Ao8CDwO1hmHsKd4DdR3w3eLTluKYyJKbSGeK9zwlSx34BofT5Ioh3VVlJxL08oJt4cLyvZCX9qCXapeaR4iIiIisZgudqQoD3zMMwwE4gCeBd5qmmTcM4ypgn2EYNRTbnwMsxTGRxTSTzTE4Mkn/UIqRVA/mwRG6ownGjtj7qdFXTSTk5cxNATrCPiIhL6GGWux2NY8QERERWWtsllWepXBlsA7o0vI/OdLkdJZn+sY50DtOXzxJ/3Ca2Ojh1uUOu40Wv6fUOCJS3DzX53aVd+ByQvo5qzyqWeVRzSqL6lV5VLPyOWL533oKkzhzslQt1UVWFMuyGE9l6I4m6RtK0j+UojeeoieaJG9Z2G02wk21tAc9vGBriLagh1a/hx1GiLHR9MkvICIiIiJrlkKVrDr5Yve97miCQ9FCA4nu5+z9VO910er38PLzO9na0cDGtnqqq46990mb6YqIiIjIyShUSUWbyeboG0rRHU2WAlRPLMn0TA4oLN9rC3jYudFPR9hXaB6hvZ9EREREZBEpVElFyOYKrcv7hlJER9JERyfpiycZOKL7Xo3LQUfIy0WntxAJe+kM+2gNeLT3k4iIiIgsKYUqWXGmMll6YsnS7FN3NEH/UIpsrhCebIC/vobWgIczNwfoCPnoCHsJNNRit6n7noiIiIgsL4UqKauJVKZ071N3NEl3LElsJM1sf0ZvbRWdYS+X7Y4Uuu8FvYQaa3Wvk4iIiIisGApVsiwsyyI+PkX3YILuWDFAPWfvp0B9DR1hH+dvCxfufwp7afRVY9Psk4iIiIisYApVsuhmsoX7n3piyeIyvgTdsSST01kA7DYbLQE3p3U20RH2lgKUmkeIiIiISCVSqJJ5yVsW/fEUNrsNd3Xh26cvniwFqJ5YoXlEvrh7rstpJxLyct62cKl5RFvAg+s47ctFRERERCqRQpWclGVZ9MVTPPhUlN88McjwxPRxz2v0VRMJeTlzc4BIyEsk5CXc6MZu1/I9EREREVm9FKrkuCZSGZ48OMITXSM8cXCEsWQGu83Gjg1NvHrPBlxVdtJTWXJ5i9aAh0jIi7dWy/dEREREZO1RqBIyM7Mb6CboiSV5pm+c7mgSAE+Nk23rmti+vokzNgWo97jKPFoRERERkZVFoWqNGU9l6CmGp+5iE4nBkTTFW6CocTlY1+zjdS/cwPb1TXSGfVq+JyIiIiLyPBSqVql83iI6mi7u/VQIUT3RJOOpwy3M/XXVREI+dhuhwh5QYR+B+hptoCsiIiIiMg8KVavAVCZLbyxVCk/d0SR98SSZbB4Ah91GW8DDjvVNRMI+OkJe2nUPlIiIiIjIolCoqiCWZTGamC4t3ZtdxhcbnaS4eg9PjZNIyMslZ7WVOvC1Bjw4Hfayjl1EREREZLWquFBlGMYW4OuAHxgG/sI0zQPlHdXiy+byDA6niwEqQXe0sAdUcnKmdE6ooZZI2MsFO5qJhAob6Db6qrFp+Z6IiIiIyLKpuFAF3A18wTTN+wzDeDOwD7i0zGNakPRUlt54oWlEd/Hep76hJNlcYf6pymmnPehh15ZAKTy1B73UVldi+UREREREVpeK+q3cMIwQsAt4cfGhfwDuMgwjaJpmvHwjmx/Lsni2f4JHnxni0QND9A2lSse8tVV0hr1ctjtCR6jQPKK5qRaHXcv3RERERERWoooKVUAE6DNNMwdgmmbOMIz+4uMVE6p++dggX/3hU9htNrZE6nntCzfQGfYSCflo8Lq0fE9EREREpIJUWqhaML/fW9brB4M+Lj3XRWvYx/YNfrxubaa70gWDvnIPQeZJNas8qlnlUc0qi+pVeVSzylJpoaoHaDMMw1GcpXIArcXH52R4OEk+b538xCUQDPqIxxMAbAh7mUxNM5maLstYZG6OrJlUBtWs8qhmlUc1qyyqV+VRzcrHbred0iRMRd2oY5pmDHgUuLL40JXA7yvpfioREREREVldKm2mCuA64OuGYXwQGAX+oszjERERERGRNaziQpVpmk8D55Z7HCIiIiIiIlCBoWoBHFBYJ1lO5b6+zJ9qVnlUs8qjmlUe1ayyqF6VRzUrjyO+7o75PM9mWeVp2lAGe4D7yz0IERERERFZ8S4CHpjryWspVFUD5wADQK7MYxERERERkZXHAbQAvwPm3KZ7LYUqERERERGRRVdRLdVFRERERERWGoUqERERERGRBVCoEhERERERWQCFKhERERERkQVQqBIREREREVkAhSoREREREZEFUKgSERERERFZAIUqERERERGRBVCoEhERERERWQCFKhERERERkQVQqBIREREREVkAhSoREREREZEFUKgSERERERFZAIUqERERERGRBVCoEhERERERWQCFKhERERERkQVQqBIREREREVkAhSoREREREZEFUKgSERERERFZAIUqERERERGRBVCoEhERERERWQCFKhERERERkQVQqBIREREREVkAhSoREREREZEFUKgSERERERFZAIUqERERERGRBXCWewDLqBo4BxgAcmUei4iIiIiIrDwOoAX4HTA91yetpVB1DnB/uQchIiIiIiIr3kXAA3M9eS2FqgGA0dEU+bxVlgH4/V6Gh5NlubacGtWs8qhmlUc1qzyqWWVRvSqPalY+druNxkYPFLPDXK2lUJUDyOetsoWq2etLZVHNKo9qVnlUs8qjmlUW1avyqGZlN6/bhdSoQkREREREZAEUqkRERERERBZAoUpERERERGQB1tI9VceVy2UZHY2TzWaW/FqxmJ18Pr/k15HFs1g1czpdNDYGcTjW/I+ciIiIrGHpqSw9sQTd0STd0QTdsSSZbJ6PXvMCnI7Kne9Z87/hjY7Gqalx4/E0Y7PZlvRaTqedbFahqpIsRs0syyKVmmB0NE4g0LJIIxMRERFZuSzLYjQxTXesEJ56okkORRMMjU+Vzqn3uIiEvWzrbMJuX9rfw5famg9V2WxmWQKVrF02mw2Pp45kcqzcQxERERFZdPm8xeBIujTz1B0tzEQlJ2dK54Qba1nfUsfFZ7bSEfbREfJS760u46gX15oPVYAClSw5fY+JiIjIajA9k6M3nqTniOV7vcUlfABOh422oJezNgcK4SnspT3opbZ6dceO1f3uRERERETklCTSmWOW7w2OpLGKW2i5q510hL1cclYbkZCXzrCPZr+7ou+NOlUKVSvMFVfsxeVyUVXlIp/P8Za3XMNll72ERx55iPe85zquvPIq3vWuvyydf8MN7+DRRx/hJz/5BW63+7iv+cgjD/GFL3yOe+75JgB79uzm3HMv4I477iyds2fP7tJrPPLIQ3zpS59nZmaGmZkMfn+Av//7L3LzzX/LwEA/AM88s5+NGzdhs9lpamriM5+564Tv6cEHf829936Z0dFRnE4nra1tXHvtDWzcuIkrrtjLzEyG73//hzgcDgD+/d9/wK23foS//usbef3r38APf/iv3HnnHTQ3t5LNztDZuY6bbrqFurp6brjhHVx55VVceOFFz/t1nZqa4l3vejt33fX/U1tbO7diAPF4nA9+8P18/vP7nve8TCbD9ddfw+c+9yW8Xu+cX19ERESk3CzLYmh8qrRsb3YGajQxXTqnqa6ajpCPc7aGSsv3/PU1Wo1TpFC1An3sY59kw4ZN7N//NNdddw27d58LQEdHJ/ff/3Ouu+4GHA4H/f19TE9PneTVjq+7+yC///3DnHXW2Uc9ns1mufnmv+Xzn9/Hpk2bAdi//2lsNhu33vrp0nl79uzmS1/66gmD3Kzf/vY33HbbR7n11k+zdeu20usNDw+xceMmAPz+AL/97a85//w9APzoR/+GYZx21Ovs3v0CPvax28nn83zwg+/j61+/h3e/+71zfr//63/9I5dccum8AhVAMBg8aaACcLlcvOQlL+M73/kW11xz7byuISIiIrJcsrk8/UMpemKFmaeeaJLuWJLJ6SwANhu0+j0YHQ10hArL9zrCPry1VWUe+cqmUPUcv3xsgAf+MLAkr33xWa2ct615zudv2bIVt9vNwEAfALW1btatW18KID/60b/x0pe+gqeeenLeY7n66newb98XuPvurx71eDqdZmpqkqampqPGcaq+9rUv85a3XFMKVMd7vZe9bC8//OG/cf75e0pBccOGjcd9Pbvdzq5d5/DrXz8wr3H84Af/zJ133l36/Ior9nL55S/j4Yd/Rzwe47rr3s3Y2Ag//el/MDExwfvf/yHOOOMs+vv7edvb3sS///vPgEKYfMc73skvfvFzxsfHede73sMll7wIgMsuewnXXHOVQpWIiIisCJPTWXpiyaMaSPQPpcjmCuv3XFV2IkEv520LEwkXlu+1BTy4qhxlHnnlUahawR555CEymQzt7R0cOGAC8PKX7+V//+/vc955F/Kzn/2EL33pHj772U/N+7UvvvhSvv/973L//T/noosuKT1eV1fH3r2v4Y1vfB1nnrmLnTvP4PLLX0o4PPcweKT9+5/mve/92+c9Z9eu3fzzP/8TExMTpaD49NNPHffcTCbDAw/8gq1bTzvu8eOJRgeZmpqiufnoduYzMzPs2/c1nnrqCd797mu5/vr38OUvf4Of/eyn3H33XXzpS/cc9/U8Hg9f+co3+MMfHuWDH/y7UqhqavLjdFZx6NBBOjvXzXl8IiIiIgthWRbjqcwxy/dio5Olc3zuKjrCPl68u6nUQCLc6K74VuYrhULVc1y4s4ULdy7NXkJz3fPolltuwuWqxuPx8PGPfxKfz1c6tmvXbu644zZ+8Yufs2HDRurrG055PNdeewOf+9ynufDCFx71+HvfexNveMObeOSRh/jNb37Jffd9ja985ZtEIh2nfK3nY7PBpZe+mJ/97CeloPjcUPXQQ7/lrW/9cwB27jyDq65625xfPx6P0djYdMzjL3rRi4HCzNnU1BQvetHlAGzdehp9fb0nfL0XveglAGzfvpOhoTjT09NUVxdagvr9fmKxqEKViIiILIm8ZREdSR+9fC+aYCJ9uH15qKGWSNjLhTtb6AgVlu81eF26/2kJKVStQLP3VB2PzWbj0ktfzO23f4z3v//DC7rO7t0voKkpwI9//MNjjrW1tdPW1s7eva/hb/7mPfzyl7/gjW9887yvsWXLVp588gk2bzae97yXveyVXHvtWznzzF3HDYqz91SdiurqajKZ6WMed7lcAKUGGbOf2+12crnsCV/vuc/L5XKlY5nM4YAlIiIishAz2Ry98dRRy/d6YymmZwq/ezjsNtoCHk7fGCgt32sPenHX6Ff85aaveAV69atfR21tLeeee/6CX+v662/gAx/4u9Ln6XSaxx//A+eccy42m41EIsHAQB8tLW2n9Ppvecs13H77x9m6dRuGUbiX6sknH2d8fJzzz7+wdF5bWztvf/s72bZtx8Le0HF0dHQyPDxMJpMpBaKlkMvl6O/vO2EgFhERETmR5OQMPcXwFB2f4kD3KANDafLF/uW11Q4iIR8Xnd5SWr7XGvCsyfblK5FCVQUKBkO86U1vWZTXKoQdo9QMAyy+//3v8tnP3o7LVU0ul+Pyy1/GxRf/ySm9/nnnXcCNN/4dn/nMJxkfHy+2VG/luutuOObcV7/6dad0jU984sO4XIdnhz71qc+VOhcCVFfXsGvX2fz+9w8vShA9kcce+79s27ZDLdVFRETkhCzLYnhiqrTv02wjieGJw6tq/PU1tAU8nLU5WFi+1+wjUF+DXcv3ViybNbt71+q3DugaHk6Szx9+z4ODh2hu7lyWAcz1nipZfI899n/59re/wa233jGv582nZh/+8M284hWv4pxzzj3u8eX8XlvLgkEf8Xii3MOQeVDNKo9qVllUr/LJ5fMMDKePaiDRE0uSmiq2Lwea/e7SxrkdYR+RkJeN6/yqWZnY7Tb8fi/AeuDgXJ+nmSpZE3buPIMLLriIycnJee9VNReZTIYzzzzrhIFKREREVrepTJbeWIruWKIUonrjKbK5wh9nq5x22oMedm8NlZpHtAe9VLvUvnw1mFeoMgzjQ8CHgZ2maT5uGMZ5wD6glkKSe7NpmrHiuct6TOCaa646qmkCwPbtO7jxxvcv6XUPHDD5+Mf/v2Mef/3r/4y9e1+zpNeej6Uci8vl4jWvuWLJXl9ERERWjvFUhp5oorR871A0SWwkzexaKE+Nk46wjxed3Va4/ynkpdnvxmHX/U+r1ZxDlWEYu4DzgO7i5zbgPuCtpmk+YBjGLcBtwNXLfWwxvhCrwT33fLMs19282eDee79dlmuLiIiILJW8ZREfmzy891M0SXcswXgyUzonUF9DJOTl/CM20G30Vat9+Rozp1BlGEY18AXgz4H/Kj68G5gyTfOB4ud3U5g9uroMxxbEsix948uSWkP3LoqIiFSkmWye/qHUUeGpJ5ZkKlNYBWS32WgNuNm+rqm0fC8S9uKpqSrzyGUlmOtM1UeA+0zT7DKM0n5DHcCh2U9M0xwyDMNuGEbTch8zTXNknu+7xOl0kUpN4PHUKVjJkrAsi1RqAqdz6dq5i4iIyNylp2ZKy/YKy/iSDAynyBWbmVW7HERCXi7Y0VxqX94W8FDl1P1PcnwnDVWGYZwPnAO8b+mHs/SK3TxKGhpq6OnpIR7vLdOIZC2ora1h06b1VFXpr1nLIRj0lXsIMk+qWeVRzSrLWq2XZVkMj0/xbN84z/aPF/7tGyc6ki6d0+irZn1bPeef3sL61no2ttXT7Pdgt5f3j+1rtWaVai4zVRcDW4HZWap24MfAnUCpP7RhGAHAMk1zxDCM7uU8Np83/NyW6gA+XxDfMnzfqqVp5VnMmo2NTQFTi/JacmL6Oas8qlnlUc0qy1qpVz5vMTiSPmr5Xnc0SXJypnROuLGWjrCPPTubSw0k6r3Vz3kli+Hh5PIO/jnWSs1WoiNaqs/LSUOVaZq3UWgIAYBhGAeBVwJPAu8wDGNP8T6n64DvFk97GKhdxmMiIiIiskZMz+TojSfpmW0gEUvSG0uSKe4t6XTYaAt6OWtzoLR8rz3opbZauwnJ0jjl7yzTNPOGYVwF7DMMo4Zii/NyHBMRERGR1SmRztAdK26cG01yKJpgcCTNbA8od7WTjrCXS85qK22i2+x343SofbksH9sa6kq2Dug63vK/5aKp3MqjmlUe1azyqGaVRzWrLJVSL8uyGBqfOrx8rzgDNZqYLp3TVFdNR6gw8zS7fM9fX7Pqmo1VSs1WoyOW/62nMIkzJ5oDFREREZFllc0V2pcXOvAVZqC6Y0kmp7MA2GzQ6vdgdDQcFaK8tWr4JCuTQpWIiIiILJnJ6Sw9scMzT93RBP1DKbK5wsohV5WdSNDLeUdsntsW8OCqUvtyqRwKVSIiIiKyYJZlMZ7KHLN8LzY6WTrH566iI+zjxbubSg0kwo3usrcvF1kohSoRERERmZe8ZREdSR+9fC+aYCJ9uH15qKGWSNjLhTtb6AgVlu81eF2r7v4nEVCoEhEREZHnMZPN0RtPHbV8rzeWYnomB4DDbqMt4OH0jYHS8r32oBd3jX7NlLVD3+0iIiIiAkBycoaeI8JTdyzJwFCafLFbdG21g0jIx0Wnt5SW77UGPGpfLmueQpWIiIjIGmNZFsMTU6V9n2YbSQxPHG5f3uirJhLyctbmYGH5XrOPQH0Ndi3fEzmGQpWIiIjIKpbL5emNJ49qINETS5KaKrYvB5r9bja21XPpLh8dYR+RkJc6j6u8AxepIApVIiIiIqvEVCZLbyxFdyxRClF9QylmsnkAqpx22oMedm8NlZpHtAe9VLvUvlxkIRSqRERERCrQeCpDTzRRWr53KJokNpLGKh731DjpCPt45Z4NBHwuOkJemv1uHHbd/ySy2BSqRERERFawvGURH5s8vPdTNEl3LMF4MlM6J1BfQ0fYx/nbwqUGEo2+amw2G8Ggj3g8UcZ3ILL6KVSJiIiIrBAz2Tz9Q6mjwlNPLMlUptC+3G6z0Rpws31dU2n5XiTsxVNTVeaRi6xtClUiIiIiZZCemikt2yss40v+v/buPDbS8z7s+Jf3NbPcXXJmuCRnrLWOx5HsldaSEMlH7QpWrkZwE6dJ3NjKAQOxkQNtkjZAkKQH4NawXaRJ7FRu0qSK3RhwEcAJCjQpitR1VCdAmlpJ3DRPfEi73EMcHrtcDof3vP1jXs5ydezyHg75/QALct/nnZmX+9uXwx+f3/N7uDqzwHqtXsDX091BMZ/hLW8cacw+jQ0P0NXp+ifpsDGpkiRJ2kdJknBtfrkx87RRxjc9t9Q4Z3Cgm2Ihw4P3DFHM1zfQzZ3qs3251CJMqiRJkvZIrZZwdbZa30B3UxJVWVxtnFM41cfZMyd4x0Oj9RmofIbBTE8Tr1rSbplUSZIk7cDy6nq6/9PN8r3LUxVW0vblnR1tjOUynL93uFG+N57L0Nfjj1/SUeNdLUmSdAfz1ZVXlO+9NFslSfuX9/d0UipkeOf5sUb53shQP50dti+XjgOTKkmSpFSSJEzNLTVmniYm57lYrnBtfrlxzukTPZTyWR59Q75Rvjc02Eub65+kY8ukSpIkHUtr6/X25fUOfPUZqIlyhd8pxQoAAB62SURBVMXlNQDa2mB0aIBQOkkpXy/fKxWyZPpsXy7pViZVkiTpyFtcXmOinG6em368Mr3A2nq9fq+7q51iLsNj9xcoFurle2PDA3R32b5c0p2ZVEmSpCMjSRLmFlZubp6bJlHla4uNc7L9XZQKWZ585HSjgUThVD/t7ZbvSdqZLSVVIYTPA2eBGlABfiLG+HwI4T7gWWAImAGejjF+NX3MgY5JkqTjpVZLmLxWbZTvTaRJ1I3qzfbl+ZN9FAsZ3vqmM5Ty9fK9k5lu1z9J2lNbnan6wRjjHEAI4d3AbwJvBp4BPhlj/EwI4X3Ap4An0scc9JgkSTqiVtfWuTS1cHMGqjzPpfICy6vrAHS0tzE2PMC5u4cb5XvjuQz9vRblSNp/W/pOs5FQpQaBWgghTz2xejI9/lngEyGEHNB2kGMxxqmtf8mSJOkwqyyuNrrubZTvXZ2uUkv7l/f1dFDMZ3n7uTON8r3R4QHbl0tqmi3/+iaE8BvAt1BPbr4NKAKXY4zrADHG9RDClfR42wGPbTmpGhrKbPXUfZHLZZv6+to+Y9Z6jFnrMWatZy9iliQJU9cW+caVOb5xOf1zZY6pTeufhgZ7OTs6yNseHOPs2CB3jw2Sd/3TtnmPtR5j1lq2nFTFGD8AEEJ4P/Ax4Bf266L208xMhVotacpr53JZpqbmm/La2hlj1nqMWesxZq1nJzFbr9W4OlO9pYHERLnCwlLavhwYGern7EiWdz44SqmQpZjPcGKg+9YnqtWYmans0VdyPHiPtR5j1jzt7W07moTZdqFxjPHTIYR/D1wCxkIIHemsUQcwCkxQ/954kGOSJOmQWFpZ41J5gYvl+UYSdWlqgbX1GgBdne2M5zI8smnz3PFchp5u25dLak13TKpCCBngVIxxIv37U8AsUAaeB94LfCb9+OWN9U0hhAMdkyRJB29uYYWJyfl6971yhQuTFcqzVTZqQgZ6OykVsrzr4XGK6ea5I6f76Gh3/ZOko2MrM1UDwH8OIQwA69QTqqdijEkI4YPAsyGEXwSuAU9vetxBj0mSpH1SSxKmri9ycbLC9J9N8DcvzHKxPM9cZaVxzvBgL6VClsfvLzQaSJzK9ti+XNKR15YkzVlf1AR3AS+4pkrbYcxajzFrPcbs8Fldq3Fl+tb25RPlCksr9fbl7e1tjA71N0r3SoUsxUKGgd6uJl+5Xo33WOsxZs2zaU3VWeDFrT7OzRskSTrGqkurjbK9ehlfhaszC6ynv4Ds6e6gmM/wljeONGafHnzDCHPXq02+ckk6PEyqJEk6BpIk4dr8cmPmaaMD3/TcUuOcwYFuioUMD94zRDFf30A3d6qP9peV73V32VBCkjYzqZIk6Yip1RKuzlbrG+huSqIqi6uNcwqn+jh75gTveGi0UcY3mOlp4lVLUusyqZIkqYUtr65zaarCxU3le5enKqys1duXd3a0MZbLcP7e4Ub53nguQ1+PPwJI0l7xO6okSS1ivrryivK9l2arbPSc6u/ppFTI8M7zY43yvZGhfjo7bF8uSfvJpEqSpEMmSRKm5pYaM08Tk/NcLFe4Nr/cOOf0iR5K+SyPbtpAd2iw1/blktQEJlWSJDXR2vpG+/KbM1AT5QqLy2sAtLXB6NAAoXSSUr5evlcqZMn02b5ckg4LkypJkg7I4vIaE+XKLfs/XZleYG29Xr/X3dVOMZfhsfsLFAv18r2x4QG77UnSIWdSJUnSHkuShOuVFSbKm8r3JiuUry82zsn2d1EqZHnykdONBhKFU/20t1u+J0mtxqRKkqRdqNUSJq9Vby3fm5znRvVm+/L8yT6KhQxvPXeGUr5evncy0+36J0k6IkyqJEnaotW1dS5NLdxSvnepvMDy6joAHe1tjA0PcO7u4Ub53nguQ3+vb7eSdJT5XV6SpFdRWVxtdN27mH68Ol2llvYv7+vpoJjP8vZzZxrle6PDA7Yvl6RjyKRKknSsJUnCzI0lJiYrXJicbzSSmLlxs335qWwPxXyG8/fm6uV7I1mGB3tpt3xPkoRJlSTpGFlbr/HSbPVm+V6aRC0spe3LgZGhfu4ZP8kT6dqnYj7DiYHu5l64JOlQM6mSJB1JSytrXCovpM0j6l34Lk8tsLZeA6Crs53xXIZHNm2eO57L0NNt+3JJ0vaYVEmSWt7cwgoTk/ON8r0LkxXKs1WSdHygt5NSIcu7Hh6nmG6eO3K6j4521z9JknbPpEqS1DJqScLU9cVG6d5GB765ykrjnOHBXkqFLI/fX2g0kDiV7bF9uSRp35hUSZIOpdW1Glemb21fPlGusLRSb1/e3tbG6HA/D9x1urH3U7GQYaC3q8lXLkk6bkyqJElNV11abZTtTaTrn67OLLBeqxfw9XR3UMxneMsbRxqzT2PDA3R1uv5JktR8JlWSpAOTJAnX5pcbM08XJytcnl5gcrbaOGdwoJtiIcOD9wxRzNc30M2d6rN9uSTp0LpjUhVCGAI+DdwNLANfA340xjgVQngM+BTQB7wIvC/GWE4fd6BjkqTDpVZLuDpbrW+guymJqiyuNs4pnOrj3tIp3vamkUYHvsFMTxOvWpKk7dvKTFUCfDTG+AWAEMLHgI+EED4AfAb4oRjjcyGEnwc+AvxICKHtIMf26h9DkrQzy6vrXJqqcHFT+d7lqQora/X25Z0dbYzlMpy/d7hRvjeey9DX00kul2Vqar7JX4EkSTt3x6QqxjgLfGHToT8FPgQ8AizFGJ9Ljz9DffboR5owJkk6IPPVlVtmni5OzvPSbJUk7V/e39NJqZDhnefHGuV7I0P9dHbYvlySdDRta01VCKGdekL1+0AJuLAxFmOcDiG0hxBOH/RYmvhJkvZQkiRMzS01Zp4mJue5WK5wbX65cc7pEz2U8lke3bSB7tBgr+3LJUnHynYbVfwqUAE+AXzX3l/O/hsayjT19XO5bFNfX9tnzFqPMdu+1bUal8rzfP3SHC9cmePrl+sfq0trALS3wXghy4P35nj92CCvHx3k7NggJwa69+T1jVnrMWatxXi1HmPWWracVIUQPg7cCzwVY6yFEC4Cr9s0PgwkMcbZgx7bzhc8M1OhlrboPWiuG2g9xqz1GLM7W1xeY6J86+a5V6YXWFuvf2/s7mqnmMvwzd9UoFiol++NDQ/Q3XVr+/Ll6jJT1eVXe4ltMWatx5i1FuPVeoxZ87S3t+1oEmZLSVUI4cPAw8DfizFuvIP+OdAXQnhbus7pg8DnmjQmSXqZJEm4XllhorypfG+yQvn6YuOcbH8XpUKWJx853WggUTjVT3u75XuSJG3VVlqqPwD8HPC3wJdCCAAvxBi/K4TwfuBTIYRe0hbnAOlM1oGNSdJxV6slTF6r3tJAYmJynhvVm+3L8yf7KBYyvPXcGUr5DKVClpOZbtc/SZK0S21J0pxSuCa4C3jB8j9thzFrPcchZqtr61yaWrilfO9SeYHl1XUAOtrbGBseoFTINsr3xnMZ+nsP537vxyFmR40xay3Gq/UYs+bZVP53lvokzpYczndYSRIAlcXVm9330hmoqzNVaukvxPp6Oijms7z93JlG+d7o8IDtyyVJOkAmVZJ0CCRJwsyNpca+TxfTJGrmxs1GEKeyPRTzGc7fl6uX741kGR7spd3yPUmSmsqkSpIO2Np6jZdmqrdsnjtRrrCQti9vA0aG+rln/CRPpGufivnMnrUvlyRJe8ukSpL20dLKGpfKC2kCVS/juzy1wNp6DYCuznbGcxke2bR57nguQ093xx2eWZIkHRYmVZK0R+YWVtL1T/WZpwuTFcqzVTZa4wz0dlIqZHnXw+MUC/UZqJHTfXS0u/5JkqRWZlIlSdtUSxKmri/esv7pYnmeucpK45zhwV5KhSyP319oNJA4le2xfbkkSUeQSZUk3cbqWo0r07e2L58oV1haqbcvb29rY3S4nwfuOt3Y+6lYyDDQ29XkK5ckSQfFpEqSUtWl1UbZ3kYb86szC6yne9v1dHdQzGd4yxtHGrNPY8MDdHW6/kmSpOPMpErSsZMkCdfmlxszTxtlfNNzS41zBge6KRYyPHjPEMV8fQPd3Kk+25dLkqRXMKmSdKTVaglXZ6tMbCrfuzhZobK4CtTbl+dP93P2zAne8dBoowPfYKanuRcuSZJahkmVpCNjeXWdeGGWv4jlRvne5akKK2v19uWdHW2M5TKcv3eYUiHL6wpZxnID9PX4rVCSJO2cP0lIaknz1ZVXlO+9NFslSfuX9/d0UipkeOf5sUb53shQP50dti+XJEl7y6RK0qGWJAlTc0uNmaeJyXkulitcm19unHP6RA+lfJZH35DnjffmOdnbwdBgr+3LJUnSgTCpknRorK1vtC+/OQM1UZ5ncbnevrytDUaHBgilk5Ty9e57pUKWTN/N9uW5XJapqflmfQmSJOkYMqmS1BSLy2tMlG/dPPfK9AJr6/X6ve6udoq5DI/dP0KxUC/fGxseoLvL9uWSJOlwMamStK+SJOF6ZYWJ8qbyvckK5euLjXOy/V2UClmefOR0Y/+nwql+2tst35MkSYefSZWkPVOrJUxeq95avjc5z43qauOc/Mk+ioUMbz13hlK+Xr53MtPt+idJktSyTKok7cjq2jqXphZuKd+7VF5gebW+/qmjvY2x4QHO3T3cKN8bz2Xo7/XbjiRJOlr86UbSHVUWV29230tnoK7OVKml/cv7ejoo5rO8/dyZRvne6PCA7cslSdKxYFIlqSFJEmZuLDX2fdrovjdz42b78lPZHor5DOfvy9XL90ayDA/20m75niRJOqbumFSFED4OvAe4C3hTjPEr6fH7gGeBIWAGeDrG+NVmjEnavrX1Gi/NVG/ZPHeiXGFhaQ2ANmBkqJ97xk/yRLr2qZjPcGKgu7kXLkmSdMhsZabq88AvA3/8suPPAJ+MMX4mhPA+4FPAE00ak3QbSytrXCovcGFyvtGF7/LUAmvrNQC6OtsZz2V45A35evlePsN4LkNPt+3LJUmS7uSOSVWM8TmAEELjWAghD7wZeDI99FngEyGEHPVfcB/YWIxxaptfs3SkzS2spKV7Gw0kKpRnqyTp+EBvJ6VClnc9PE4x3Tx35HQfHe2uf5IkSdqJna6pKgKXY4zrADHG9RDClfR42wGPmVTpWKolCVPXFrm4eQPdyXnmFlYa5wwP9lIqZHn8/kKjgcSpbI/tyyVJkvbQsWtUMTSUaerr53LZpr6+tu8wxGx1bZ0LL83zjctzvHB5jq9fnuPFq3MsLtfbl7e3t1EqZHn4mwqcHR3k7rFBzo6eINN/PNc/HYaYaXuMWesxZq3FeLUeY9ZadppUTQBjIYSOdNaoAxhNj7cd8Ni2zMxUqNWSO5+4D3K5LFNT8015be1MM2JWXVplolypty9P25hfnVlgPf1/29PdQTGf4fEHRhqzT2PDA3R13rr+aXFhmcWF5Vd7iSPN+6z1GLPWY8xai/FqPcasedrb23Y0CbOjpCrGWA4hPA+8F/hM+vHLG+ubDnpMakVJknBtfrmxce5G+d703FLjnMGBbkqFLA/eM0QxX99AN3eqz/blkiRJh8hWWqr/CvDdwAjw30MIMzHGB4APAs+GEH4RuAY8velhBz0mHWq1WsLV2SoTjeYR9Y+VxVWgPhWbP93P2TMneMdDo40OfIOZnuZeuCRJku6oLUmaUwrXBHcBL1j+p+3YScyWV9e5NFWpb5w7udG+vMLKWr19eWdHG2O5TH3j3EKW1xWyjOUG6Os5dksc94X3WesxZq3HmLUW49V6jFnzbCr/Owu8uNXH+VOctAvz1ZVXlO+9NFtl43cV/T2dlAoZ3nl+rFG+NzLUT2eH7cslSZKOCpMqaQuSJGFqbqkx8zQxOc/FcoVr8zcbQZw+0UMpn+XRTRvoDg322r5ckiTpiDOpkl5mbb3GlekFLk5WmJ5/kb95cZaJ8nyjfXlbG4wODRBKJynl6933SoUsmb6uJl+5JEmSmsGkSsfa4vIaE5s3zy3Pc2V6gbX1m+3Lx4cHeOz+EYqFevne2PAA3V0dd3hmSZIkHRcmVToWkiThemWFifKm8r3JCuXri41zsv1dlApZnnzkdGP/pwfuKzA7U2nilUuSJOmwM6nSkVOrJUxeq97SQGJicp4b1dXGOfmTfRQLGd567kyjC9/JTPcr1j91tLseSpIkSbdnUqWWtrK6zuXphVvK9y6VF1hera9/6mhvY2x4gHN3DzfK98ZzGfp7/a8vSZKkveFPlmoZlcXVm9330hmoqzNVamn/8r6eDor5LG8/d6ZRvjc6PGD7ckmSJO0rkyodOkmSMHNjqbHv08U0iZq5cbN9+alsD8V8hvP35erleyNZhgd7abd9uSRJkg6YSZWaam29xksz1Vs2z50oV1hYWgOgDRgZ6uee8ZM8ka59KuYznBjobu6FS5IkSSmTKh2YpZU1LpUXuDA53+jCd3lqgbX1GgBdne2M5zI8smnz3PFchp5u25dLkiTp8DKp0r6YW1hJS/c2GkhUKM9WSdLxgd5OSoUs73p4nGK6ee7I6T462l3/JEmSpNZiUqVdqSUJU9cWubh5A93JeeYWVhrnDA/2Uipkefz+QqOBxKlszyval0uSJEmtyKRKW7a6VuPKdFq+N1nhQrm+/ml55Wb78jNDAzxw9nSjfK9YyDDQ29XkK5ckSZL2j0mVXlV1abVRtrcxA3V1ZoH1Wr2Ar6e7g2I+w9veeKax/9PocD9dna5/kiRJ0vFiUnXMJUnCtfnlm+3L0yRqem6pcc7gQDelQpYH7xlqzEDlTvXZvlySJEnCpOpYWa/VeGl2sd62fLKSduGrUFlcBerty/On+zl75gTveGi0kUANZnqae+GSJEnSIWZSdUQtr65zqXxr+d7lqQora/X25Z0dbYzlMpy/d5hSIcvrClnGcgP09fhfQpIkSdoOf4I+AuarK+n6p5vd916arZKk/cv7ezopFTK88/wYxXx9/dPIUD+dHbYvlyRJknbLpKqFJEnC1NwSE5P1jXMn0jVQ1+aXG+ecPtFDKZ/l0U0b6A4N9tq+XJIkSdonLZdUhRDuA54FhoAZ4OkY41ebe1V7b2293r588wzURHmexeV6+/K2NhgdGiCUTlLK1/d+KhWyZPpsXy5JkiQdpJZLqoBngE/GGD8TQngf8CngiSZf064sLq8xUa409n+6ODnP5emb7cu7u9op5jI8dv9Io3352PAA3V22L5ckSZKaraWSqhBCHngz8GR66LPAJ0IIuRjjVPOubHtqtYS//MYMf/3CLH994RpXphcaY9n+LkqFLN+ysYFuIUPhVD/t7ZbvSZIkSYdRSyVVQBG4HGNcB4gxrocQrqTHWyap+uJfXuG3/yDS1dnOfcWTfPM3peufCllOZrpd/yRJkiS1kFZLqnZtaCjT1NfP5bJ859+5h3P35Tk7OmgJXwvI5bLNvgRtkzFrPcas9Riz1mK8Wo8xay2tllRNAGMhhI50lqoDGE2Pb8nMTIVaulbpoOVyWaam5gE43d/F3PVqU65DW7c5ZmoNxqz1GLPWY8xai/FqPcasedrb23Y0CdNSGxXFGMvA88B700PvBb7cSuupJEmSJB0trTZTBfBB4NkQwi8C14Cnm3w9kiRJko6xlkuqYox/A3xzs69DkiRJkqAFk6pd6ACa3pq82a+v7TNmrceYtR5j1nqMWWsxXq3HmDXHpn/3bXWTa0uS5jRtaIK3AX/c7IuQJEmSdOi9HXhuqycfp6SqB3gUuAqsN/laJEmSJB0+HcAZ4M+A5a0+6DglVZIkSZK051qqpbokSZIkHTYmVZIkSZK0CyZVkiRJkrQLJlWSJEmStAsmVZIkSZK0CyZVkiRJkrQLJlWSJEmStAudzb6A4yKEcB/wLDAEzABPxxi/2tyrOn5CCC8CS+kfgJ+NMf5hCOEx4FNAH/Ai8L4YYzl9zI7GtDMhhI8D7wHuAt4UY/xKevw176H9GNPW3SZmL/Iq91s65j3XJCGEIeDTwN3UN7b8GvCjMcap/YiLMdu9O8QsAf4KqKWnvz/G+Ffp454CPkb9570/B344xljdzZi2LoTweeAs9dhUgJ+IMT7v+9nR5EzVwXkG+GSM8T7gk9TfYNQc3xNjfCj984chhDbgM8CPpfH5IvARgJ2OaVc+D/wd4MLLjt/uHtqPMW3da8UMXna/wc7vK++5PZMAH40xhhjjOeDrwEf2Iy7GbM+8asw2jb9l0322kVBlgF8Hnoox3gPMAz+zmzFt2w/GGB+MMZ4HPg78Znrc97MjyKTqAIQQ8sCbgc+mhz4LvDmEkGveVWmTR4ClGONz6d+fAb53l2PaoRjjczHGic3HbncP7cfYfn1tR9WrxewOvOeaKMY4G2P8wqZDfwq8jv2JizHbA7eJ2e18O/C/N81WPAN83y7HtA0xxrlNfx0Ear6fHV0mVQejCFyOMa4DpB+vpMd18P5TCOEvQwi/FkI4CZTY9Bv2GOM00B5COL2LMe2t291D+zGmvfPy+w285w6NEEI78CHg99mfuBizPfaymG34Qgjh+RDCvw4h9KTHbvm3By5y8/vbTse0TSGE3wghXAQ+DPwgvp8dWSZVOm7eHmN8EHgUaAM+0eTrkY4y77fD71epr/UwNq3j5TErxRgfoV6Cez/wC826ML1SjPEDMcYS8HPU16npiDKpOhgTwFgIoQMg/TiaHtcB2ihRijEuA78GvJX6b+EaZRQhhGEgiTHO7mJMe+t299B+jGkPvMb9Bt5zh0LaYORe4PtijDX2Jy7GbA+9Ssw232c3gN/gNe4z6jNQE7sc0w7FGD8N/F3gEr6fHUkmVQcg7XL0PPDe9NB7gS/HGKead1XHTwhhIIQwmH7eBnw/9bj8OdAXQnhbeuoHgc+ln+90THvodvfQfozt/1d09N3mfgPvuaYLIXwYeBj4+2nSC/sTF2O2R14tZiGEUyGEvvTzTuB7uHmf/QHwaAjh3vTvm//tdzqmLQohZEIIxU1/fwqYBXw/O6LakiRp9jUcCyGEN1BvdXkKuEa91WVs7lUdLyGE1wO/C3Skf/4a+MkY49UQwluod8rp5WbL38n0cTsa086EEH4F+G5gBJgGZmKMD9zuHtqPMW3dq8UMeIrXuN/Sx3jPNUkI4QHgK8DfAovp4RdijN+1H3ExZrv3WjEDPkr93zYBuoAvAf8oxlhJH/fu9JwO4MvAD8UYF3Yzpq0JIRSA3wMGgHXqCdXPxBj/j+9nR5NJlSRJkiTtguV/kiRJkrQLJlWSJEmStAsmVZIkSZK0CyZVkiRJkrQLJlWSJEmStAsmVZKklhNCeCaE8Au3GU9CCPfs8Wv+QAjhv+3lc0qSjgZbqkuSmiqE8P3APwbeCCxQ33/nWeDfxRh39CYVQkiAe2OMX3uVsS8AjwFrwBLwReDHNvbQ2gshhB8CPhBjfNudzpUktT5nqiRJTRNC+Gngl4GPUd88uAB8EHgr0P0aj+nYg5f+8RhjBrgPOAn80h48pyTpmOps9gVIko6nEMIg8C+Bp2OMv7tp6MvAD2w67z8Ci8DrgHcA7w4hvA+4FGP8+fScfwL8FJAAP7/Va4gxzoYQfhf40KZr+lXg24Eq8OvAv4ox1l4++5TOhn0I+GlgGPgd4MeBNwDPAF0hhAqwFmM8GUL4DuDjQBG4AfxSjPHjW71WSdLh5UyVJKlZHgd6gN/bwrn/EPgwkAWe2zwQQvg24GeAJ4F7gXdt9QJCCMPAe6gnclBPqAaB11NP4J4Gfvg2T/GdwKPAg8D3At8aY/x/1Gfb/iTGmIkxnkzP/Q/Aj8YYs9RLHf9oq9cpSTrcnKmSJDXLMDAdY1zbOBBC+BJwP/Vk61tjjF9Mh34vxvi/0s+XQgibn+d7gd+KMX4lfY5/Drz3Dq/9KyGEj1Nfw/UF4KfSssLvA87HGOeB+RDCvwHeTz0hejUfiTFeB66HEP4H8BDwB69x7ipwfwjhL2KM14Brd7hGSVKLcKZKktQsM8BwCKHxC74Y41vSmZ0Zbn2PmrjN84y+bPzCFl77J2OMJ2OMYzHGH4gxTlFP8rpf9vgLwNhtnuelTZ9Xgcxtzn0P8B3AhRDC/wwhPL6F65QktQCTKklSs/wJsAy8ewvn3q4L4FXq65Q2lHZ4PdPUZ5Ne97LnuryD53rF9cYY/yzG+G4gD3we+NxOLlKSdPhY/idJaooY4/UQwr8Afi2E0Ea9bK4KnAMGtvFUnwN+K4Tw28CLwD/b4fWshxA+B3w4hPA0cJp684udNJOYBMZDCN0xxpUQQjfwD4D/EmOcCyHcANZ3cp2SpMPHmSpJUtPEGD9KPXH5p0CZejLyKeBngS9t8Tn+K/BvqTd++Bq7awDxE9TXWX2DekOM3wF+cwfP80fA/wVeCiFMp8feD7yYJlQfBN63i+uUJB0ibv4rSZIkSbvgTJUkSZIk7YJJlSRJkiTtgkmVJEmSJO2CSZUkSZIk7YJJlSRJkiTtgkmVJEmSJO2CSZUkSZIk7YJJlSRJkiTtgkmVJEmSJO3C/wen+hhFIJHd4gAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 1008x432 with 2 Axes>"
]
},
- "metadata": {},
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, (ax1, ax2) = plt.subplots(nrows=2, sharex=True)\n",
+ "df.set_index(\"Grid Points\")[\"PM_RUN_CYC (min)\"].plot(ax=ax1, legend=True);\n",
+ "df.set_index(\"Grid Points\")[\"PM_INST_CMPL (min)\"].plot(ax=ax2, legend=True);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Although some slight variations can be seen for run cycles for many grid points, the correlation looks quite linear (as one would naively expect). Let's test that by fitting a linear function!\n",
+ "\n",
+ "*The details of the fitting have been extracted into dedicated function, `print_and_return_fit()`, of the `common.py` helper file. If you're interested, [go have a look at it](common.py).* "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def linear_function(x, a, b):\n",
+ " return a*x+b"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Counter PM_RUN_CYC (min) is proportional to the grid points (nx*ny) by a factor of 8.1021 (± 0.0057)\n",
+ "Counter PM_INST_CMPL (min) is proportional to the grid points (nx*ny) by a factor of 14.0630 (± 0.0003)\n"
+ ]
+ }
+ ],
+ "source": [
+ "fit_parameters, fit_covariance = common.print_and_return_fit(\n",
+ " [\"PM_RUN_CYC (min)\", \"PM_INST_CMPL (min)\"], \n",
+ " df.set_index(\"Grid Points\"), \n",
+ " linear_function,\n",
+ " format_uncertainty=\".4f\"\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's overlay our fits to the graphs from before."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1008x432 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
"output_type": "display_data"
}
],
"source": [
- "# Plot Cycles and Instructions - both per grid cell\n",
"fig, (ax1, ax2) = plt.subplots(nrows=2, sharex=True)\n",
- "df.set_index(\"nx\")[\"Cycles / Loop Iteration\"].plot(ax=ax1, legend=True);\n",
- "df.set_index(\"nx\")[\"Instructions / Loop Iteration\"].plot(ax=ax2, legend=True);"
+ "for ax, pmu_counter in zip([ax1, ax2], [\"PM_RUN_CYC (min)\", \"PM_INST_CMPL (min)\"]):\n",
+ " df.set_index(\"Grid Points\")[pmu_counter].plot(ax=ax, legend=True);\n",
+ " ax.plot(\n",
+ " df[\"Grid Points\"], \n",
+ " linear_function(df[\"Grid Points\"], *fit_parameters[pmu_counter]), \n",
+ " linestyle=\"--\", \n",
+ " label=\"Fit: {:.2f} * x + {:.2f}\".format(*fit_parameters[pmu_counter])\n",
+ " )\n",
+ " ax.legend();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "What is your result? What value do the graphs come asymptotically close too?\n",
+ "Please execute the next cell to summarize the first task."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The algorithm under investigation runs about 8 cycles and executes about 14 instructions per grid point\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(\"The algorithm under investigation runs about {:.0f} cycles and executes about {:.0f} instructions per grid point\".format(\n",
+ " *[fit_parameters[pmu_counter][0] for pmu_counter in [\"PM_RUN_CYC (min)\", \"PM_INST_CMPL (min)\"]]\n",
+ "))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Bonus:**\n",
"\n",
+ "The linear fits also calculate a y intersection (»`b`«). How do you interpret this value?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "exercise": "solution"
+ },
+ "source": [
+ "The y axis intersection; that is, `b` of the linear fit, is the inherent overhead of the program execution. Even if our program would not compute any stencil operation at all for any grid point, it would still complete this many (~1800) instructions and run this many (~680) cycles. Interestingly, it is also the unparallelizable overhead of this (toy) example."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
"We are revisiting the graph in a little while.\n",
"\n",
"[Back to top](#toc)"
@@ -915,7 +1047,10 @@
"\n",
"Let's compare your estimate to what the system actually does!\n",
"\n",
- "<a name=\"task2-a\"></a>**TASK A**: Please measure counters for loads and stores. See the TODOs in [`poisson2d.ld_st.c`](/edit/Tasks/poisson2d.ld_st.c). This time, implement `PM_LD_CMPL` and `PM_ST_CMPL`.\n",
+ "### Task A\n",
+ "<a name=\"task2-a\"></a>\n",
+ "\n",
+ "Please measure counters for loads and stores. See the TODOs in [`poisson2d.ld_st.c`](/edit/Tasks/poisson2d.ld_st.c). This time, implement `PM_LD_CMPL` and `PM_ST_CMPL`.\n",
"\n",
"Compile with `make task2`, test your program with a single run with `make run_task2`, and then finally submit a benchmarking run to the batch system with `make bench_task2`. The following cell will take care of all this.\n",
"\n",
@@ -924,561 +1059,530 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "gcc -DUSE_DOUBLE -Ofast -std=c99 -lm -lpapi poisson2d.ld_st.c -o poisson2d.ld_st.bin\n",
- "bsub -W 60 -nnodes 1 -Is jsrun -n 1 -c 1 -g ALL_GPUS ./bench.sh poisson2d.ld_st.bin /gpfs/wolf/gen110/scratch/aherten//poisson2d.ld_st.bin.csv\n",
- "Job <4032> is submitted to default queue <batch>.\n",
+ "bsub -W 60 -nnodes 1 -Is -P TRN003 jsrun -n 1 -c 1 -g ALL_GPUS ./bench.sh poisson2d.ld_st.bin /gpfs/wolf/trn003/scratch/aherten//poisson2d.ld_st.bin.csv\n",
+ "Job <24416> is submitted to default queue <batch>.\n",
"<<Waiting for dispatch ...>>\n",
"<<Starting on login1>>\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,4,0.0012,95115,474,789,21343,106,249\n",
+ "200,32,4,0.0012,119819,598,817,32902,164,266\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,8,0.0014,137115,684,999,33343,166,309\n",
+ "200,32,8,0.0013,161819,808,1027,56902,284,386\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,12,0.0014,197115,984,1299,45343,226,369\n",
+ "200,32,12,0.0014,221819,1108,1327,71902,359,461\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,16,0.0015,257115,1284,1599,63343,316,459\n",
+ "200,32,16,0.0015,281819,1408,1627,86902,434,536\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,20,0.0016,317115,1584,1899,75343,376,519\n",
+ "200,32,20,0.0015,341819,1708,1927,101902,509,611\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,24,0.0016,377115,1884,2199,93343,466,609\n",
+ "200,32,24,0.0016,401819,2008,2227,116902,584,686\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,28,0.0017,437115,2184,2499,105343,526,669\n",
+ "200,32,28,0.0016,461819,2308,2527,131902,659,761\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,32,0.0017,497115,2484,2799,123343,616,759\n",
+ "200,32,32,0.0018,521819,2608,2827,146902,734,836\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,36,0.0018,557115,2784,3099,135343,676,819\n",
+ "200,32,36,0.0018,581819,2908,3127,161902,809,911\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,40,0.0020,617115,3084,3399,153343,766,909\n",
+ "200,32,40,0.0018,641819,3208,3427,176902,884,986\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,44,0.0019,677115,3384,3699,165343,826,969\n",
+ "200,32,44,0.0019,701819,3508,3727,191902,959,1061\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,48,0.0020,737115,3684,3999,183343,916,1059\n",
+ "200,32,48,0.0020,761819,3808,4027,206902,1034,1136\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,52,0.0021,797115,3984,4299,195343,976,1119\n",
+ "200,32,52,0.0020,821819,4108,4327,221902,1109,1211\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,56,0.0021,857115,4284,4599,213343,1066,1209\n",
+ "200,32,56,0.0021,881819,4408,4627,236902,1184,1286\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,60,0.0023,917115,4584,4899,225343,1126,1269\n",
+ "200,32,60,0.0022,941819,4708,4927,251902,1259,1361\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,64,0.0023,977115,4884,5199,243343,1216,1359\n",
+ "200,32,64,0.0023,1001819,5008,5227,266902,1334,1436\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,68,0.0024,1037115,5184,5499,255343,1276,1419\n",
+ "200,32,68,0.0023,1061819,5308,5527,281902,1409,1511\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,72,0.0025,1097115,5484,5799,273343,1366,1509\n",
+ "200,32,72,0.0025,1121819,5608,5827,296902,1484,1586\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,76,0.0025,1157115,5784,6099,285343,1426,1569\n",
+ "200,32,76,0.0028,1181819,5908,6127,311902,1559,1661\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,80,0.0025,1217115,6084,6399,303343,1516,1659\n",
+ "200,32,80,0.0025,1241819,6208,6427,326902,1634,1736\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,84,0.0026,1277115,6384,6699,315343,1576,1719\n",
+ "200,32,84,0.0026,1301819,6508,6727,341902,1709,1811\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,88,0.0027,1337115,6684,6999,333343,1666,1809\n",
+ "200,32,88,0.0026,1361819,6808,7027,356902,1784,1886\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,92,0.0027,1397115,6984,7299,345343,1726,1869\n",
+ "200,32,92,0.0027,1421819,7108,7327,371902,1859,1961\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,96,0.0028,1457115,7284,7599,363343,1816,1959\n",
+ "200,32,96,0.0028,1481819,7408,7627,386902,1934,2036\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,100,0.0029,1517115,7584,7899,375343,1876,2019\n",
+ "200,32,100,0.0029,1541819,7708,7927,401902,2009,2111\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,104,0.0029,1577115,7884,8199,393343,1966,2109\n",
+ "200,32,104,0.0029,1601819,8008,8227,416902,2084,2186\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,108,0.0030,1637115,8184,8499,405343,2026,2169\n",
+ "200,32,108,0.0031,1661819,8308,8527,431902,2159,2261\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,112,0.0030,1697115,8484,8799,423343,2116,2259\n",
+ "200,32,112,0.0030,1721819,8608,8827,446902,2234,2336\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,116,0.0031,1757115,8784,9099,435343,2176,2319\n",
+ "200,32,116,0.0031,1781819,8908,9127,461902,2309,2411\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,120,0.0033,1817115,9084,9399,453343,2266,2409\n",
+ "200,32,120,0.0032,1841819,9208,9427,476902,2384,2486\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,124,0.0032,1877115,9384,9699,465343,2326,2469\n",
+ "200,32,124,0.0033,1901819,9508,9727,491902,2459,2561\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,128,0.0033,1937115,9684,9999,483343,2416,2559\n",
+ "200,32,128,0.0033,1961819,9808,10027,506902,2534,2636\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,132,0.0034,1997115,9984,10299,495343,2476,2619\n",
+ "200,32,132,0.0034,2021819,10108,10327,521902,2609,2711\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,136,0.0035,2057115,10284,10599,513343,2566,2709\n",
+ "200,32,136,0.0035,2081819,10408,10627,536902,2684,2786\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,140,0.0035,2117115,10584,10899,525343,2626,2769\n",
+ "200,32,140,0.0036,2141819,10708,10927,551902,2759,2861\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,144,0.0036,2177115,10884,11199,543343,2716,2859\n",
+ "200,32,144,0.0036,2201819,11008,11227,566902,2834,2936\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,148,0.0036,2237115,11184,11499,555343,2776,2919\n",
+ "200,32,148,0.0036,2261819,11308,11527,581902,2909,3011\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,152,0.0037,2297115,11484,11799,573343,2866,3009\n",
+ "200,32,152,0.0037,2321819,11608,11827,596902,2984,3086\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,156,0.0038,2357115,11784,12099,585343,2926,3069\n",
+ "200,32,156,0.0038,2381819,11908,12127,611902,3059,3161\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,160,0.0038,2417115,12084,12399,603343,3016,3159\n",
+ "200,32,160,0.0040,2441819,12208,12427,626902,3134,3236\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,164,0.0039,2477115,12384,12699,615343,3076,3219\n",
+ "200,32,164,0.0039,2501819,12508,12727,641902,3209,3311\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,168,0.0039,2537115,12684,12999,633343,3166,3309\n",
+ "200,32,168,0.0040,2561819,12808,13027,656902,3284,3386\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,172,0.0040,2597115,12984,13299,645343,3226,3369\n",
+ "200,32,172,0.0040,2621819,13108,13327,671902,3359,3461\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,176,0.0041,2657115,13284,13599,663343,3316,3459\n",
+ "200,32,176,0.0041,2681819,13408,13627,686902,3434,3536\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,180,0.0041,2717115,13584,13899,675343,3376,3519\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
+ "200,32,180,0.0041,2741819,13708,13927,701902,3509,3611\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,184,0.0042,2777115,13884,14199,693343,3466,3609\n",
+ "200,32,184,0.0042,2801819,14008,14227,716902,3584,3686\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,188,0.0043,2837115,14184,14499,705343,3526,3669\n",
+ "200,32,188,0.0044,2861819,14308,14527,731902,3659,3761\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,192,0.0043,2897115,14484,14799,723343,3616,3759\n",
+ "200,32,192,0.0044,2921819,14608,14827,746902,3734,3836\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,196,0.0044,2957115,14784,15099,735343,3676,3819\n",
+ "200,32,196,0.0045,2981819,14908,15127,761902,3809,3911\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,200,0.0045,3017115,15084,15399,753343,3766,3909\n",
+ "200,32,200,0.0045,3041819,15208,15427,776902,3884,3986\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,204,0.0045,3077115,15384,15699,765343,3826,3969\n",
+ "200,32,204,0.0045,3101819,15508,15727,791902,3959,4061\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,208,0.0046,3137115,15684,15999,783343,3916,4059\n",
+ "200,32,208,0.0046,3161819,15808,16027,806902,4034,4136\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,212,0.0047,3197115,15984,16299,795343,3976,4119\n",
+ "200,32,212,0.0047,3221819,16108,16327,821902,4109,4211\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,216,0.0047,3257115,16284,16599,813343,4066,4209\n",
+ "200,32,216,0.0047,3281819,16408,16627,836902,4184,4286\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,220,0.0048,3317115,16584,16899,825343,4126,4269\n",
+ "200,32,220,0.0048,3341819,16708,16927,851902,4259,4361\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,224,0.0049,3377115,16884,17199,843343,4216,4359\n",
+ "200,32,224,0.0049,3401819,17008,17227,866902,4334,4436\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,228,0.0049,3437115,17184,17499,855343,4276,4419\n",
+ "200,32,228,0.0050,3461819,17308,17527,881902,4409,4511\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,232,0.0050,3497115,17484,17799,873343,4366,4509\n",
+ "200,32,232,0.0050,3521819,17608,17827,896902,4484,4586\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,236,0.0051,3557115,17784,18099,885343,4426,4569\n",
+ "200,32,236,0.0051,3581819,17908,18127,911902,4559,4661\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,240,0.0052,3617115,18084,18399,903343,4516,4659\n",
+ "200,32,240,0.0051,3641819,18208,18427,926902,4634,4736\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,244,0.0052,3677115,18384,18699,915343,4576,4719\n",
+ "200,32,244,0.0052,3701819,18508,18727,941902,4709,4811\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,248,0.0052,3737115,18684,18999,933343,4666,4809\n",
+ "200,32,248,0.0053,3761819,18808,19027,956902,4784,4886\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,252,0.0054,3797115,18984,19299,945343,4726,4869\n",
+ "200,32,252,0.0053,3821819,19108,19327,971902,4859,4961\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,256,0.0054,3857115,19284,19599,963343,4816,4959\n",
+ "200,32,256,0.0054,3881819,19408,19627,986902,4934,5036\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,260,0.0054,3917115,19584,19899,975343,4876,5019\n",
+ "200,32,260,0.0055,3941819,19708,19927,1001902,5009,5111\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,264,0.0055,3977115,19884,20199,993343,4966,5109\n",
+ "200,32,264,0.0055,4001819,20008,20227,1016902,5084,5186\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,268,0.0056,4037115,20184,20499,1005343,5026,5169\n",
+ "200,32,268,0.0056,4061819,20308,20527,1031902,5159,5261\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,272,0.0056,4097115,20484,20799,1023343,5116,5259\n",
+ "200,32,272,0.0057,4121819,20608,20827,1046902,5234,5336\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,276,0.0057,4157115,20784,21099,1035343,5176,5319\n",
+ "200,32,276,0.0057,4181819,20908,21127,1061902,5309,5411\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,280,0.0057,4217115,21084,21399,1053343,5266,5409\n",
+ "200,32,280,0.0058,4241819,21208,21427,1076902,5384,5486\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,284,0.0058,4277115,21384,21699,1065343,5326,5469\n",
+ "200,32,284,0.0059,4301819,21508,21727,1091902,5459,5561\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,288,0.0059,4337115,21684,21999,1083343,5416,5559\n",
+ "200,32,288,0.0059,4361819,21808,22027,1106902,5534,5636\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,292,0.0059,4397115,21984,22299,1095343,5476,5619\n",
+ "200,32,292,0.0060,4421819,22108,22327,1121902,5609,5711\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,296,0.0061,4457115,22284,22599,1113343,5566,5709\n",
+ "200,32,296,0.0061,4481819,22408,22627,1136902,5684,5786\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,300,0.0061,4517115,22584,22899,1125343,5626,5769\n",
+ "200,32,300,0.0061,4541819,22708,22927,1151902,5759,5861\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,304,0.0061,4577115,22884,23199,1143343,5716,5859\n",
+ "200,32,304,0.0062,4601819,23008,23227,1166902,5834,5936\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,308,0.0062,4637115,23184,23499,1155343,5776,5919\n",
+ "200,32,308,0.0063,4661819,23308,23527,1181902,5909,6011\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,312,0.0063,4697115,23484,23799,1173343,5866,6009\n",
+ "200,32,312,0.0064,4721819,23608,23827,1196902,5984,6086\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,316,0.0064,4757115,23784,24099,1185343,5926,6069\n",
+ "200,32,316,0.0066,4781819,23908,24127,1211902,6059,6161\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,320,0.0064,4817115,24084,24399,1203343,6016,6159\n",
+ "200,32,320,0.0065,4841819,24208,24427,1226902,6134,6236\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,324,0.0065,4877115,24384,24699,1215343,6076,6219\n",
+ "200,32,324,0.0065,4901819,24508,24727,1241902,6209,6311\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,328,0.0065,4937115,24684,24999,1233343,6166,6309\n",
+ "200,32,328,0.0069,4961819,24808,25027,1256902,6284,6386\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,332,0.0066,4997115,24984,25299,1245343,6226,6369\n",
+ "200,32,332,0.0066,5021819,25108,25327,1271902,6359,6461\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,336,0.0066,5057115,25284,25599,1263343,6316,6459\n",
+ "200,32,336,0.0067,5081819,25408,25627,1286902,6434,6536\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,340,0.0068,5117115,25584,25899,1275343,6376,6519\n",
+ "200,32,340,0.0068,5141819,25708,25927,1301902,6509,6611\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,344,0.0068,5177115,25884,26199,1293343,6466,6609\n",
+ "200,32,344,0.0069,5201819,26008,26227,1316902,6584,6686\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,348,0.0069,5237115,26184,26499,1305343,6526,6669\n",
+ "200,32,348,0.0069,5261819,26308,26527,1331902,6659,6761\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,352,0.0071,5297115,26484,26799,1323343,6616,6759\n",
+ "200,32,352,0.0070,5321819,26608,26827,1346902,6734,6836\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,356,0.0070,5357115,26784,27099,1335343,6676,6819\n",
+ "200,32,356,0.0070,5381819,26908,27127,1361902,6809,6911\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,360,0.0070,5417115,27084,27399,1353343,6766,6909\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
+ "200,32,360,0.0071,5441819,27208,27427,1376902,6884,6986\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,364,0.0071,5477115,27384,27699,1365343,6826,6969\n",
+ "200,32,364,0.0072,5501819,27508,27727,1391902,6959,7061\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,368,0.0072,5537115,27684,27999,1383343,6916,7059\n",
+ "200,32,368,0.0072,5561819,27808,28027,1406902,7034,7136\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,372,0.0073,5597115,27984,28299,1395343,6976,7119\n",
+ "200,32,372,0.0073,5621819,28108,28327,1421902,7109,7211\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,376,0.0073,5657115,28284,28599,1413343,7066,7209\n",
+ "200,32,376,0.0074,5681819,28408,28627,1436902,7184,7286\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,380,0.0074,5717115,28584,28899,1425343,7126,7269\n",
+ "200,32,380,0.0074,5741819,28708,28927,1451902,7259,7361\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,384,0.0074,5777115,28884,29199,1443343,7216,7359\n",
+ "200,32,384,0.0075,5801819,29008,29227,1466902,7334,7436\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,388,0.0075,5837115,29184,29499,1455343,7276,7419\n",
+ "200,32,388,0.0076,5861819,29308,29527,1481902,7409,7511\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,392,0.0076,5897115,29484,29799,1473343,7366,7509\n",
+ "200,32,392,0.0076,5921819,29608,29827,1496902,7484,7586\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,396,0.0076,5957115,29784,30099,1485343,7426,7569\n",
+ "200,32,396,0.0077,5981819,29908,30127,1511902,7559,7661\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,400,0.0078,6017115,30084,30399,1503343,7516,7659\n",
+ "200,32,400,0.0078,6041819,30208,30427,1526902,7634,7736\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,404,0.0078,6077115,30384,30699,1515343,7576,7719\n",
+ "200,32,404,0.0079,6101819,30508,30727,1541902,7709,7811\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,408,0.0078,6137115,30684,30999,1533343,7666,7809\n",
+ "200,32,408,0.0079,6161819,30808,31027,1556902,7784,7886\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,412,0.0079,6197115,30984,31299,1545343,7726,7869\n",
+ "200,32,412,0.0080,6221819,31108,31327,1571902,7859,7961\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,416,0.0080,6257115,31284,31599,1563343,7816,7959\n",
+ "200,32,416,0.0081,6281819,31408,31627,1586902,7934,8036\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,420,0.0080,6317115,31584,31899,1575343,7876,8019\n",
+ "200,32,420,0.0081,6341819,31708,31927,1601902,8009,8111\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,424,0.0081,6377115,31884,32199,1593343,7966,8109\n",
+ "200,32,424,0.0082,6401819,32008,32227,1616902,8084,8186\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,428,0.0081,6437115,32184,32499,1605343,8026,8169\n",
+ "200,32,428,0.0082,6461819,32308,32527,1631902,8159,8261\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,432,0.0082,6497115,32484,32799,1623343,8116,8259\n",
+ "200,32,432,0.0085,6521819,32608,32827,1646902,8234,8336\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,436,0.0083,6557115,32784,33099,1635343,8176,8319\n",
+ "200,32,436,0.0084,6581819,32908,33127,1661902,8309,8411\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,440,0.0083,6617115,33084,33399,1653343,8266,8409\n",
+ "200,32,440,0.0084,6641819,33208,33427,1676902,8384,8486\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,444,0.0084,6677115,33384,33699,1665343,8326,8469\n",
+ "200,32,444,0.0085,6701819,33508,33727,1691902,8459,8561\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,448,0.0085,6737115,33684,33999,1683343,8416,8559\n",
+ "200,32,448,0.0087,6761819,33808,34027,1706902,8534,8636\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,452,0.0085,6797115,33984,34299,1695343,8476,8619\n",
+ "200,32,452,0.0087,6821819,34108,34327,1721902,8609,8711\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,456,0.0086,6857115,34284,34599,1713343,8566,8709\n",
+ "200,32,456,0.0087,6881819,34408,34627,1736902,8684,8786\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,460,0.0087,6917115,34584,34899,1725343,8626,8769\n",
+ "200,32,460,0.0088,6941819,34708,34927,1751902,8759,8861\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,464,0.0088,6977115,34884,35199,1743343,8716,8859\n",
+ "200,32,464,0.0088,7001819,35008,35227,1766902,8834,8936\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,468,0.0088,7037115,35184,35499,1755343,8776,8919\n",
+ "200,32,468,0.0089,7061819,35308,35527,1781902,8909,9011\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,472,0.0089,7097115,35484,35799,1773343,8866,9009\n",
+ "200,32,472,0.0090,7121819,35608,35827,1796902,8984,9086\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,476,0.0090,7157115,35784,36099,1785343,8926,9069\n",
+ "200,32,476,0.0091,7181819,35908,36127,1811902,9059,9161\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,480,0.0090,7217115,36084,36399,1803343,9016,9159\n",
+ "200,32,480,0.0091,7241819,36208,36427,1826902,9134,9236\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,484,0.0091,7277115,36384,36699,1815343,9076,9219\n",
+ "200,32,484,0.0092,7301819,36508,36727,1841902,9209,9311\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,488,0.0091,7337115,36684,36999,1833343,9166,9309\n",
+ "200,32,488,0.0093,7361819,36808,37027,1856902,9284,9386\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,492,0.0092,7397115,36984,37299,1845343,9226,9369\n",
+ "200,32,492,0.0094,7421819,37108,37327,1871902,9359,9461\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,496,0.0093,7457115,37284,37599,1863343,9316,9459\n",
+ "200,32,496,0.0095,7481819,37408,37627,1886902,9434,9536\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,500,0.0093,7517115,37584,37899,1875343,9376,9519\n",
+ "200,32,500,0.0094,7541819,37708,37927,1901902,9509,9611\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,504,0.0094,7577115,37884,38199,1893343,9466,9609\n",
+ "200,32,504,0.0095,7601819,38008,38227,1916902,9584,9686\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,508,0.0095,7637115,38184,38499,1905343,9526,9669\n",
+ "200,32,508,0.0096,7661819,38308,38527,1931902,9659,9761\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,512,0.0095,7697115,38484,38799,1923343,9616,9759\n",
+ "200,32,512,0.0097,7721819,38608,38827,1946902,9734,9836\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,516,0.0096,7757115,38784,39099,1938343,9691,9834\n",
+ "200,32,516,0.0098,7781819,38908,39127,1961902,9809,9911\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,520,0.0097,7817115,39084,39399,1953343,9766,9909\n",
+ "200,32,520,0.0098,7841819,39208,39427,1976902,9884,9986\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,524,0.0097,7877115,39384,39699,1968343,9841,9984\n",
+ "200,32,524,0.0099,7901819,39508,39727,1991902,9959,10061\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,528,0.0098,7937115,39684,39999,1983343,9916,10059\n",
+ "200,32,528,0.0099,7961819,39808,40027,2006902,10034,10136\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,532,0.0099,7997115,39984,40299,1998343,9991,10134\n",
+ "200,32,532,0.0100,8021819,40108,40327,2021902,10109,10211\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,536,0.0100,8057115,40284,40599,2013343,10066,10209\n",
+ "200,32,536,0.0101,8081819,40408,40627,2036902,10184,10286\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,540,0.0101,8117115,40584,40899,2028343,10141,10284\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
+ "200,32,540,0.0101,8141819,40708,40927,2051902,10259,10361\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,544,0.0101,8177115,40884,41199,2043343,10216,10359\n",
+ "200,32,544,0.0103,8201819,41008,41227,2066902,10334,10436\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,548,0.0102,8237115,41184,41499,2058343,10291,10434\n",
+ "200,32,548,0.0103,8261819,41308,41527,2081902,10409,10511\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,552,0.0103,8297115,41484,41799,2073343,10366,10509\n",
+ "200,32,552,0.0104,8321819,41608,41827,2096902,10484,10586\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,556,0.0104,8357115,41784,42099,2088343,10441,10584\n",
+ "200,32,556,0.0106,8381819,41908,42127,2111902,10559,10661\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,560,0.0104,8417115,42084,42399,2103343,10516,10659\n",
+ "200,32,560,0.0106,8441819,42208,42427,2126902,10634,10736\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,564,0.0105,8477115,42384,42699,2118343,10591,10734\n",
+ "200,32,564,0.0106,8501819,42508,42727,2141902,10709,10811\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,568,0.0106,8537115,42684,42999,2133343,10666,10809\n",
+ "200,32,568,0.0107,8561819,42808,43027,2156902,10784,10886\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,572,0.0106,8597115,42984,43299,2148343,10741,10884\n",
+ "200,32,572,0.0108,8621819,43108,43327,2171902,10859,10961\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,576,0.0107,8657115,43284,43599,2163343,10816,10959\n",
+ "200,32,576,0.0109,8681819,43408,43627,2186902,10934,11036\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,580,0.0109,8717115,43584,43899,2178343,10891,11034\n",
+ "200,32,580,0.0110,8741819,43708,43927,2201902,11009,11111\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,584,0.0108,8777115,43884,44199,2193343,10966,11109\n",
+ "200,32,584,0.0110,8801819,44008,44227,2216902,11084,11186\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,588,0.0110,8837115,44184,44499,2208343,11041,11184\n",
+ "200,32,588,0.0110,8861819,44308,44527,2231902,11159,11261\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,592,0.0110,8897115,44484,44799,2223343,11116,11259\n",
+ "200,32,592,0.0111,8921819,44608,44827,2246902,11234,11336\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,596,0.0111,8957115,44784,45099,2238343,11191,11334\n",
+ "200,32,596,0.0113,8981819,44908,45127,2261902,11309,11411\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,600,0.0111,9017115,45084,45399,2253343,11266,11409\n",
+ "200,32,600,0.0113,9041819,45208,45427,2276902,11384,11486\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,604,0.0112,9077115,45384,45699,2268343,11341,11484\n",
+ "200,32,604,0.0114,9101819,45508,45727,2291902,11459,11561\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,608,0.0113,9137115,45684,45999,2283343,11416,11559\n",
+ "200,32,608,0.0115,9161819,45808,46027,2306902,11534,11636\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,612,0.0113,9197115,45984,46299,2298343,11491,11634\n",
+ "200,32,612,0.0115,9221819,46108,46327,2321902,11609,11711\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,616,0.0114,9257115,46284,46599,2313343,11566,11709\n",
+ "200,32,616,0.0115,9281819,46408,46627,2336902,11684,11786\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,620,0.0115,9317115,46584,46899,2328343,11641,11784\n",
+ "200,32,620,0.0116,9341819,46708,46927,2351902,11759,11861\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,624,0.0115,9377115,46884,47199,2343343,11716,11859\n",
+ "200,32,624,0.0117,9401819,47008,47227,2366902,11834,11936\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,628,0.0115,9437115,47184,47499,2358343,11791,11934\n",
+ "200,32,628,0.0117,9461819,47308,47527,2381902,11909,12011\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,632,0.0117,9497115,47484,47799,2373343,11866,12009\n",
+ "200,32,632,0.0118,9521819,47608,47827,2396902,11984,12086\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,636,0.0118,9557115,47784,48099,2388343,11941,12084\n",
+ "200,32,636,0.0119,9581819,47908,48127,2411902,12059,12161\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,640,0.0119,9617115,48084,48399,2403343,12016,12159\n",
+ "200,32,640,0.0119,9641819,48208,48427,2426902,12134,12236\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,644,0.0118,9677115,48384,48699,2418343,12091,12234\n",
+ "200,32,644,0.0121,9701819,48508,48727,2441902,12209,12311\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,648,0.0119,9737115,48684,48999,2433343,12166,12309\n",
+ "200,32,648,0.0121,9761819,48808,49027,2456902,12284,12386\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,652,0.0121,9797115,48984,49299,2448343,12241,12384\n",
+ "200,32,652,0.0121,9821819,49108,49327,2471902,12359,12461\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,656,0.0121,9857115,49284,49599,2463343,12316,12459\n",
+ "200,32,656,0.0122,9881819,49408,49627,2486902,12434,12536\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,660,0.0122,9917115,49584,49899,2478343,12391,12534\n",
+ "200,32,660,0.0123,9941819,49708,49927,2501902,12509,12611\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,664,0.0122,9977115,49884,50199,2493343,12466,12609\n",
+ "200,32,664,0.0123,10001819,50008,50227,2516902,12584,12686\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,668,0.0123,10037115,50184,50499,2508343,12541,12684\n",
+ "200,32,668,0.0124,10061819,50308,50527,2531902,12659,12761\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,672,0.0123,10097115,50484,50799,2523343,12616,12759\n",
+ "200,32,672,0.0124,10121819,50608,50827,2546902,12734,12836\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,676,0.0125,10157115,50784,51099,2538343,12691,12834\n",
+ "200,32,676,0.0126,10181819,50908,51127,2561902,12809,12911\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,680,0.0124,10217115,51084,51399,2553343,12766,12909\n",
+ "200,32,680,0.0126,10241819,51208,51427,2576902,12884,12986\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,684,0.0125,10277115,51384,51699,2568343,12841,12984\n",
+ "200,32,684,0.0127,10301819,51508,51727,2591902,12959,13061\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,688,0.0126,10337115,51684,51999,2583343,12916,13059\n",
+ "200,32,688,0.0128,10361819,51808,52027,2606902,13034,13136\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,692,0.0126,10397115,51984,52299,2598343,12991,13134\n",
+ "200,32,692,0.0128,10421819,52108,52327,2621902,13109,13211\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,696,0.0127,10457115,52284,52599,2613343,13066,13209\n",
+ "200,32,696,0.0129,10481819,52408,52627,2636902,13184,13286\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,700,0.0128,10517115,52584,52899,2628343,13141,13284\n",
+ "200,32,700,0.0131,10541819,52708,52927,2651902,13259,13361\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,704,0.0129,10577115,52884,53199,2643343,13216,13359\n",
+ "200,32,704,0.0131,10601819,53008,53227,2666902,13334,13436\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,708,0.0129,10637115,53184,53499,2658343,13291,13434\n",
+ "200,32,708,0.0130,10661819,53308,53527,2681902,13409,13511\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,712,0.0129,10697115,53484,53799,2673343,13366,13509\n",
+ "200,32,712,0.0131,10721819,53608,53827,2696902,13484,13586\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,716,0.0130,10757115,53784,54099,2688343,13441,13584\n",
+ "200,32,716,0.0132,10781819,53908,54127,2711902,13559,13661\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,720,0.0130,10817115,54084,54399,2703343,13516,13659\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
+ "200,32,720,0.0132,10841819,54208,54427,2726902,13634,13736\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,724,0.0132,10877115,54384,54699,2718343,13591,13734\n",
+ "200,32,724,0.0134,10901819,54508,54727,2741902,13709,13811\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,728,0.0131,10937115,54684,54999,2733343,13666,13809\n",
+ "200,32,728,0.0134,10961819,54808,55027,2756902,13784,13886\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,732,0.0133,10997115,54984,55299,2748343,13741,13884\n",
+ "200,32,732,0.0134,11021819,55108,55327,2771902,13859,13961\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,736,0.0135,11057115,55284,55599,2763343,13816,13959\n",
+ "200,32,736,0.0135,11081819,55408,55627,2786902,13934,14036\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,740,0.0134,11117115,55584,55899,2778343,13891,14034\n",
+ "200,32,740,0.0137,11141819,55708,55927,2801902,14009,14111\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,744,0.0134,11177115,55884,56199,2793343,13966,14109\n",
+ "200,32,744,0.0138,11201819,56008,56227,2816902,14084,14186\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,748,0.0135,11237115,56184,56499,2808343,14041,14184\n",
+ "200,32,748,0.0137,11261819,56308,56527,2831902,14159,14261\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,752,0.0136,11297115,56484,56799,2823343,14116,14259\n",
+ "200,32,752,0.0138,11321819,56608,56827,2846902,14234,14336\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,756,0.0136,11357115,56784,57099,2838343,14191,14334\n",
+ "200,32,756,0.0139,11381819,56908,57127,2861902,14309,14411\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,760,0.0138,11417115,57084,57399,2853343,14266,14409\n",
+ "200,32,760,0.0140,11441819,57208,57427,2876902,14384,14486\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,764,0.0139,11477115,57384,57699,2868343,14341,14484\n",
+ "200,32,764,0.0140,11501819,57508,57727,2891902,14459,14561\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,768,0.0138,11537115,57684,57999,2883343,14416,14559\n",
+ "200,32,768,0.0141,11561819,57808,58027,2906902,14534,14636\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,772,0.0140,11597115,57984,58299,2898343,14491,14634\n",
+ "200,32,772,0.0141,11621819,58108,58327,2921902,14609,14711\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,776,0.0140,11657115,58284,58599,2913343,14566,14709\n",
+ "200,32,776,0.0142,11681819,58408,58627,2936902,14684,14786\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,780,0.0142,11717115,58584,58899,2928343,14641,14784\n",
+ "200,32,780,0.0143,11741819,58708,58927,2951902,14759,14861\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,784,0.0141,11777115,58884,59199,2943343,14716,14859\n",
+ "200,32,784,0.0144,11801819,59008,59227,2966902,14834,14936\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,788,0.0143,11837115,59184,59499,2958343,14791,14934\n",
+ "200,32,788,0.0144,11861819,59308,59527,2981902,14909,15011\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,792,0.0143,11897115,59484,59799,2973343,14866,15009\n",
+ "200,32,792,0.0145,11921819,59608,59827,2996902,14984,15086\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,796,0.0146,11957115,59784,60099,2988343,14941,15084\n",
+ "200,32,796,0.0145,11981819,59908,60127,3011902,15059,15161\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,800,0.0144,12017115,60084,60399,3003343,15016,15159\n",
+ "200,32,800,0.0147,12041819,60208,60427,3026902,15134,15236\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,804,0.0145,12077115,60384,60699,3018343,15091,15234\n",
+ "200,32,804,0.0147,12101819,60508,60727,3041902,15209,15311\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,808,0.0146,12137115,60684,60999,3033343,15166,15309\n",
+ "200,32,808,0.0148,12161819,60808,61027,3056902,15284,15386\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,812,0.0146,12197115,60984,61299,3048343,15241,15384\n",
+ "200,32,812,0.0148,12221819,61108,61327,3071902,15359,15461\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,816,0.0146,12257115,61284,61599,3063343,15316,15459\n",
+ "200,32,816,0.0150,12281819,61408,61627,3086902,15434,15536\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,820,0.0148,12317115,61584,61899,3078343,15391,15534\n",
+ "200,32,820,0.0149,12341819,61708,61927,3101902,15509,15611\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,824,0.0149,12377115,61884,62199,3093343,15466,15609\n",
+ "200,32,824,0.0150,12401819,62008,62227,3116902,15584,15686\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,828,0.0149,12437115,62184,62499,3108343,15541,15684\n",
+ "200,32,828,0.0151,12461819,62308,62527,3131902,15659,15761\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,832,0.0149,12497115,62484,62799,3123343,15616,15759\n",
+ "200,32,832,0.0152,12521819,62608,62827,3146902,15734,15836\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,836,0.0151,12557115,62784,63099,3138343,15691,15834\n",
+ "200,32,836,0.0152,12581819,62908,63127,3161902,15809,15911\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,840,0.0150,12617115,63084,63399,3153343,15766,15909\n",
+ "200,32,840,0.0153,12641819,63208,63427,3176902,15884,15986\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,844,0.0152,12677115,63384,63699,3168343,15841,15984\n",
+ "200,32,844,0.0153,12701819,63508,63727,3191902,15959,16061\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,848,0.0152,12737115,63684,63999,3183343,15916,16059\n",
+ "200,32,848,0.0154,12761819,63808,64027,3206902,16034,16136\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,852,0.0153,12797115,63984,64299,3198343,15991,16134\n",
+ "200,32,852,0.0155,12821819,64108,64327,3221902,16109,16211\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,856,0.0153,12857115,64284,64599,3213343,16066,16209\n",
+ "200,32,856,0.0156,12881819,64408,64627,3236902,16184,16286\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,860,0.0155,12917115,64584,64899,3228343,16141,16284\n",
+ "200,32,860,0.0156,12941819,64708,64927,3251902,16259,16361\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,864,0.0156,12977115,64884,65199,3243343,16216,16359\n",
+ "200,32,864,0.0157,13001819,65008,65227,3266902,16334,16436\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,868,0.0157,13037115,65184,65499,3258343,16291,16434\n",
+ "200,32,868,0.0158,13061819,65308,65527,3281902,16409,16511\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,872,0.0156,13097115,65484,65799,3273343,16366,16509\n",
+ "200,32,872,0.0159,13121819,65608,65827,3296902,16484,16586\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,876,0.0157,13157115,65784,66099,3288343,16441,16584\n",
+ "200,32,876,0.0159,13181819,65908,66127,3311902,16559,16661\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,880,0.0158,13217115,66084,66399,3303343,16516,16659\n",
+ "200,32,880,0.0160,13241819,66208,66427,3326902,16634,16736\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,884,0.0158,13277115,66384,66699,3318343,16591,16734\n",
+ "200,32,884,0.0160,13301819,66508,66727,3341902,16709,16811\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,888,0.0159,13337115,66684,66999,3333343,16666,16809\n",
+ "200,32,888,0.0161,13361819,66808,67027,3356902,16784,16886\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,892,0.0160,13397115,66984,67299,3348343,16741,16884\n",
+ "200,32,892,0.0162,13421819,67108,67327,3371902,16859,16961\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,896,0.0161,13457115,67284,67599,3363343,16816,16959\n",
+ "200,32,896,0.0163,13481819,67408,67627,3386902,16934,17036\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,900,0.0162,13517115,67584,67899,3378343,16891,17034\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
+ "200,32,900,0.0164,13541819,67708,67927,3401902,17009,17111\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,904,0.0163,13577115,67884,68199,3393343,16966,17109\n",
+ "200,32,904,0.0165,13601819,68008,68227,3416902,17084,17186\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,908,0.0164,13637115,68184,68499,3408343,17041,17184\n",
+ "200,32,908,0.0165,13661819,68308,68527,3431902,17159,17261\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,912,0.0165,13697115,68484,68799,3423343,17116,17259\n",
+ "200,32,912,0.0166,13721819,68608,68827,3446902,17234,17336\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,916,0.0165,13757115,68784,69099,3438343,17191,17334\n",
+ "200,32,916,0.0166,13781819,68908,69127,3461902,17309,17411\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,920,0.0165,13817115,69084,69399,3453343,17266,17409\n",
+ "200,32,920,0.0167,13841819,69208,69427,3476902,17384,17486\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,924,0.0168,13877115,69384,69699,3468343,17341,17484\n",
+ "200,32,924,0.0168,13901819,69508,69727,3491902,17459,17561\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,928,0.0167,13937115,69684,69999,3483343,17416,17559\n",
+ "200,32,928,0.0169,13961819,69808,70027,3506902,17534,17636\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,932,0.0169,13997115,69984,70299,3498343,17491,17634\n",
+ "200,32,932,0.0175,14021819,70108,70327,3521902,17609,17711\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,936,0.0168,14057115,70284,70599,3513343,17566,17709\n",
+ "200,32,936,0.0170,14081819,70408,70627,3536902,17684,17786\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,940,0.0169,14117115,70584,70899,3528343,17641,17784\n",
+ "200,32,940,0.0171,14141819,70708,70927,3551902,17759,17861\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,944,0.0169,14177115,70884,71199,3543343,17716,17859\n",
+ "200,32,944,0.0171,14201819,71008,71227,3566902,17834,17936\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,948,0.0170,14237115,71184,71499,3558343,17791,17934\n",
+ "200,32,948,0.0172,14261819,71308,71527,3581902,17909,18011\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,952,0.0171,14297115,71484,71799,3573343,17866,18009\n",
+ "200,32,952,0.0172,14321819,71608,71827,3596902,17984,18086\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,956,0.0173,14357115,71784,72099,3588343,17941,18084\n",
+ "200,32,956,0.0173,14381819,71908,72127,3611902,18059,18161\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,960,0.0172,14417115,72084,72399,3603343,18016,18159\n",
+ "200,32,960,0.0174,14441819,72208,72427,3626902,18134,18236\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,964,0.0177,14477115,72384,72699,3618343,18091,18234\n",
+ "200,32,964,0.0176,14501819,72508,72727,3641902,18209,18311\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,968,0.0177,14537115,72684,72999,3633343,18166,18309\n",
+ "200,32,968,0.0178,14561819,72808,73027,3656902,18284,18386\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,972,0.0177,14597115,72984,73299,3648343,18241,18384\n",
+ "200,32,972,0.0177,14621819,73108,73327,3671902,18359,18461\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,976,0.0179,14657115,73284,73599,3663343,18316,18459\n",
+ "200,32,976,0.0178,14681819,73408,73627,3686902,18434,18536\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,980,0.0180,14717115,73584,73899,3678343,18391,18534\n",
+ "200,32,980,0.0179,14741819,73708,73927,3701902,18509,18611\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,984,0.0180,14777115,73884,74199,3693343,18466,18609\n",
+ "200,32,984,0.0179,14801819,74008,74227,3716902,18584,18686\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,988,0.0180,14837115,74184,74499,3708343,18541,18684\n",
+ "200,32,988,0.0180,14861819,74308,74527,3731902,18659,18761\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,992,0.0181,14897115,74484,74799,3723343,18616,18759\n",
+ "200,32,992,0.0181,14921819,74608,74827,3746902,18734,18836\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,996,0.0184,14957115,74784,75099,3738343,18691,18834\n",
+ "200,32,996,0.0182,14981819,74908,75127,3761902,18809,18911\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,1000,0.0182,15017115,75084,75399,3753343,18766,18909\n",
+ "200,32,1000,0.0182,15041819,75208,75427,3776902,18884,18986\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,1004,0.0183,15077115,75384,75699,3768343,18841,18984\n",
+ "200,32,1004,0.0183,15101819,75508,75727,3791902,18959,19061\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,1008,0.0184,15137115,75684,75999,3783343,18916,19059\n",
+ "200,32,1008,0.0183,15161819,75808,76027,3806902,19034,19136\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,1012,0.0185,15197115,75984,76299,3798343,18991,19134\n",
+ "200,32,1012,0.0184,15221819,76108,76327,3821902,19109,19211\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,1016,0.0185,15257115,76284,76599,3813343,19066,19209\n",
+ "200,32,1016,0.0185,15281819,76408,76627,3836902,19184,19286\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,1020,0.0186,15317115,76584,76899,3828343,19141,19284\n",
+ "200,32,1020,0.0185,15341819,76708,76927,3851902,19259,19361\n",
"iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,1024,0.0183,15377115,76884,77199,3843343,19216,19359\n",
- "mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.ld_st.bin.csv .\n"
+ "200,32,1024,0.0186,15401819,77008,77227,3866902,19334,19436\n",
+ "mv /gpfs/wolf/trn003/scratch/aherten//poisson2d.ld_st.bin.csv .\n"
]
}
],
@@ -1490,12 +1594,12 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Once the run finished, let's plot it again with the following cell (non-interactive: `make graph_task2a`)."
+ "Once the run finished, let's plot it again in the course of the following cells (non-interactive: `make graph_task2a`)."
]
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 8,
"metadata": {},
"outputs": [
{
@@ -1529,8 +1633,7 @@
" <th>PM_ST_CMPL (total)</th>\n",
" <th>PM_ST_CMPL (min)</th>\n",
" <th>PM_ST_CMPL (max)</th>\n",
- " <th>Loads / Loop Iteration</th>\n",
- " <th>Stores / Loop Iteration</th>\n",
+ " <th>Grid Points</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
@@ -1540,29 +1643,27 @@
" <td>32</td>\n",
" <td>4</td>\n",
" <td>0.0012</td>\n",
- " <td>95115</td>\n",
- " <td>474</td>\n",
- " <td>789</td>\n",
- " <td>21343</td>\n",
- " <td>106</td>\n",
- " <td>249</td>\n",
- " <td>3.703125</td>\n",
- " <td>0.828125</td>\n",
+ " <td>119819</td>\n",
+ " <td>598</td>\n",
+ " <td>817</td>\n",
+ " <td>32902</td>\n",
+ " <td>164</td>\n",
+ " <td>266</td>\n",
+ " <td>128</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>200</td>\n",
" <td>32</td>\n",
" <td>8</td>\n",
- " <td>0.0014</td>\n",
- " <td>137115</td>\n",
- " <td>684</td>\n",
- " <td>999</td>\n",
- " <td>33343</td>\n",
- " <td>166</td>\n",
- " <td>309</td>\n",
- " <td>2.671875</td>\n",
- " <td>0.648438</td>\n",
+ " <td>0.0013</td>\n",
+ " <td>161819</td>\n",
+ " <td>808</td>\n",
+ " <td>1027</td>\n",
+ " <td>56902</td>\n",
+ " <td>284</td>\n",
+ " <td>386</td>\n",
+ " <td>256</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
@@ -1570,14 +1671,13 @@
" <td>32</td>\n",
" <td>12</td>\n",
" <td>0.0014</td>\n",
- " <td>197115</td>\n",
- " <td>984</td>\n",
- " <td>1299</td>\n",
- " <td>45343</td>\n",
- " <td>226</td>\n",
- " <td>369</td>\n",
- " <td>2.562500</td>\n",
- " <td>0.588542</td>\n",
+ " <td>221819</td>\n",
+ " <td>1108</td>\n",
+ " <td>1327</td>\n",
+ " <td>71902</td>\n",
+ " <td>359</td>\n",
+ " <td>461</td>\n",
+ " <td>384</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
@@ -1585,29 +1685,27 @@
" <td>32</td>\n",
" <td>16</td>\n",
" <td>0.0015</td>\n",
- " <td>257115</td>\n",
- " <td>1284</td>\n",
- " <td>1599</td>\n",
- " <td>63343</td>\n",
- " <td>316</td>\n",
- " <td>459</td>\n",
- " <td>2.507812</td>\n",
- " <td>0.617188</td>\n",
+ " <td>281819</td>\n",
+ " <td>1408</td>\n",
+ " <td>1627</td>\n",
+ " <td>86902</td>\n",
+ " <td>434</td>\n",
+ " <td>536</td>\n",
+ " <td>512</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>200</td>\n",
" <td>32</td>\n",
" <td>20</td>\n",
- " <td>0.0016</td>\n",
- " <td>317115</td>\n",
- " <td>1584</td>\n",
- " <td>1899</td>\n",
- " <td>75343</td>\n",
- " <td>376</td>\n",
- " <td>519</td>\n",
- " <td>2.475000</td>\n",
- " <td>0.587500</td>\n",
+ " <td>0.0015</td>\n",
+ " <td>341819</td>\n",
+ " <td>1708</td>\n",
+ " <td>1927</td>\n",
+ " <td>101902</td>\n",
+ " <td>509</td>\n",
+ " <td>611</td>\n",
+ " <td>640</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
@@ -1615,59 +1713,130 @@
],
"text/plain": [
" iter ny nx Runtime PM_LD_CMPL (total) PM_LD_CMPL (min) \\\n",
- "0 200 32 4 0.0012 95115 474 \n",
- "1 200 32 8 0.0014 137115 684 \n",
- "2 200 32 12 0.0014 197115 984 \n",
- "3 200 32 16 0.0015 257115 1284 \n",
- "4 200 32 20 0.0016 317115 1584 \n",
+ "0 200 32 4 0.0012 119819 598 \n",
+ "1 200 32 8 0.0013 161819 808 \n",
+ "2 200 32 12 0.0014 221819 1108 \n",
+ "3 200 32 16 0.0015 281819 1408 \n",
+ "4 200 32 20 0.0015 341819 1708 \n",
"\n",
" PM_LD_CMPL (max) PM_ST_CMPL (total) PM_ST_CMPL (min) PM_ST_CMPL (max) \\\n",
- "0 789 21343 106 249 \n",
- "1 999 33343 166 309 \n",
- "2 1299 45343 226 369 \n",
- "3 1599 63343 316 459 \n",
- "4 1899 75343 376 519 \n",
+ "0 817 32902 164 266 \n",
+ "1 1027 56902 284 386 \n",
+ "2 1327 71902 359 461 \n",
+ "3 1627 86902 434 536 \n",
+ "4 1927 101902 509 611 \n",
"\n",
- " Loads / Loop Iteration Stores / Loop Iteration \n",
- "0 3.703125 0.828125 \n",
- "1 2.671875 0.648438 \n",
- "2 2.562500 0.588542 \n",
- "3 2.507812 0.617188 \n",
- "4 2.475000 0.587500 "
+ " Grid Points \n",
+ "0 128 \n",
+ "1 256 \n",
+ "2 384 \n",
+ "3 512 \n",
+ "4 640 "
]
},
- "execution_count": 6,
+ "execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_ldst = pd.read_csv(\"poisson2d.ld_st.bin.csv\", skiprows=range(2, 50000, 2))\n",
- "common.normalize(df_ldst, \"PM_LD_CMPL (min)\", \"Loads / Loop Iteration\")\n",
- "common.normalize(df_ldst, \"PM_ST_CMPL (min)\", \"Stores / Loop Iteration\")\n",
+ "df_ldst[\"Grid Points\"] = df_ldst[\"nx\"] * df_ldst[\"ny\"] \n",
"df_ldst.head()"
]
},
{
"cell_type": "code",
- "execution_count": 79,
+ "execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 1008x432 with 2 Axes>"
]
},
- "metadata": {},
+ "metadata": {
+ "needs_background": "light"
+ },
"output_type": "display_data"
}
],
"source": [
"fig, (ax1, ax2) = plt.subplots(nrows=2, sharex=True)\n",
- "df_ldst.set_index(\"nx\")[\"Loads / Loop Iteration\"].plot(ax=ax1, legend=True);\n",
- "df_ldst.set_index(\"nx\")[\"Stores / Loop Iteration\"].plot(ax=ax2, legend=True);"
+ "df_ldst.set_index(\"Grid Points\")[\"PM_LD_CMPL (min)\"].plot(ax=ax1, legend=True);\n",
+ "df_ldst.set_index(\"Grid Points\")[\"PM_ST_CMPL (min)\"].plot(ax=ax2, legend=True);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Also this behaviour looks – at a first glance – linear. We can again fit a first-order polynom (and re-use our previously defined function `curve_fit`)!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Counter PM_LD_CMPL (min) is proportional to the grid points (nx*ny) by a factor of 2.3437 (± 0.000037)\n",
+ "Counter PM_ST_CMPL (min) is proportional to the grid points (nx*ny) by a factor of 0.5860 (± 0.000019)\n"
+ ]
+ }
+ ],
+ "source": [
+ "_fit, _cov = common.print_and_return_fit(\n",
+ " [\"PM_LD_CMPL (min)\", \"PM_ST_CMPL (min)\"], \n",
+ " df_ldst.set_index(\"Grid Points\"), \n",
+ " linear_function,\n",
+ " format_value=\".4f\"\n",
+ ")\n",
+ "fit_parameters = {**fit_parameters, **_fit}\n",
+ "fit_covariance = {**fit_covariance, **_cov}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's overlay this in one common plot:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1008x432 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, (ax1, ax2) = plt.subplots(nrows=2, sharex=True)\n",
+ "for ax, pmu_counter in zip([ax1, ax2], [\"PM_LD_CMPL (min)\", \"PM_ST_CMPL (min)\"]):\n",
+ " df_ldst.set_index(\"Grid Points\")[pmu_counter].plot(ax=ax, legend=True);\n",
+ " ax.plot(\n",
+ " df_ldst[\"Grid Points\"], \n",
+ " linear_function(df[\"Grid Points\"], *fit_parameters[pmu_counter]), \n",
+ " linestyle=\"--\", \n",
+ " label=\"Fit: {:.2f} * x + {:.2f}\".format(*fit_parameters[pmu_counter])\n",
+ " )\n",
+ " ax.legend();"
]
},
{
@@ -1676,9 +1845,12 @@
"source": [
"Did you expect more?\n",
"\n",
- "The reason is simple: Among the load and store instructions counted by `PM_LD_CMPL` and `PM_ST_CMPL` are vector instructions which can load and store multiple (two) values at a time. To see how many *bytes* are loaded and stored, we need to measure counters for vectorized loads and stores as well.\n",
+ "The reason is simple: Among the load and store instructions counted by `PM_LD_CMPL` and `PM_ST_CMPL` are vector instructions which can load and store multiple (in this case: two) values at a time. To see how many *bytes* are loaded and stored, we need to measure counters for vectorized loads and stores as well.\n",
+ "\n",
+ "### TASK B\n",
+ "<a name=\"task2-b\"></a>\n",
"\n",
- "<a name=\"task2-b\"></a>**TASK B**: Please measure counters for _vectorized_ loads and _vectorized_ stores. See the TODOs in [`poisson2d.vld.c`](/edit/Tasks/poisson2d.vld.c) and [`poisson2d.vst.c`](/edit/Tasks/poisson2d.vst.c) (*Note: These vector counters can not be measured together and need separate files and runs*). Can you find out the name of the counters yourself, using `papi_native_avail | grep VECTOR_`?\n",
+ "Please measure counters for _vectorized_ loads and _vectorized_ stores. See the TODOs in [`poisson2d.vld.c`](poisson2d.vld.c) and [`poisson2d.vst.c`](poisson2d.vst.c) (*Note: These vector counters can not be measured together and need separate files and runs*). Can you find out the name of the counters yourself, using `papi_native_avail | grep VECTOR_`?\n",
"\n",
"Compile, test, and bench-run your program again.\n",
"\n",
@@ -1687,16 +1859,16 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "| PM_VECTOR_FLOP_CMPL |\r\n",
- "| PM_VECTOR_LD_CMPL |\r\n",
- "| PM_VECTOR_ST_CMPL |\r\n"
+ "| PM_VECTOR_FLOP_CMPL |\n",
+ "| PM_VECTOR_LD_CMPL |\n",
+ "| PM_VECTOR_ST_CMPL |\n"
]
}
],
@@ -1713,15 +1885,15 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "bsub -W 60 -nnodes 1 -Is jsrun -n 1 -c 1 -g ALL_GPUS ./bench.sh poisson2d.vld.bin /gpfs/wolf/gen110/scratch/aherten//poisson2d.vld.bin.csv\n",
- "Job <4097> is submitted to default queue <batch>.\n",
+ "bsub -W 60 -nnodes 1 -Is -P TRN003 jsrun -n 1 -c 1 -g ALL_GPUS ./bench.sh poisson2d.vld.bin /gpfs/wolf/trn003/scratch/aherten//poisson2d.vld.bin.csv\n",
+ "Job <24641> is submitted to default queue <batch>.\n",
"<<Waiting for dispatch ...>>\n",
"<<Starting on login1>>\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
@@ -1731,9 +1903,9 @@
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
"200,32,12,0.0012,174000,870,870\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,16,0.0013,234000,1170,1170\n",
+ "200,32,16,0.0012,234000,1170,1170\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,20,0.0014,294000,1470,1470\n",
+ "200,32,20,0.0013,294000,1470,1470\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
"200,32,24,0.0014,354000,1770,1770\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
@@ -1747,11 +1919,11 @@
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
"200,32,44,0.0017,654000,3270,3270\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,48,0.0017,714000,3570,3570\n",
+ "200,32,48,0.0018,714000,3570,3570\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
"200,32,52,0.0018,774000,3870,3870\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,56,0.0020,834000,4170,4170\n",
+ "200,32,56,0.0019,834000,4170,4170\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
"200,32,60,0.0020,894000,4470,4470\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
@@ -1761,123 +1933,117 @@
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
"200,32,72,0.0022,1074000,5370,5370\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,76,0.0023,1134000,5670,5670\n",
+ "200,32,76,0.0022,1134000,5670,5670\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
"200,32,80,0.0023,1194000,5970,5970\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,84,0.0023,1254000,6270,6270\n",
+ "200,32,84,0.0024,1254000,6270,6270\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
"200,32,88,0.0024,1314000,6570,6570\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
"200,32,92,0.0025,1374000,6870,6870\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,96,0.0025,1434000,7170,7170\n",
+ "200,32,96,0.0027,1434000,7170,7170\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
"200,32,100,0.0026,1494000,7470,7470\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,104,0.0027,1554000,7770,7770\n",
+ "200,32,104,0.0029,1554000,7770,7770\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
"200,32,108,0.0027,1614000,8070,8070\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
"200,32,112,0.0028,1674000,8370,8370\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,116,0.0028,1734000,8670,8670\n",
+ "200,32,116,0.0029,1734000,8670,8670\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
"200,32,120,0.0029,1794000,8970,8970\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
"200,32,124,0.0030,1854000,9270,9270\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,128,0.0030,1914000,9570,9570\n",
+ "200,32,128,0.0032,1914000,9570,9570\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
"200,32,132,0.0031,1974000,9870,9870\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
"200,32,136,0.0032,2034000,10170,10170\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,140,0.0032,2094000,10470,10470\n",
+ "200,32,140,0.0033,2094000,10470,10470\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
"200,32,144,0.0033,2154000,10770,10770\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
"200,32,148,0.0034,2214000,11070,11070\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,152,0.0035,2274000,11370,11370\n",
+ "200,32,152,0.0036,2274000,11370,11370\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
"200,32,156,0.0035,2334000,11670,11670\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
"200,32,160,0.0036,2394000,11970,11970\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,164,0.0036,2454000,12270,12270\n",
+ "200,32,164,0.0037,2454000,12270,12270\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
"200,32,168,0.0037,2514000,12570,12570\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,172,0.0037,2574000,12870,12870\n",
+ "200,32,172,0.0038,2574000,12870,12870\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,176,0.0038,2634000,13170,13170\n",
+ "200,32,176,0.0039,2634000,13170,13170\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
"200,32,180,0.0039,2694000,13470,13470\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,184,0.0041,2754000,13770,13770\n",
+ "200,32,184,0.0040,2754000,13770,13770\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,188,0.0040,2814000,14070,14070\n",
+ "200,32,188,0.0041,2814000,14070,14070\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
"200,32,192,0.0041,2874000,14370,14370\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,196,0.0041,2934000,14670,14670\n",
+ "200,32,196,0.0042,2934000,14670,14670\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
"200,32,200,0.0042,2994000,14970,14970\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
"200,32,204,0.0043,3054000,15270,15270\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,208,0.0044,3114000,15570,15570\n",
+ "200,32,208,0.0045,3114000,15570,15570\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,212,0.0044,3174000,15870,15870\n",
+ "200,32,212,0.0045,3174000,15870,15870\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,216,0.0044,3234000,16170,16170\n",
+ "200,32,216,0.0045,3234000,16170,16170\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,220,0.0045,3294000,16470,16470\n",
+ "200,32,220,0.0046,3294000,16470,16470\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,224,0.0046,3354000,16770,16770\n",
+ "200,32,224,0.0048,3354000,16770,16770\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
"200,32,228,0.0047,3414000,17070,17070\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,232,0.0047,3474000,17370,17370\n",
+ "200,32,232,0.0048,3474000,17370,17370\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
"200,32,236,0.0048,3534000,17670,17670\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,240,0.0048,3594000,17970,17970\n",
+ "200,32,240,0.0049,3594000,17970,17970\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,244,0.0049,3654000,18270,18270\n",
+ "200,32,244,0.0050,3654000,18270,18270\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,248,0.0049,3714000,18570,18570\n",
+ "200,32,248,0.0052,3714000,18570,18570\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,252,0.0050,3774000,18870,18870\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
+ "200,32,252,0.0051,3774000,18870,18870\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,256,0.0051,3834000,19170,19170\n",
+ "200,32,256,0.0052,3834000,19170,19170\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
"200,32,260,0.0052,3894000,19470,19470\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,264,0.0052,3954000,19770,19770\n",
+ "200,32,264,0.0053,3954000,19770,19770\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,268,0.0053,4014000,20070,20070\n",
+ "200,32,268,0.0054,4014000,20070,20070\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,272,0.0053,4074000,20370,20370\n",
+ "200,32,272,0.0054,4074000,20370,20370\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
"200,32,276,0.0055,4134000,20670,20670\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,280,0.0055,4194000,20970,20970\n",
+ "200,32,280,0.0056,4194000,20970,20970\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,284,0.0055,4254000,21270,21270\n",
+ "200,32,284,0.0056,4254000,21270,21270\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
"200,32,288,0.0057,4314000,21570,21570\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,292,0.0056,4374000,21870,21870\n",
+ "200,32,292,0.0058,4374000,21870,21870\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,296,0.0057,4434000,22170,22170\n",
+ "200,32,296,0.0058,4434000,22170,22170\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
"200,32,300,0.0059,4494000,22470,22470\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
@@ -1885,384 +2051,366 @@
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
"200,32,308,0.0060,4614000,23070,23070\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,312,0.0060,4674000,23370,23370\n",
+ "200,32,312,0.0061,4674000,23370,23370\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,316,0.0061,4734000,23670,23670\n",
+ "200,32,316,0.0062,4734000,23670,23670\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,320,0.0061,4794000,23970,23970\n",
+ "200,32,320,0.0062,4794000,23970,23970\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,324,0.0062,4854000,24270,24270\n",
+ "200,32,324,0.0063,4854000,24270,24270\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,328,0.0062,4914000,24570,24570\n",
+ "200,32,328,0.0063,4914000,24570,24570\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,332,0.0063,4974000,24870,24870\n",
+ "200,32,332,0.0064,4974000,24870,24870\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,336,0.0063,5034000,25170,25170\n",
+ "200,32,336,0.0065,5034000,25170,25170\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,340,0.0066,5094000,25470,25470\n",
+ "200,32,340,0.0065,5094000,25470,25470\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,344,0.0065,5154000,25770,25770\n",
+ "200,32,344,0.0066,5154000,25770,25770\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,348,0.0067,5214000,26070,26070\n",
+ "200,32,348,0.0069,5214000,26070,26070\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
"200,32,352,0.0068,5274000,26370,26370\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,356,0.0067,5334000,26670,26670\n",
+ "200,32,356,0.0070,5334000,26670,26670\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,360,0.0067,5394000,26970,26970\n",
+ "200,32,360,0.0069,5394000,26970,26970\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,364,0.0068,5454000,27270,27270\n",
+ "200,32,364,0.0070,5454000,27270,27270\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,368,0.0069,5514000,27570,27570\n",
+ "200,32,368,0.0070,5514000,27570,27570\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,372,0.0069,5574000,27870,27870\n",
+ "200,32,372,0.0071,5574000,27870,27870\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,376,0.0070,5634000,28170,28170\n",
+ "200,32,376,0.0073,5634000,28170,28170\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,380,0.0071,5694000,28470,28470\n",
+ "200,32,380,0.0073,5694000,28470,28470\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,384,0.0071,5754000,28770,28770\n",
+ "200,32,384,0.0073,5754000,28770,28770\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,388,0.0073,5814000,29070,29070\n",
+ "200,32,388,0.0074,5814000,29070,29070\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
"200,32,392,0.0074,5874000,29370,29370\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,396,0.0073,5934000,29670,29670\n",
+ "200,32,396,0.0076,5934000,29670,29670\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,400,0.0074,5994000,29970,29970\n",
+ "200,32,400,0.0075,5994000,29970,29970\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,404,0.0074,6054000,30270,30270\n",
+ "200,32,404,0.0076,6054000,30270,30270\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,408,0.0075,6114000,30570,30570\n",
+ "200,32,408,0.0077,6114000,30570,30570\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,412,0.0076,6174000,30870,30870\n",
+ "200,32,412,0.0078,6174000,30870,30870\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,416,0.0076,6234000,31170,31170\n",
+ "200,32,416,0.0079,6234000,31170,31170\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,420,0.0080,6294000,31470,31470\n",
+ "200,32,420,0.0079,6294000,31470,31470\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
"200,32,424,0.0079,6354000,31770,31770\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,428,0.0078,6414000,32070,32070\n",
+ "200,32,428,0.0080,6414000,32070,32070\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,432,0.0079,6474000,32370,32370\n",
+ "200,32,432,0.0080,6474000,32370,32370\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,436,0.0080,6534000,32670,32670\n",
+ "200,32,436,0.0081,6534000,32670,32670\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,440,0.0080,6594000,32970,32970\n",
+ "200,32,440,0.0082,6594000,32970,32970\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
"200,32,444,0.0083,6654000,33270,33270\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,448,0.0082,6714000,33570,33570\n",
+ "200,32,448,0.0084,6714000,33570,33570\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,452,0.0082,6774000,33870,33870\n",
+ "200,32,452,0.0084,6774000,33870,33870\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,456,0.0083,6834000,34170,34170\n",
+ "200,32,456,0.0084,6834000,34170,34170\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,460,0.0086,6894000,34470,34470\n",
+ "200,32,460,0.0085,6894000,34470,34470\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,464,0.0084,6954000,34770,34770\n",
+ "200,32,464,0.0086,6954000,34770,34770\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,468,0.0085,7014000,35070,35070\n",
+ "200,32,468,0.0087,7014000,35070,35070\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,472,0.0086,7074000,35370,35370\n",
+ "200,32,472,0.0088,7074000,35370,35370\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,476,0.0086,7134000,35670,35670\n",
+ "200,32,476,0.0088,7134000,35670,35670\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,480,0.0087,7194000,35970,35970\n",
+ "200,32,480,0.0089,7194000,35970,35970\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,484,0.0088,7254000,36270,36270\n",
+ "200,32,484,0.0090,7254000,36270,36270\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,488,0.0088,7314000,36570,36570\n",
+ "200,32,488,0.0091,7314000,36570,36570\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,492,0.0089,7374000,36870,36870\n",
+ "200,32,492,0.0091,7374000,36870,36870\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
"200,32,496,0.0091,7434000,37170,37170\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,500,0.0092,7494000,37470,37470\n",
+ "200,32,500,0.0094,7494000,37470,37470\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,504,0.0091,7554000,37770,37770\n",
+ "200,32,504,0.0093,7554000,37770,37770\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,508,0.0092,7614000,38070,38070\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
+ "200,32,508,0.0095,7614000,38070,38070\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,512,0.0092,7674000,38370,38370\n",
+ "200,32,512,0.0096,7674000,38370,38370\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,516,0.0093,7734000,38670,38670\n",
+ "200,32,516,0.0095,7734000,38670,38670\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,520,0.0093,7794000,38970,38970\n",
+ "200,32,520,0.0095,7794000,38970,38970\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,524,0.0094,7854000,39270,39270\n",
+ "200,32,524,0.0097,7854000,39270,39270\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
"200,32,528,0.0097,7914000,39570,39570\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,532,0.0095,7974000,39870,39870\n",
+ "200,32,532,0.0098,7974000,39870,39870\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,536,0.0096,8034000,40170,40170\n",
+ "200,32,536,0.0098,8034000,40170,40170\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,540,0.0097,8094000,40470,40470\n",
+ "200,32,540,0.0099,8094000,40470,40470\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,544,0.0097,8154000,40770,40770\n",
+ "200,32,544,0.0100,8154000,40770,40770\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,548,0.0099,8214000,41070,41070\n",
+ "200,32,548,0.0101,8214000,41070,41070\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,552,0.0099,8274000,41370,41370\n",
+ "200,32,552,0.0101,8274000,41370,41370\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,556,0.0100,8334000,41670,41670\n",
+ "200,32,556,0.0104,8334000,41670,41670\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,560,0.0100,8394000,41970,41970\n",
+ "200,32,560,0.0103,8394000,41970,41970\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,564,0.0101,8454000,42270,42270\n",
+ "200,32,564,0.0103,8454000,42270,42270\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,568,0.0102,8514000,42570,42570\n",
+ "200,32,568,0.0106,8514000,42570,42570\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,572,0.0103,8574000,42870,42870\n",
+ "200,32,572,0.0105,8574000,42870,42870\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,576,0.0103,8634000,43170,43170\n",
+ "200,32,576,0.0106,8634000,43170,43170\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,580,0.0104,8694000,43470,43470\n",
+ "200,32,580,0.0108,8694000,43470,43470\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,584,0.0104,8754000,43770,43770\n",
+ "200,32,584,0.0109,8754000,43770,43770\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,588,0.0106,8814000,44070,44070\n",
+ "200,32,588,0.0108,8814000,44070,44070\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,592,0.0106,8874000,44370,44370\n",
+ "200,32,592,0.0109,8874000,44370,44370\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,596,0.0107,8934000,44670,44670\n",
+ "200,32,596,0.0109,8934000,44670,44670\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,600,0.0107,8994000,44970,44970\n",
+ "200,32,600,0.0110,8994000,44970,44970\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,604,0.0109,9054000,45270,45270\n",
+ "200,32,604,0.0111,9054000,45270,45270\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,608,0.0109,9114000,45570,45570\n",
+ "200,32,608,0.0112,9114000,45570,45570\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,612,0.0110,9174000,45870,45870\n",
+ "200,32,612,0.0112,9174000,45870,45870\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,616,0.0110,9234000,46170,46170\n",
+ "200,32,616,0.0114,9234000,46170,46170\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,620,0.0111,9294000,46470,46470\n",
+ "200,32,620,0.0113,9294000,46470,46470\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,624,0.0112,9354000,46770,46770\n",
+ "200,32,624,0.0114,9354000,46770,46770\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,628,0.0112,9414000,47070,47070\n",
+ "200,32,628,0.0117,9414000,47070,47070\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,632,0.0113,9474000,47370,47370\n",
+ "200,32,632,0.0116,9474000,47370,47370\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,636,0.0114,9534000,47670,47670\n",
+ "200,32,636,0.0116,9534000,47670,47670\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,640,0.0115,9594000,47970,47970\n",
+ "200,32,640,0.0117,9594000,47970,47970\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,644,0.0115,9654000,48270,48270\n",
+ "200,32,644,0.0119,9654000,48270,48270\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,648,0.0115,9714000,48570,48570\n",
+ "200,32,648,0.0118,9714000,48570,48570\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,652,0.0116,9774000,48870,48870\n",
+ "200,32,652,0.0119,9774000,48870,48870\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,656,0.0118,9834000,49170,49170\n",
+ "200,32,656,0.0119,9834000,49170,49170\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,660,0.0117,9894000,49470,49470\n",
+ "200,32,660,0.0121,9894000,49470,49470\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,664,0.0118,9954000,49770,49770\n",
+ "200,32,664,0.0122,9954000,49770,49770\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,668,0.0118,10014000,50070,50070\n",
+ "200,32,668,0.0123,10014000,50070,50070\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,672,0.0120,10074000,50370,50370\n",
+ "200,32,672,0.0122,10074000,50370,50370\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,676,0.0121,10134000,50670,50670\n",
+ "200,32,676,0.0123,10134000,50670,50670\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,680,0.0120,10194000,50970,50970\n",
+ "200,32,680,0.0123,10194000,50970,50970\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,684,0.0121,10254000,51270,51270\n",
+ "200,32,684,0.0125,10254000,51270,51270\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,688,0.0123,10314000,51570,51570\n",
+ "200,32,688,0.0125,10314000,51570,51570\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,692,0.0122,10374000,51870,51870\n",
+ "200,32,692,0.0127,10374000,51870,51870\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,696,0.0123,10434000,52170,52170\n",
+ "200,32,696,0.0126,10434000,52170,52170\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,700,0.0124,10494000,52470,52470\n",
+ "200,32,700,0.0127,10494000,52470,52470\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,704,0.0124,10554000,52770,52770\n",
+ "200,32,704,0.0128,10554000,52770,52770\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,708,0.0125,10614000,53070,53070\n",
+ "200,32,708,0.0129,10614000,53070,53070\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,712,0.0126,10674000,53370,53370\n",
+ "200,32,712,0.0128,10674000,53370,53370\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,716,0.0126,10734000,53670,53670\n",
+ "200,32,716,0.0131,10734000,53670,53670\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,720,0.0126,10794000,53970,53970\n",
+ "200,32,720,0.0130,10794000,53970,53970\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,724,0.0128,10854000,54270,54270\n",
+ "200,32,724,0.0130,10854000,54270,54270\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,728,0.0128,10914000,54570,54570\n",
+ "200,32,728,0.0132,10914000,54570,54570\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,732,0.0129,10974000,54870,54870\n",
+ "200,32,732,0.0133,10974000,54870,54870\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,736,0.0130,11034000,55170,55170\n",
+ "200,32,736,0.0135,11034000,55170,55170\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,740,0.0130,11094000,55470,55470\n",
+ "200,32,740,0.0135,11094000,55470,55470\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,744,0.0130,11154000,55770,55770\n",
+ "200,32,744,0.0135,11154000,55770,55770\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,748,0.0131,11214000,56070,56070\n",
+ "200,32,748,0.0134,11214000,56070,56070\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,752,0.0132,11274000,56370,56370\n",
+ "200,32,752,0.0135,11274000,56370,56370\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,756,0.0133,11334000,56670,56670\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
+ "200,32,756,0.0136,11334000,56670,56670\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,760,0.0134,11394000,56970,56970\n",
+ "200,32,760,0.0137,11394000,56970,56970\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,764,0.0134,11454000,57270,57270\n",
+ "200,32,764,0.0137,11454000,57270,57270\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,768,0.0135,11514000,57570,57570\n",
+ "200,32,768,0.0138,11514000,57570,57570\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,772,0.0135,11574000,57870,57870\n",
+ "200,32,772,0.0139,11574000,57870,57870\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,776,0.0136,11634000,58170,58170\n",
+ "200,32,776,0.0141,11634000,58170,58170\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,780,0.0138,11694000,58470,58470\n",
+ "200,32,780,0.0140,11694000,58470,58470\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,784,0.0138,11754000,58770,58770\n",
+ "200,32,784,0.0142,11754000,58770,58770\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,788,0.0139,11814000,59070,59070\n",
+ "200,32,788,0.0141,11814000,59070,59070\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,792,0.0139,11874000,59370,59370\n",
+ "200,32,792,0.0142,11874000,59370,59370\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,796,0.0141,11934000,59670,59670\n",
+ "200,32,796,0.0143,11934000,59670,59670\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,800,0.0140,11994000,59970,59970\n",
+ "200,32,800,0.0143,11994000,59970,59970\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,804,0.0141,12054000,60270,60270\n",
+ "200,32,804,0.0145,12054000,60270,60270\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,808,0.0142,12114000,60570,60570\n",
+ "200,32,808,0.0145,12114000,60570,60570\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,812,0.0143,12174000,60870,60870\n",
+ "200,32,812,0.0145,12174000,60870,60870\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,816,0.0143,12234000,61170,61170\n",
+ "200,32,816,0.0148,12234000,61170,61170\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,820,0.0143,12294000,61470,61470\n",
+ "200,32,820,0.0148,12294000,61470,61470\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,824,0.0144,12354000,61770,61770\n",
+ "200,32,824,0.0148,12354000,61770,61770\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,828,0.0145,12414000,62070,62070\n",
+ "200,32,828,0.0148,12414000,62070,62070\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,832,0.0145,12474000,62370,62370\n",
+ "200,32,832,0.0149,12474000,62370,62370\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,836,0.0146,12534000,62670,62670\n",
+ "200,32,836,0.0150,12534000,62670,62670\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,840,0.0146,12594000,62970,62970\n",
+ "200,32,840,0.0150,12594000,62970,62970\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,844,0.0147,12654000,63270,63270\n",
+ "200,32,844,0.0151,12654000,63270,63270\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,848,0.0148,12714000,63570,63570\n",
+ "200,32,848,0.0153,12714000,63570,63570\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,852,0.0149,12774000,63870,63870\n",
+ "200,32,852,0.0153,12774000,63870,63870\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,856,0.0150,12834000,64170,64170\n",
+ "200,32,856,0.0153,12834000,64170,64170\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,860,0.0150,12894000,64470,64470\n",
+ "200,32,860,0.0154,12894000,64470,64470\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,864,0.0151,12954000,64770,64770\n",
+ "200,32,864,0.0154,12954000,64770,64770\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,868,0.0152,13014000,65070,65070\n",
+ "200,32,868,0.0155,13014000,65070,65070\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,872,0.0151,13074000,65370,65370\n",
+ "200,32,872,0.0157,13074000,65370,65370\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,876,0.0152,13134000,65670,65670\n",
+ "200,32,876,0.0156,13134000,65670,65670\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,880,0.0154,13194000,65970,65970\n",
+ "200,32,880,0.0157,13194000,65970,65970\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,884,0.0154,13254000,66270,66270\n",
+ "200,32,884,0.0157,13254000,66270,66270\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,888,0.0154,13314000,66570,66570\n",
+ "200,32,888,0.0158,13314000,66570,66570\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,892,0.0155,13374000,66870,66870\n",
+ "200,32,892,0.0159,13374000,66870,66870\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,896,0.0156,13434000,67170,67170\n",
+ "200,32,896,0.0160,13434000,67170,67170\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,900,0.0158,13494000,67470,67470\n",
+ "200,32,900,0.0160,13494000,67470,67470\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,904,0.0158,13554000,67770,67770\n",
+ "200,32,904,0.0162,13554000,67770,67770\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,908,0.0159,13614000,68070,68070\n",
+ "200,32,908,0.0162,13614000,68070,68070\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,912,0.0161,13674000,68370,68370\n",
+ "200,32,912,0.0163,13674000,68370,68370\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,916,0.0162,13734000,68670,68670\n",
+ "200,32,916,0.0163,13734000,68670,68670\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,920,0.0162,13794000,68970,68970\n",
+ "200,32,920,0.0164,13794000,68970,68970\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,924,0.0163,13854000,69270,69270\n",
+ "200,32,924,0.0165,13854000,69270,69270\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,928,0.0162,13914000,69570,69570\n",
+ "200,32,928,0.0166,13914000,69570,69570\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,932,0.0164,13974000,69870,69870\n",
+ "200,32,932,0.0166,13974000,69870,69870\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,936,0.0163,14034000,70170,70170\n",
+ "200,32,936,0.0167,14034000,70170,70170\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,940,0.0164,14094000,70470,70470\n",
+ "200,32,940,0.0167,14094000,70470,70470\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,944,0.0165,14154000,70770,70770\n",
+ "200,32,944,0.0168,14154000,70770,70770\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,948,0.0166,14214000,71070,71070\n",
+ "200,32,948,0.0170,14214000,71070,71070\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,952,0.0166,14274000,71370,71370\n",
+ "200,32,952,0.0171,14274000,71370,71370\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,956,0.0170,14334000,71670,71670\n",
+ "200,32,956,0.0171,14334000,71670,71670\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,960,0.0168,14394000,71970,71970\n",
+ "200,32,960,0.0171,14394000,71970,71970\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,964,0.0174,14454000,72270,72270\n",
+ "200,32,964,0.0175,14454000,72270,72270\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,968,0.0172,14514000,72570,72570\n",
+ "200,32,968,0.0176,14514000,72570,72570\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,972,0.0173,14574000,72870,72870\n",
+ "200,32,972,0.0176,14574000,72870,72870\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,976,0.0173,14634000,73170,73170\n",
+ "200,32,976,0.0175,14634000,73170,73170\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,980,0.0175,14694000,73470,73470\n",
+ "200,32,980,0.0178,14694000,73470,73470\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,984,0.0175,14754000,73770,73770\n",
+ "200,32,984,0.0180,14754000,73770,73770\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,988,0.0176,14814000,74070,74070\n",
+ "200,32,988,0.0178,14814000,74070,74070\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,992,0.0176,14874000,74370,74370\n",
+ "200,32,992,0.0179,14874000,74370,74370\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,996,0.0178,14934000,74670,74670\n",
+ "200,32,996,0.0181,14934000,74670,74670\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,1000,0.0179,14994000,74970,74970\n",
+ "200,32,1000,0.0180,14994000,74970,74970\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,1004,0.0178,15054000,75270,75270\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
+ "200,32,1004,0.0182,15054000,75270,75270\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,1008,0.0179,15114000,75570,75570\n",
+ "200,32,1008,0.0181,15114000,75570,75570\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,1012,0.0179,15174000,75870,75870\n",
+ "200,32,1012,0.0183,15174000,75870,75870\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,1016,0.0181,15234000,76170,76170\n",
+ "200,32,1016,0.0183,15234000,76170,76170\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,1020,0.0181,15294000,76470,76470\n",
+ "200,32,1020,0.0186,15294000,76470,76470\n",
"iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,1024,0.0179,15354000,76770,76770\n",
- "mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.vld.bin.csv .\n",
- "bsub -W 60 -nnodes 1 -Is jsrun -n 1 -c 1 -g ALL_GPUS ./bench.sh poisson2d.vst.bin /gpfs/wolf/gen110/scratch/aherten//poisson2d.vst.bin.csv\n",
- "Job <4098> is submitted to default queue <batch>.\n",
+ "200,32,1024,0.0182,15354000,76770,76770\n",
+ "mv /gpfs/wolf/trn003/scratch/aherten//poisson2d.vld.bin.csv .\n",
+ "bsub -W 60 -nnodes 1 -Is -P TRN003 jsrun -n 1 -c 1 -g ALL_GPUS ./bench.sh poisson2d.vst.bin /gpfs/wolf/trn003/scratch/aherten//poisson2d.vst.bin.csv\n",
+ "Job <24642> is submitted to default queue <batch>.\n",
"<<Waiting for dispatch ...>>\n",
"<<Starting on login1>>\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
@@ -2276,11 +2424,11 @@
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
"200,32,20,0.0013,54200,271,271\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,24,0.0014,66200,331,331\n",
+ "200,32,24,0.0013,66200,331,331\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
"200,32,28,0.0014,78200,391,391\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,32,0.0016,90200,451,451\n",
+ "200,32,32,0.0015,90200,451,451\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
"200,32,36,0.0015,102200,511,511\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
@@ -2296,115 +2444,109 @@
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
"200,32,60,0.0020,174200,871,871\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,64,0.0022,186200,931,931\n",
+ "200,32,64,0.0020,186200,931,931\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
"200,32,68,0.0022,198200,991,991\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,72,0.0021,210200,1051,1051\n",
+ "200,32,72,0.0023,210200,1051,1051\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,76,0.0023,222200,1111,1111\n",
+ "200,32,76,0.0022,222200,1111,1111\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
"200,32,80,0.0023,234200,1171,1171\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,84,0.0023,246200,1231,1231\n",
+ "200,32,84,0.0024,246200,1231,1231\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
"200,32,88,0.0024,258200,1291,1291\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
"200,32,92,0.0025,270200,1351,1351\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,96,0.0027,282200,1411,1411\n",
+ "200,32,96,0.0025,282200,1411,1411\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
"200,32,100,0.0026,294200,1471,1471\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
"200,32,104,0.0027,306200,1531,1531\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,108,0.0027,318200,1591,1591\n",
+ "200,32,108,0.0028,318200,1591,1591\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
"200,32,112,0.0028,330200,1651,1651\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,116,0.0028,342200,1711,1711\n",
+ "200,32,116,0.0029,342200,1711,1711\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
"200,32,120,0.0030,354200,1771,1771\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
"200,32,124,0.0030,366200,1831,1831\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,128,0.0030,378200,1891,1891\n",
+ "200,32,128,0.0031,378200,1891,1891\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
"200,32,132,0.0032,390200,1951,1951\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
"200,32,136,0.0032,402200,2011,2011\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,140,0.0032,414200,2071,2071\n",
+ "200,32,140,0.0033,414200,2071,2071\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
"200,32,144,0.0033,426200,2131,2131\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,148,0.0033,438200,2191,2191\n",
+ "200,32,148,0.0035,438200,2191,2191\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,152,0.0034,450200,2251,2251\n",
+ "200,32,152,0.0035,450200,2251,2251\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
"200,32,156,0.0035,462200,2311,2311\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
"200,32,160,0.0036,474200,2371,2371\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,164,0.0036,486200,2431,2431\n",
+ "200,32,164,0.0038,486200,2431,2431\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
"200,32,168,0.0037,498200,2491,2491\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,172,0.0037,510200,2551,2551\n",
+ "200,32,172,0.0038,510200,2551,2551\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,176,0.0039,522200,2611,2611\n",
+ "200,32,176,0.0038,522200,2611,2611\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
"200,32,180,0.0039,534200,2671,2671\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,184,0.0039,546200,2731,2731\n",
+ "200,32,184,0.0040,546200,2731,2731\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
"200,32,188,0.0040,558200,2791,2791\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,192,0.0040,570200,2851,2851\n",
+ "200,32,192,0.0041,570200,2851,2851\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,196,0.0041,582200,2911,2911\n",
+ "200,32,196,0.0042,582200,2911,2911\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,200,0.0042,594200,2971,2971\n",
+ "200,32,200,0.0044,594200,2971,2971\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,204,0.0042,606200,3031,3031\n",
+ "200,32,204,0.0043,606200,3031,3031\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,208,0.0043,618200,3091,3091\n",
+ "200,32,208,0.0044,618200,3091,3091\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
"200,32,212,0.0044,630200,3151,3151\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,216,0.0044,642200,3211,3211\n",
+ "200,32,216,0.0045,642200,3211,3211\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
"200,32,220,0.0046,654200,3271,3271\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
"200,32,224,0.0046,666200,3331,3331\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,228,0.0046,678200,3391,3391\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
+ "200,32,228,0.0047,678200,3391,3391\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,232,0.0047,690200,3451,3451\n",
+ "200,32,232,0.0048,690200,3451,3451\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,236,0.0047,702200,3511,3511\n",
+ "200,32,236,0.0048,702200,3511,3511\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,240,0.0048,714200,3571,3571\n",
+ "200,32,240,0.0049,714200,3571,3571\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,244,0.0049,726200,3631,3631\n",
+ "200,32,244,0.0050,726200,3631,3631\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,248,0.0049,738200,3691,3691\n",
+ "200,32,248,0.0050,738200,3691,3691\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,252,0.0050,750200,3751,3751\n",
+ "200,32,252,0.0051,750200,3751,3751\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,256,0.0051,762200,3811,3811\n",
+ "200,32,256,0.0052,762200,3811,3811\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,260,0.0051,774200,3871,3871\n",
+ "200,32,260,0.0052,774200,3871,3871\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
"200,32,264,0.0053,786200,3931,3931\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,268,0.0053,798200,3991,3991\n",
+ "200,32,268,0.0054,798200,3991,3991\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
"200,32,272,0.0054,810200,4051,4051\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
@@ -2412,396 +2554,378 @@
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
"200,32,280,0.0055,834200,4171,4171\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,284,0.0055,846200,4231,4231\n",
+ "200,32,284,0.0056,846200,4231,4231\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,288,0.0056,858200,4291,4291\n",
+ "200,32,288,0.0057,858200,4291,4291\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
"200,32,292,0.0057,870200,4351,4351\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,296,0.0057,882200,4411,4411\n",
+ "200,32,296,0.0058,882200,4411,4411\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,300,0.0058,894200,4471,4471\n",
+ "200,32,300,0.0059,894200,4471,4471\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,304,0.0058,906200,4531,4531\n",
+ "200,32,304,0.0059,906200,4531,4531\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,308,0.0059,918200,4591,4591\n",
+ "200,32,308,0.0060,918200,4591,4591\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,312,0.0060,930200,4651,4651\n",
+ "200,32,312,0.0061,930200,4651,4651\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,316,0.0060,942200,4711,4711\n",
+ "200,32,316,0.0061,942200,4711,4711\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,320,0.0061,954200,4771,4771\n",
+ "200,32,320,0.0062,954200,4771,4771\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,324,0.0061,966200,4831,4831\n",
+ "200,32,324,0.0063,966200,4831,4831\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,328,0.0062,978200,4891,4891\n",
+ "200,32,328,0.0063,978200,4891,4891\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,332,0.0063,990200,4951,4951\n",
+ "200,32,332,0.0064,990200,4951,4951\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,336,0.0063,1002200,5011,5011\n",
+ "200,32,336,0.0065,1002200,5011,5011\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,340,0.0064,1014200,5071,5071\n",
+ "200,32,340,0.0066,1014200,5071,5071\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,344,0.0065,1026200,5131,5131\n",
+ "200,32,344,0.0066,1026200,5131,5131\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,348,0.0066,1038200,5191,5191\n",
+ "200,32,348,0.0067,1038200,5191,5191\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,352,0.0066,1050200,5251,5251\n",
+ "200,32,352,0.0069,1050200,5251,5251\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,356,0.0067,1062200,5311,5311\n",
+ "200,32,356,0.0068,1062200,5311,5311\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,360,0.0067,1074200,5371,5371\n",
+ "200,32,360,0.0068,1074200,5371,5371\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,364,0.0068,1086200,5431,5431\n",
+ "200,32,364,0.0069,1086200,5431,5431\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,368,0.0068,1098200,5491,5491\n",
+ "200,32,368,0.0070,1098200,5491,5491\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,372,0.0069,1110200,5551,5551\n",
+ "200,32,372,0.0071,1110200,5551,5551\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,376,0.0070,1122200,5611,5611\n",
+ "200,32,376,0.0071,1122200,5611,5611\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,380,0.0071,1134200,5671,5671\n",
+ "200,32,380,0.0072,1134200,5671,5671\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,384,0.0072,1146200,5731,5731\n",
+ "200,32,384,0.0073,1146200,5731,5731\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,388,0.0072,1158200,5791,5791\n",
+ "200,32,388,0.0073,1158200,5791,5791\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,392,0.0072,1170200,5851,5851\n",
+ "200,32,392,0.0074,1170200,5851,5851\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,396,0.0073,1182200,5911,5911\n",
+ "200,32,396,0.0075,1182200,5911,5911\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,400,0.0074,1194200,5971,5971\n",
+ "200,32,400,0.0075,1194200,5971,5971\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,404,0.0074,1206200,6031,6031\n",
+ "200,32,404,0.0076,1206200,6031,6031\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,408,0.0076,1218200,6091,6091\n",
+ "200,32,408,0.0077,1218200,6091,6091\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,412,0.0076,1230200,6151,6151\n",
+ "200,32,412,0.0077,1230200,6151,6151\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,416,0.0077,1242200,6211,6211\n",
+ "200,32,416,0.0080,1242200,6211,6211\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,420,0.0077,1254200,6271,6271\n",
+ "200,32,420,0.0078,1254200,6271,6271\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,424,0.0078,1266200,6331,6331\n",
+ "200,32,424,0.0079,1266200,6331,6331\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,428,0.0078,1278200,6391,6391\n",
+ "200,32,428,0.0080,1278200,6391,6391\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,432,0.0080,1290200,6451,6451\n",
+ "200,32,432,0.0081,1290200,6451,6451\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,436,0.0079,1302200,6511,6511\n",
+ "200,32,436,0.0082,1302200,6511,6511\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,440,0.0081,1314200,6571,6571\n",
+ "200,32,440,0.0082,1314200,6571,6571\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,444,0.0081,1326200,6631,6631\n",
+ "200,32,444,0.0083,1326200,6631,6631\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,448,0.0082,1338200,6691,6691\n",
+ "200,32,448,0.0083,1338200,6691,6691\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,452,0.0082,1350200,6751,6751\n",
+ "200,32,452,0.0084,1350200,6751,6751\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,456,0.0084,1362200,6811,6811\n",
+ "200,32,456,0.0085,1362200,6811,6811\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,460,0.0084,1374200,6871,6871\n",
+ "200,32,460,0.0085,1374200,6871,6871\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,464,0.0084,1386200,6931,6931\n",
+ "200,32,464,0.0087,1386200,6931,6931\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,468,0.0085,1398200,6991,6991\n",
+ "200,32,468,0.0086,1398200,6991,6991\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,472,0.0085,1410200,7051,7051\n",
+ "200,32,472,0.0087,1410200,7051,7051\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,476,0.0086,1422200,7111,7111\n",
+ "200,32,476,0.0088,1422200,7111,7111\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,480,0.0087,1434200,7171,7171\n",
+ "200,32,480,0.0090,1434200,7171,7171\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,484,0.0088,1446200,7231,7231\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
+ "200,32,484,0.0089,1446200,7231,7231\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,488,0.0088,1458200,7291,7291\n",
+ "200,32,488,0.0090,1458200,7291,7291\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,492,0.0089,1470200,7351,7351\n",
+ "200,32,492,0.0092,1470200,7351,7351\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,496,0.0089,1482200,7411,7411\n",
+ "200,32,496,0.0092,1482200,7411,7411\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,500,0.0090,1494200,7471,7471\n",
+ "200,32,500,0.0092,1494200,7471,7471\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,504,0.0092,1506200,7531,7531\n",
+ "200,32,504,0.0093,1506200,7531,7531\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,508,0.0093,1518200,7591,7591\n",
+ "200,32,508,0.0094,1518200,7591,7591\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,512,0.0092,1530200,7651,7651\n",
+ "200,32,512,0.0095,1530200,7651,7651\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,516,0.0093,1542200,7711,7711\n",
+ "200,32,516,0.0096,1542200,7711,7711\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,520,0.0094,1554200,7771,7771\n",
+ "200,32,520,0.0096,1554200,7771,7771\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,524,0.0094,1566200,7831,7831\n",
+ "200,32,524,0.0096,1566200,7831,7831\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,528,0.0094,1578200,7891,7891\n",
+ "200,32,528,0.0097,1578200,7891,7891\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
"200,32,532,0.0097,1590200,7951,7951\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,536,0.0096,1602200,8011,8011\n",
+ "200,32,536,0.0098,1602200,8011,8011\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,540,0.0097,1614200,8071,8071\n",
+ "200,32,540,0.0100,1614200,8071,8071\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,544,0.0097,1626200,8131,8131\n",
+ "200,32,544,0.0099,1626200,8131,8131\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,548,0.0099,1638200,8191,8191\n",
+ "200,32,548,0.0100,1638200,8191,8191\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,552,0.0099,1650200,8251,8251\n",
+ "200,32,552,0.0101,1650200,8251,8251\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,556,0.0101,1662200,8311,8311\n",
+ "200,32,556,0.0102,1662200,8311,8311\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,560,0.0100,1674200,8371,8371\n",
+ "200,32,560,0.0102,1674200,8371,8371\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,564,0.0101,1686200,8431,8431\n",
+ "200,32,564,0.0105,1686200,8431,8431\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,568,0.0102,1698200,8491,8491\n",
+ "200,32,568,0.0104,1698200,8491,8491\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,572,0.0103,1710200,8551,8551\n",
+ "200,32,572,0.0105,1710200,8551,8551\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,576,0.0103,1722200,8611,8611\n",
+ "200,32,576,0.0105,1722200,8611,8611\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,580,0.0104,1734200,8671,8671\n",
+ "200,32,580,0.0108,1734200,8671,8671\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,584,0.0104,1746200,8731,8731\n",
+ "200,32,584,0.0108,1746200,8731,8731\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,588,0.0105,1758200,8791,8791\n",
+ "200,32,588,0.0109,1758200,8791,8791\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,592,0.0107,1770200,8851,8851\n",
+ "200,32,592,0.0109,1770200,8851,8851\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,596,0.0108,1782200,8911,8911\n",
+ "200,32,596,0.0109,1782200,8911,8911\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,600,0.0107,1794200,8971,8971\n",
+ "200,32,600,0.0111,1794200,8971,8971\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,604,0.0109,1806200,9031,9031\n",
+ "200,32,604,0.0111,1806200,9031,9031\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,608,0.0109,1818200,9091,9091\n",
+ "200,32,608,0.0112,1818200,9091,9091\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,612,0.0109,1830200,9151,9151\n",
+ "200,32,612,0.0112,1830200,9151,9151\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,616,0.0110,1842200,9211,9211\n",
+ "200,32,616,0.0114,1842200,9211,9211\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,620,0.0111,1854200,9271,9271\n",
+ "200,32,620,0.0113,1854200,9271,9271\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,624,0.0112,1866200,9331,9331\n",
+ "200,32,624,0.0114,1866200,9331,9331\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,628,0.0111,1878200,9391,9391\n",
+ "200,32,628,0.0114,1878200,9391,9391\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,632,0.0112,1890200,9451,9451\n",
+ "200,32,632,0.0116,1890200,9451,9451\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,636,0.0113,1902200,9511,9511\n",
+ "200,32,636,0.0116,1902200,9511,9511\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,640,0.0116,1914200,9571,9571\n",
+ "200,32,640,0.0117,1914200,9571,9571\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,644,0.0114,1926200,9631,9631\n",
+ "200,32,644,0.0118,1926200,9631,9631\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,648,0.0115,1938200,9691,9691\n",
+ "200,32,648,0.0118,1938200,9691,9691\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,652,0.0117,1950200,9751,9751\n",
+ "200,32,652,0.0121,1950200,9751,9751\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,656,0.0117,1962200,9811,9811\n",
+ "200,32,656,0.0121,1962200,9811,9811\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,660,0.0117,1974200,9871,9871\n",
+ "200,32,660,0.0121,1974200,9871,9871\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,664,0.0118,1986200,9931,9931\n",
+ "200,32,664,0.0121,1986200,9931,9931\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,668,0.0119,1998200,9991,9991\n",
+ "200,32,668,0.0122,1998200,9991,9991\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,672,0.0120,2010200,10051,10051\n",
+ "200,32,672,0.0122,2010200,10051,10051\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,676,0.0120,2022200,10111,10111\n",
+ "200,32,676,0.0124,2022200,10111,10111\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,680,0.0120,2034200,10171,10171\n",
+ "200,32,680,0.0123,2034200,10171,10171\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,684,0.0121,2046200,10231,10231\n",
+ "200,32,684,0.0124,2046200,10231,10231\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,688,0.0122,2058200,10291,10291\n",
+ "200,32,688,0.0126,2058200,10291,10291\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,692,0.0123,2070200,10351,10351\n",
+ "200,32,692,0.0127,2070200,10351,10351\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,696,0.0124,2082200,10411,10411\n",
+ "200,32,696,0.0126,2082200,10411,10411\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,700,0.0124,2094200,10471,10471\n",
+ "200,32,700,0.0128,2094200,10471,10471\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,704,0.0125,2106200,10531,10531\n",
+ "200,32,704,0.0127,2106200,10531,10531\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,708,0.0125,2118200,10591,10591\n",
+ "200,32,708,0.0128,2118200,10591,10591\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,712,0.0125,2130200,10651,10651\n",
+ "200,32,712,0.0129,2130200,10651,10651\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,716,0.0125,2142200,10711,10711\n",
+ "200,32,716,0.0130,2142200,10711,10711\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,720,0.0126,2154200,10771,10771\n",
+ "200,32,720,0.0130,2154200,10771,10771\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,724,0.0127,2166200,10831,10831\n",
+ "200,32,724,0.0131,2166200,10831,10831\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,728,0.0128,2178200,10891,10891\n",
+ "200,32,728,0.0131,2178200,10891,10891\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,732,0.0128,2190200,10951,10951\n",
+ "200,32,732,0.0132,2190200,10951,10951\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,736,0.0130,2202200,11011,11011\n",
+ "200,32,736,0.0134,2202200,11011,11011\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,740,0.0130,2214200,11071,11071\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
+ "200,32,740,0.0134,2214200,11071,11071\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,744,0.0130,2226200,11131,11131\n",
+ "200,32,744,0.0134,2226200,11131,11131\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,748,0.0131,2238200,11191,11191\n",
+ "200,32,748,0.0135,2238200,11191,11191\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,752,0.0133,2250200,11251,11251\n",
+ "200,32,752,0.0136,2250200,11251,11251\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,756,0.0133,2262200,11311,11311\n",
+ "200,32,756,0.0136,2262200,11311,11311\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,760,0.0133,2274200,11371,11371\n",
+ "200,32,760,0.0137,2274200,11371,11371\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,764,0.0134,2286200,11431,11431\n",
+ "200,32,764,0.0138,2286200,11431,11431\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,768,0.0135,2298200,11491,11491\n",
+ "200,32,768,0.0138,2298200,11491,11491\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,772,0.0137,2310200,11551,11551\n",
+ "200,32,772,0.0139,2310200,11551,11551\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,776,0.0136,2322200,11611,11611\n",
+ "200,32,776,0.0139,2322200,11611,11611\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,780,0.0137,2334200,11671,11671\n",
+ "200,32,780,0.0140,2334200,11671,11671\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,784,0.0137,2346200,11731,11731\n",
+ "200,32,784,0.0141,2346200,11731,11731\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,788,0.0138,2358200,11791,11791\n",
+ "200,32,788,0.0142,2358200,11791,11791\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,792,0.0139,2370200,11851,11851\n",
+ "200,32,792,0.0142,2370200,11851,11851\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,796,0.0140,2382200,11911,11911\n",
+ "200,32,796,0.0144,2382200,11911,11911\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,800,0.0140,2394200,11971,11971\n",
+ "200,32,800,0.0144,2394200,11971,11971\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,804,0.0141,2406200,12031,12031\n",
+ "200,32,804,0.0144,2406200,12031,12031\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,808,0.0143,2418200,12091,12091\n",
+ "200,32,808,0.0146,2418200,12091,12091\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,812,0.0142,2430200,12151,12151\n",
+ "200,32,812,0.0146,2430200,12151,12151\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,816,0.0143,2442200,12211,12211\n",
+ "200,32,816,0.0146,2442200,12211,12211\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,820,0.0144,2454200,12271,12271\n",
+ "200,32,820,0.0147,2454200,12271,12271\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,824,0.0144,2466200,12331,12331\n",
+ "200,32,824,0.0148,2466200,12331,12331\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,828,0.0145,2478200,12391,12391\n",
+ "200,32,828,0.0149,2478200,12391,12391\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,832,0.0146,2490200,12451,12451\n",
+ "200,32,832,0.0149,2490200,12451,12451\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,836,0.0146,2502200,12511,12511\n",
+ "200,32,836,0.0150,2502200,12511,12511\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,840,0.0147,2514200,12571,12571\n",
+ "200,32,840,0.0151,2514200,12571,12571\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,844,0.0148,2526200,12631,12631\n",
+ "200,32,844,0.0152,2526200,12631,12631\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,848,0.0149,2538200,12691,12691\n",
+ "200,32,848,0.0151,2538200,12691,12691\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,852,0.0149,2550200,12751,12751\n",
+ "200,32,852,0.0152,2550200,12751,12751\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,856,0.0150,2562200,12811,12811\n",
+ "200,32,856,0.0153,2562200,12811,12811\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,860,0.0152,2574200,12871,12871\n",
+ "200,32,860,0.0154,2574200,12871,12871\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,864,0.0151,2586200,12931,12931\n",
+ "200,32,864,0.0155,2586200,12931,12931\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,868,0.0151,2598200,12991,12991\n",
+ "200,32,868,0.0155,2598200,12991,12991\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,872,0.0151,2610200,13051,13051\n",
+ "200,32,872,0.0156,2610200,13051,13051\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,876,0.0152,2622200,13111,13111\n",
+ "200,32,876,0.0156,2622200,13111,13111\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,880,0.0155,2634200,13171,13171\n",
+ "200,32,880,0.0157,2634200,13171,13171\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,884,0.0154,2646200,13231,13231\n",
+ "200,32,884,0.0158,2646200,13231,13231\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,888,0.0155,2658200,13291,13291\n",
+ "200,32,888,0.0159,2658200,13291,13291\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,892,0.0155,2670200,13351,13351\n",
+ "200,32,892,0.0159,2670200,13351,13351\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,896,0.0156,2682200,13411,13411\n",
+ "200,32,896,0.0160,2682200,13411,13411\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,900,0.0157,2694200,13471,13471\n",
+ "200,32,900,0.0160,2694200,13471,13471\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,904,0.0159,2706200,13531,13531\n",
+ "200,32,904,0.0162,2706200,13531,13531\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,908,0.0160,2718200,13591,13591\n",
+ "200,32,908,0.0162,2718200,13591,13591\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,912,0.0161,2730200,13651,13651\n",
+ "200,32,912,0.0163,2730200,13651,13651\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,916,0.0162,2742200,13711,13711\n",
+ "200,32,916,0.0163,2742200,13711,13711\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,920,0.0161,2754200,13771,13771\n",
+ "200,32,920,0.0164,2754200,13771,13771\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,924,0.0162,2766200,13831,13831\n",
+ "200,32,924,0.0165,2766200,13831,13831\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,928,0.0163,2778200,13891,13891\n",
+ "200,32,928,0.0166,2778200,13891,13891\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,932,0.0165,2790200,13951,13951\n",
+ "200,32,932,0.0168,2790200,13951,13951\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,936,0.0165,2802200,14011,14011\n",
+ "200,32,936,0.0167,2802200,14011,14011\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,940,0.0165,2814200,14071,14071\n",
+ "200,32,940,0.0169,2814200,14071,14071\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,944,0.0166,2826200,14131,14131\n",
+ "200,32,944,0.0169,2826200,14131,14131\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,948,0.0166,2838200,14191,14191\n",
+ "200,32,948,0.0169,2838200,14191,14191\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,952,0.0168,2850200,14251,14251\n",
+ "200,32,952,0.0170,2850200,14251,14251\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,956,0.0167,2862200,14311,14311\n",
+ "200,32,956,0.0170,2862200,14311,14311\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,960,0.0168,2874200,14371,14371\n",
+ "200,32,960,0.0171,2874200,14371,14371\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,964,0.0173,2886200,14431,14431\n",
+ "200,32,964,0.0175,2886200,14431,14431\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,968,0.0172,2898200,14491,14491\n",
+ "200,32,968,0.0175,2898200,14491,14491\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,972,0.0172,2910200,14551,14551\n",
+ "200,32,972,0.0176,2910200,14551,14551\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,976,0.0173,2922200,14611,14611\n",
+ "200,32,976,0.0176,2922200,14611,14611\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,980,0.0175,2934200,14671,14671\n",
+ "200,32,980,0.0178,2934200,14671,14671\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,984,0.0176,2946200,14731,14731\n",
+ "200,32,984,0.0178,2946200,14731,14731\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,988,0.0176,2958200,14791,14791\n",
+ "200,32,988,0.0179,2958200,14791,14791\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,992,0.0177,2970200,14851,14851\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
+ "200,32,992,0.0178,2970200,14851,14851\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,996,0.0178,2982200,14911,14911\n",
+ "200,32,996,0.0181,2982200,14911,14911\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,1000,0.0177,2994200,14971,14971\n",
+ "200,32,1000,0.0180,2994200,14971,14971\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,1004,0.0179,3006200,15031,15031\n",
+ "200,32,1004,0.0181,3006200,15031,15031\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,1008,0.0179,3018200,15091,15091\n",
+ "200,32,1008,0.0182,3018200,15091,15091\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,1012,0.0180,3030200,15151,15151\n",
+ "200,32,1012,0.0183,3030200,15151,15151\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,1016,0.0180,3042200,15211,15211\n",
+ "200,32,1016,0.0183,3042200,15211,15211\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,1020,0.0182,3054200,15271,15271\n",
+ "200,32,1020,0.0184,3054200,15271,15271\n",
"iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,1024,0.0178,3066200,15331,15331\n",
- "mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.vst.bin.csv .\n"
+ "200,32,1024,0.0182,3066200,15331,15331\n",
+ "mv /gpfs/wolf/trn003/scratch/aherten//poisson2d.vst.bin.csv .\n"
]
}
],
@@ -2815,12 +2939,12 @@
"source": [
"Let's plot it again, as soon as the run finishes! Non-interactively, call `graph_task2b`.\n",
"\n",
- "*We need to read in two CSV files now, which we combine to one common dataframe `df_vldvst`.*"
+ "*Because we couldn't measure the two vector counters at the same time, we have two CSV files to read in now. We combine them into one common dataframe `df_vldvst` in the following.*"
]
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
@@ -2831,7 +2955,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 32,
"metadata": {},
"outputs": [
{
@@ -2865,8 +2989,7 @@
" <th>PM_VECTOR_ST_CMPL (total)</th>\n",
" <th>PM_VECTOR_ST_CMPL (min)</th>\n",
" <th>PM_VECTOR_ST_CMPL (max)</th>\n",
- " <th>Vector Loads / Loop Iteration</th>\n",
- " <th>Vector Stores / Loop Iteration</th>\n",
+ " <th>Grid Points</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
@@ -2882,8 +3005,7 @@
" <td>200</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
- " <td>0.000000</td>\n",
- " <td>0.007812</td>\n",
+ " <td>128</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
@@ -2897,8 +3019,7 @@
" <td>18200</td>\n",
" <td>91</td>\n",
" <td>91</td>\n",
- " <td>2.226562</td>\n",
- " <td>0.355469</td>\n",
+ " <td>256</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
@@ -2912,38 +3033,35 @@
" <td>30200</td>\n",
" <td>151</td>\n",
" <td>151</td>\n",
- " <td>2.265625</td>\n",
- " <td>0.393229</td>\n",
+ " <td>384</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>16</td>\n",
" <td>200</td>\n",
" <td>32</td>\n",
- " <td>0.0013</td>\n",
+ " <td>0.0012</td>\n",
" <td>234000</td>\n",
" <td>1170</td>\n",
" <td>1170</td>\n",
" <td>42200</td>\n",
" <td>211</td>\n",
" <td>211</td>\n",
- " <td>2.285156</td>\n",
- " <td>0.412109</td>\n",
+ " <td>512</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>20</td>\n",
" <td>200</td>\n",
" <td>32</td>\n",
- " <td>0.0014</td>\n",
+ " <td>0.0013</td>\n",
" <td>294000</td>\n",
" <td>1470</td>\n",
" <td>1470</td>\n",
" <td>54200</td>\n",
" <td>271</td>\n",
" <td>271</td>\n",
- " <td>2.296875</td>\n",
- " <td>0.423438</td>\n",
+ " <td>640</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
@@ -2954,8 +3072,8 @@
"0 4 200 32 0.0010 0 0 \n",
"1 8 200 32 0.0011 114000 570 \n",
"2 12 200 32 0.0012 174000 870 \n",
- "3 16 200 32 0.0013 234000 1170 \n",
- "4 20 200 32 0.0014 294000 1470 \n",
+ "3 16 200 32 0.0012 234000 1170 \n",
+ "4 20 200 32 0.0013 294000 1470 \n",
"\n",
" PM_VECTOR_LD_CMPL (max) PM_VECTOR_ST_CMPL (total) \\\n",
"0 0 200 \n",
@@ -2964,52 +3082,109 @@
"3 1170 42200 \n",
"4 1470 54200 \n",
"\n",
- " PM_VECTOR_ST_CMPL (min) PM_VECTOR_ST_CMPL (max) \\\n",
- "0 1 1 \n",
- "1 91 91 \n",
- "2 151 151 \n",
- "3 211 211 \n",
- "4 271 271 \n",
- "\n",
- " Vector Loads / Loop Iteration Vector Stores / Loop Iteration \n",
- "0 0.000000 0.007812 \n",
- "1 2.226562 0.355469 \n",
- "2 2.265625 0.393229 \n",
- "3 2.285156 0.412109 \n",
- "4 2.296875 0.423438 "
+ " PM_VECTOR_ST_CMPL (min) PM_VECTOR_ST_CMPL (max) Grid Points \n",
+ "0 1 1 128 \n",
+ "1 91 91 256 \n",
+ "2 151 151 384 \n",
+ "3 211 211 512 \n",
+ "4 271 271 640 "
]
},
- "execution_count": 9,
+ "execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "common.normalize(df_vldvst, \"PM_VECTOR_LD_CMPL (min)\", \"Vector Loads / Loop Iteration\")\n",
- "common.normalize(df_vldvst, \"PM_VECTOR_ST_CMPL (min)\", \"Vector Stores / Loop Iteration\")\n",
+ "df_vldvst[\"Grid Points\"] = df_vldvst[\"nx\"] * df_vldvst[\"ny\"] \n",
"df_vldvst.head()"
]
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 1008x432 with 2 Axes>"
]
},
- "metadata": {},
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, (ax1, ax2) = plt.subplots(nrows=2, sharex=True)\n",
+ "df_vldvst.set_index(\"Grid Points\")[\"PM_VECTOR_LD_CMPL (min)\"].plot(ax=ax1, legend=True);\n",
+ "df_vldvst.set_index(\"Grid Points\")[\"PM_VECTOR_ST_CMPL (min)\"].plot(ax=ax2, legend=True);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Also here seems to be a linear correlation. Let's do our fitting and plot directly."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Counter PM_VECTOR_LD_CMPL (min) is proportional to the grid points (nx*ny) by a factor of 2.3439 (± 0.000111)\n",
+ "Counter PM_VECTOR_ST_CMPL (min) is proportional to the grid points (nx*ny) by a factor of 0.4688 (± 0.000012)\n"
+ ]
+ }
+ ],
+ "source": [
+ "_fit, _cov = common.print_and_return_fit(\n",
+ " [\"PM_VECTOR_LD_CMPL (min)\", \"PM_VECTOR_ST_CMPL (min)\"], \n",
+ " df_vldvst.set_index(\"Grid Points\"), \n",
+ " linear_function,\n",
+ " format_value=\".4f\",\n",
+ ")\n",
+ "fit_parameters = {**fit_parameters, **_fit}\n",
+ "fit_covariance = {**fit_covariance, **_cov}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1008x432 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
"output_type": "display_data"
}
],
"source": [
"fig, (ax1, ax2) = plt.subplots(nrows=2, sharex=True)\n",
- "df_vldvst.set_index(\"nx\")[\"Vector Loads / Loop Iteration\"].plot(ax=ax1, legend=True);\n",
- "df_vldvst.set_index(\"nx\")[\"Vector Stores / Loop Iteration\"].plot(ax=ax2, legend=True);"
+ "for ax, pmu_counter in zip([ax1, ax2], [\"PM_VECTOR_LD_CMPL (min)\", \"PM_VECTOR_ST_CMPL (min)\"]):\n",
+ " df_vldvst.set_index(\"Grid Points\")[pmu_counter].plot(ax=ax, legend=True);\n",
+ " ax.plot(\n",
+ " df_vldvst[\"Grid Points\"], \n",
+ " linear_function(df[\"Grid Points\"], *fit_parameters[pmu_counter]), \n",
+ " linestyle=\"--\", \n",
+ " label=\"Fit: {:.2f} * x + {:.2f}\".format(*fit_parameters[pmu_counter])\n",
+ " )\n",
+ " ax.legend();"
]
},
{
@@ -3038,46 +3213,66 @@
},
{
"cell_type": "code",
- "execution_count": 83,
+ "execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 1008x432 with 1 Axes>"
]
},
- "metadata": {},
+ "metadata": {
+ "needs_background": "light"
+ },
"output_type": "display_data"
}
],
"source": [
"df_byte = pd.DataFrame()\n",
- "df_byte[\"Loads / Loop Iteration\"] = (df_vldvst.set_index(\"nx\")[\"Vector Loads / Loop Iteration\"] + df_ldst.set_index(\"nx\")[\"Loads / Loop Iteration\"])*8\n",
- "df_byte[\"Stores / Loop Iteration\"] = (df_vldvst.set_index(\"nx\")[\"Vector Stores / Loop Iteration\"] + df_ldst.set_index(\"nx\")[\"Stores / Loop Iteration\"])*8\n",
+ "df_byte[\"Loads\"] = (df_vldvst.set_index(\"Grid Points\")[\"PM_VECTOR_LD_CMPL (min)\"] + df_ldst.set_index(\"Grid Points\")[\"PM_LD_CMPL (min)\"])*8\n",
+ "df_byte[\"Stores\"] = (df_vldvst.set_index(\"Grid Points\")[\"PM_VECTOR_ST_CMPL (min)\"] + df_ldst.set_index(\"Grid Points\")[\"PM_ST_CMPL (min)\"])*8\n",
"ax = df_byte.plot()\n",
- "ax.set_ylabel(\"Bytes / Loop Iteration\");"
+ "ax.set_ylabel(\"Bytes\");"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's quantify the difference by, again, fitting a linear function to the data."
]
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 38,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Mean byte loaded: 37.52662546714877\tMean byte stored: 8.428951320998907\n"
+ "Counter Loads is proportional to the grid points (nx*ny) by a factor of 37.5010 (± 0.000592)\n",
+ "Counter Stores is proportional to the grid points (nx*ny) by a factor of 8.4379 (± 0.000247)\n"
]
}
],
"source": [
- "import numpy as np\n",
- "mean_byte_ld = np.polyfit(df_byte[df_byte.index > 200].index, df_byte[df_byte.index > 200][\"Loads / Loop Iteration\"], 0)[0]\n",
- "mean_byte_st = np.polyfit(df_byte[df_byte.index > 200].index, df_byte[df_byte.index > 200][\"Stores / Loop Iteration\"], 0)[0]\n",
- "print(\"Mean byte loaded: {}\\tMean byte stored: {}\".format(mean_byte_ld, mean_byte_st))"
+ "_fit, _cov = common.print_and_return_fit(\n",
+ " [\"Loads\", \"Stores\"], \n",
+ " df_byte, \n",
+ " linear_function\n",
+ ")\n",
+ "fit_parameters = {**fit_parameters, **_fit}\n",
+ "fit_covariance = {**fit_covariance, **_cov}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Analagously to the proportionality factors, this mich is loaded/stored per grid point."
]
},
{
@@ -3089,34 +3284,36 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"df_bandwidth = pd.DataFrame()\n",
- "df_bandwidth[\"Bandwidth / Byte/Cycle\"] = (df_byte[\"Loads / Loop Iteration\"] + df_byte[\"Stores / Loop Iteration\"]) / df.set_index(\"nx\")[\"Cycles / Loop Iteration\"]"
+ "df_bandwidth[\"Bandwidth / Byte/Cycle\"] = (df_byte[\"Loads\"] + df_byte[\"Stores\"]) / df.set_index(\"Grid Points\")[\"PM_RUN_CYC (min)\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "Let's display it as a function of `nx`. And also compare it to the available L1 cache bandwidth in a second (sub-)plot. Non-interactive users, call `make graph_task2c`."
+ "Let's display it as a function of grid points. And also compare it to the available L1 cache bandwidth in a second (sub-)plot. Non-interactive users, call `make graph_task2c`."
]
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 1008x432 with 2 Axes>"
]
},
- "metadata": {},
+ "metadata": {
+ "needs_background": "light"
+ },
"output_type": "display_data"
}
],
@@ -3146,7 +3343,7 @@
"If you still have time, feel free to work on the following extended task.\n",
"\n",
"\n",
- "**TASK**: Please measure counters for _vectorized_ floating point operations and _scalar_ floating point operations. The two counters can also not be measured during the same run. So please see the TODOs in [`poisson2d.sflops.c`](/edit/Tasks/poisson2d.sflops.c) and [`poisson2d.vflops.c`](/edit/Tasks/poisson2d.vflops.c). By now you should be able to find out the names of the counters by yourself (*Hint: they include the words scalar and vector…*).\n",
+ "**TASK**: Please measure counters for _vectorized_ floating point operations and _scalar_ floating point operations. The two counters can also not be measured during the same run. So please see the TODOs in [`poisson2d.sflops.c`](/edit/Tasks/poisson2d.sflops.c) and [`poisson2d.vflops.c`](/edit/Tasks/poisson2d.vflops.c). By now you should be able to find out the names of the counters by yourself (*Hint: they include the words »scalar« and »vector«…*).\n",
"\n",
"As usual, compile, test, and bench-run your program.\n",
"\n",
@@ -3155,15 +3352,15 @@
},
{
"cell_type": "code",
- "execution_count": 42,
+ "execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "bsub -W 60 -nnodes 1 -Is -P GEN110 jsrun -n 1 -c 1 -g ALL_GPUS ./bench.sh poisson2d.sflop.bin /gpfs/wolf/gen110/scratch/aherten//poisson2d.sflop.bin.csv\n",
- "Job <4299> is submitted to default queue <batch>.\n",
+ "bsub -W 60 -nnodes 1 -Is -P TRN003 jsrun -n 1 -c 1 -g ALL_GPUS ./bench.sh poisson2d.sflop.bin /gpfs/wolf/trn003/scratch/aherten//poisson2d.sflop.bin.csv\n",
+ "Job <24645> is submitted to default queue <batch>.\n",
"<<Waiting for dispatch ...>>\n",
"<<Starting on login1>>\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
@@ -3177,7 +3374,7 @@
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
"200,32,20,0.0013,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,24,0.0014,0,0,0\n",
+ "200,32,24,0.0013,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
"200,32,28,0.0014,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
@@ -3193,21 +3390,21 @@
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
"200,32,52,0.0018,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,56,0.0019,0,0,0\n",
+ "200,32,56,0.0022,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,60,0.0020,0,0,0\n",
+ "200,32,60,0.0019,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
"200,32,64,0.0021,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
"200,32,68,0.0022,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,72,0.0022,0,0,0\n",
+ "200,32,72,0.0021,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
"200,32,76,0.0022,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
"200,32,80,0.0023,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,84,0.0024,0,0,0\n",
+ "200,32,84,0.0025,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
"200,32,88,0.0024,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
@@ -3215,39 +3412,39 @@
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
"200,32,96,0.0025,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,100,0.0028,0,0,0\n",
+ "200,32,100,0.0026,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
"200,32,104,0.0027,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
"200,32,108,0.0027,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,112,0.0029,0,0,0\n",
+ "200,32,112,0.0028,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
"200,32,116,0.0028,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,120,0.0029,0,0,0\n",
+ "200,32,120,0.0031,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
"200,32,124,0.0030,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,128,0.0031,0,0,0\n",
+ "200,32,128,0.0030,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
"200,32,132,0.0031,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
"200,32,136,0.0032,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,140,0.0033,0,0,0\n",
+ "200,32,140,0.0032,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,144,0.0034,0,0,0\n",
+ "200,32,144,0.0033,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
"200,32,148,0.0034,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,152,0.0034,0,0,0\n",
+ "200,32,152,0.0035,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
"200,32,156,0.0035,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
"200,32,160,0.0036,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,164,0.0037,0,0,0\n",
+ "200,32,164,0.0036,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
"200,32,168,0.0037,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
@@ -3257,13 +3454,13 @@
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
"200,32,180,0.0039,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,184,0.0039,0,0,0\n",
+ "200,32,184,0.0040,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
"200,32,188,0.0040,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
"200,32,192,0.0041,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,196,0.0041,0,0,0\n",
+ "200,32,196,0.0042,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
"200,32,200,0.0042,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
@@ -3275,9 +3472,9 @@
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
"200,32,216,0.0045,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,220,0.0046,0,0,0\n",
+ "200,32,220,0.0045,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,224,0.0047,0,0,0\n",
+ "200,32,224,0.0046,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
"200,32,228,0.0047,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
@@ -3289,97 +3486,91 @@
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
"200,32,244,0.0049,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,248,0.0050,0,0,0\n",
+ "200,32,248,0.0051,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,252,0.0050,0,0,0\n",
+ "200,32,252,0.0051,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,256,0.0051,0,0,0\n",
+ "200,32,256,0.0053,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
"200,32,260,0.0052,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,264,0.0053,0,0,0\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
+ "200,32,264,0.0053,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
"200,32,268,0.0054,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,272,0.0055,0,0,0\n",
+ "200,32,272,0.0054,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,276,0.0055,0,0,0\n",
+ "200,32,276,0.0054,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
"200,32,280,0.0055,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
"200,32,284,0.0056,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,288,0.0057,0,0,0\n",
+ "200,32,288,0.0056,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
"200,32,292,0.0057,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
"200,32,296,0.0058,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,300,0.0059,0,0,0\n",
+ "200,32,300,0.0058,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
"200,32,304,0.0059,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,308,0.0059,0,0,0\n",
+ "200,32,308,0.0060,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
"200,32,312,0.0060,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,316,0.0061,0,0,0\n",
+ "200,32,316,0.0062,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,320,0.0061,0,0,0\n",
+ "200,32,320,0.0062,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
"200,32,324,0.0062,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
"200,32,328,0.0063,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,332,0.0065,0,0,0\n",
+ "200,32,332,0.0064,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,336,0.0064,0,0,0\n",
+ "200,32,336,0.0065,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
"200,32,340,0.0065,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,344,0.0065,0,0,0\n",
+ "200,32,344,0.0066,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
"200,32,348,0.0066,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
"200,32,352,0.0067,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,356,0.0067,0,0,0\n",
+ "200,32,356,0.0068,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,360,0.0068,0,0,0\n",
+ "200,32,360,0.0069,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
"200,32,364,0.0069,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
"200,32,368,0.0070,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,372,0.0070,0,0,0\n",
+ "200,32,372,0.0072,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
"200,32,376,0.0071,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,380,0.0072,0,0,0\n",
+ "200,32,380,0.0071,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
"200,32,384,0.0072,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,388,0.0072,0,0,0\n",
+ "200,32,388,0.0073,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,392,0.0075,0,0,0\n",
+ "200,32,392,0.0074,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,396,0.0074,0,0,0\n",
+ "200,32,396,0.0076,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
"200,32,400,0.0075,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,404,0.0075,0,0,0\n",
+ "200,32,404,0.0076,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
"200,32,408,0.0076,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
"200,32,412,0.0077,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,416,0.0077,0,0,0\n",
+ "200,32,416,0.0078,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
"200,32,420,0.0078,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
@@ -3389,27 +3580,27 @@
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
"200,32,432,0.0080,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,436,0.0080,0,0,0\n",
+ "200,32,436,0.0081,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,440,0.0081,0,0,0\n",
+ "200,32,440,0.0082,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,444,0.0083,0,0,0\n",
+ "200,32,444,0.0082,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
"200,32,448,0.0084,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,452,0.0084,0,0,0\n",
+ "200,32,452,0.0083,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
"200,32,456,0.0084,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
"200,32,460,0.0085,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,464,0.0086,0,0,0\n",
+ "200,32,464,0.0085,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
"200,32,468,0.0086,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,472,0.0088,0,0,0\n",
+ "200,32,472,0.0087,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,476,0.0087,0,0,0\n",
+ "200,32,476,0.0089,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
"200,32,480,0.0088,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
@@ -3419,7 +3610,7 @@
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
"200,32,492,0.0090,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,496,0.0090,0,0,0\n",
+ "200,32,496,0.0091,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
"200,32,500,0.0092,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
@@ -3427,278 +3618,266 @@
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
"200,32,508,0.0093,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,512,0.0092,0,0,0\n",
+ "200,32,512,0.0094,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,516,0.0093,0,0,0\n",
+ "200,32,516,0.0094,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,520,0.0094,0,0,0\n",
+ "200,32,520,0.0095,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,524,0.0094,0,0,0\n",
+ "200,32,524,0.0096,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,528,0.0094,0,0,0\n",
+ "200,32,528,0.0096,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,532,0.0095,0,0,0\n",
+ "200,32,532,0.0098,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,536,0.0096,0,0,0\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
+ "200,32,536,0.0097,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
"200,32,540,0.0098,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,544,0.0097,0,0,0\n",
+ "200,32,544,0.0099,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,548,0.0098,0,0,0\n",
+ "200,32,548,0.0100,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,552,0.0099,0,0,0\n",
+ "200,32,552,0.0101,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,556,0.0099,0,0,0\n",
+ "200,32,556,0.0101,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,560,0.0100,0,0,0\n",
+ "200,32,560,0.0102,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,564,0.0102,0,0,0\n",
+ "200,32,564,0.0103,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,568,0.0102,0,0,0\n",
+ "200,32,568,0.0104,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,572,0.0103,0,0,0\n",
+ "200,32,572,0.0105,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,576,0.0103,0,0,0\n",
+ "200,32,576,0.0105,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,580,0.0105,0,0,0\n",
+ "200,32,580,0.0106,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,584,0.0104,0,0,0\n",
+ "200,32,584,0.0107,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,588,0.0106,0,0,0\n",
+ "200,32,588,0.0107,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,592,0.0107,0,0,0\n",
+ "200,32,592,0.0108,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,596,0.0106,0,0,0\n",
+ "200,32,596,0.0109,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,600,0.0107,0,0,0\n",
+ "200,32,600,0.0110,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,604,0.0109,0,0,0\n",
+ "200,32,604,0.0111,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,608,0.0109,0,0,0\n",
+ "200,32,608,0.0111,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,612,0.0109,0,0,0\n",
+ "200,32,612,0.0112,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,616,0.0110,0,0,0\n",
+ "200,32,616,0.0112,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,620,0.0117,0,0,0\n",
+ "200,32,620,0.0113,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,624,0.0112,0,0,0\n",
+ "200,32,624,0.0114,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,628,0.0111,0,0,0\n",
+ "200,32,628,0.0115,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,632,0.0112,0,0,0\n",
+ "200,32,632,0.0115,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,636,0.0113,0,0,0\n",
+ "200,32,636,0.0115,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,640,0.0115,0,0,0\n",
+ "200,32,640,0.0116,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,644,0.0114,0,0,0\n",
+ "200,32,644,0.0118,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,648,0.0115,0,0,0\n",
+ "200,32,648,0.0117,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,652,0.0116,0,0,0\n",
+ "200,32,652,0.0119,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,656,0.0117,0,0,0\n",
+ "200,32,656,0.0119,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,660,0.0117,0,0,0\n",
+ "200,32,660,0.0121,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,664,0.0118,0,0,0\n",
+ "200,32,664,0.0120,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,668,0.0119,0,0,0\n",
+ "200,32,668,0.0122,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,672,0.0119,0,0,0\n",
+ "200,32,672,0.0121,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,676,0.0119,0,0,0\n",
+ "200,32,676,0.0124,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,680,0.0120,0,0,0\n",
+ "200,32,680,0.0123,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,684,0.0121,0,0,0\n",
+ "200,32,684,0.0125,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,688,0.0122,0,0,0\n",
+ "200,32,688,0.0124,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,692,0.0122,0,0,0\n",
+ "200,32,692,0.0125,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,696,0.0123,0,0,0\n",
+ "200,32,696,0.0126,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,700,0.0124,0,0,0\n",
+ "200,32,700,0.0127,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,704,0.0124,0,0,0\n",
+ "200,32,704,0.0126,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,708,0.0125,0,0,0\n",
+ "200,32,708,0.0127,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,712,0.0125,0,0,0\n",
+ "200,32,712,0.0129,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,716,0.0126,0,0,0\n",
+ "200,32,716,0.0128,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,720,0.0126,0,0,0\n",
+ "200,32,720,0.0129,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,724,0.0127,0,0,0\n",
+ "200,32,724,0.0132,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,728,0.0128,0,0,0\n",
+ "200,32,728,0.0131,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,732,0.0128,0,0,0\n",
+ "200,32,732,0.0131,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,736,0.0129,0,0,0\n",
+ "200,32,736,0.0133,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,740,0.0130,0,0,0\n",
+ "200,32,740,0.0133,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,744,0.0130,0,0,0\n",
+ "200,32,744,0.0133,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,748,0.0131,0,0,0\n",
+ "200,32,748,0.0134,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,752,0.0131,0,0,0\n",
+ "200,32,752,0.0136,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,756,0.0132,0,0,0\n",
+ "200,32,756,0.0136,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,760,0.0133,0,0,0\n",
+ "200,32,760,0.0136,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,764,0.0134,0,0,0\n",
+ "200,32,764,0.0136,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,768,0.0134,0,0,0\n",
+ "200,32,768,0.0138,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,772,0.0136,0,0,0\n",
+ "200,32,772,0.0138,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,776,0.0136,0,0,0\n",
+ "200,32,776,0.0139,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,780,0.0136,0,0,0\n",
+ "200,32,780,0.0139,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,784,0.0137,0,0,0\n",
+ "200,32,784,0.0140,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,788,0.0138,0,0,0\n",
+ "200,32,788,0.0140,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,792,0.0139,0,0,0\n",
+ "200,32,792,0.0141,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,796,0.0139,0,0,0\n",
+ "200,32,796,0.0142,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,800,0.0140,0,0,0\n",
+ "200,32,800,0.0143,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,804,0.0141,0,0,0\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
+ "200,32,804,0.0143,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,808,0.0142,0,0,0\n",
+ "200,32,808,0.0144,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,812,0.0142,0,0,0\n",
+ "200,32,812,0.0144,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,816,0.0143,0,0,0\n",
+ "200,32,816,0.0145,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,820,0.0143,0,0,0\n",
+ "200,32,820,0.0146,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,824,0.0144,0,0,0\n",
+ "200,32,824,0.0148,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,828,0.0145,0,0,0\n",
+ "200,32,828,0.0147,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,832,0.0145,0,0,0\n",
+ "200,32,832,0.0148,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,836,0.0146,0,0,0\n",
+ "200,32,836,0.0149,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,840,0.0147,0,0,0\n",
+ "200,32,840,0.0150,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,844,0.0147,0,0,0\n",
+ "200,32,844,0.0150,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,848,0.0148,0,0,0\n",
+ "200,32,848,0.0150,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,852,0.0149,0,0,0\n",
+ "200,32,852,0.0151,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,856,0.0149,0,0,0\n",
+ "200,32,856,0.0152,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,860,0.0150,0,0,0\n",
+ "200,32,860,0.0152,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,864,0.0150,0,0,0\n",
+ "200,32,864,0.0153,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,868,0.0152,0,0,0\n",
+ "200,32,868,0.0154,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,872,0.0151,0,0,0\n",
+ "200,32,872,0.0156,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,876,0.0153,0,0,0\n",
+ "200,32,876,0.0156,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,880,0.0153,0,0,0\n",
+ "200,32,880,0.0156,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,884,0.0153,0,0,0\n",
+ "200,32,884,0.0157,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,888,0.0155,0,0,0\n",
+ "200,32,888,0.0157,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,892,0.0156,0,0,0\n",
+ "200,32,892,0.0158,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,896,0.0156,0,0,0\n",
+ "200,32,896,0.0159,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,900,0.0158,0,0,0\n",
+ "200,32,900,0.0159,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,904,0.0158,0,0,0\n",
+ "200,32,904,0.0161,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,908,0.0159,0,0,0\n",
+ "200,32,908,0.0162,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,912,0.0159,0,0,0\n",
+ "200,32,912,0.0164,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,916,0.0162,0,0,0\n",
+ "200,32,916,0.0163,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,920,0.0162,0,0,0\n",
+ "200,32,920,0.0164,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,924,0.0162,0,0,0\n",
+ "200,32,924,0.0165,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,928,0.0162,0,0,0\n",
+ "200,32,928,0.0166,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,932,0.0163,0,0,0\n",
+ "200,32,932,0.0166,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,936,0.0164,0,0,0\n",
+ "200,32,936,0.0167,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,940,0.0165,0,0,0\n",
+ "200,32,940,0.0167,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,944,0.0165,0,0,0\n",
+ "200,32,944,0.0168,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,948,0.0166,0,0,0\n",
+ "200,32,948,0.0169,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,952,0.0167,0,0,0\n",
+ "200,32,952,0.0172,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,956,0.0168,0,0,0\n",
+ "200,32,956,0.0171,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,960,0.0168,0,0,0\n",
+ "200,32,960,0.0172,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,964,0.0172,0,0,0\n",
+ "200,32,964,0.0175,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,968,0.0173,0,0,0\n",
+ "200,32,968,0.0175,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,972,0.0173,0,0,0\n",
+ "200,32,972,0.0176,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,976,0.0173,0,0,0\n",
+ "200,32,976,0.0177,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,980,0.0175,0,0,0\n",
+ "200,32,980,0.0178,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,984,0.0176,0,0,0\n",
+ "200,32,984,0.0178,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,988,0.0175,0,0,0\n",
+ "200,32,988,0.0179,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,992,0.0176,0,0,0\n",
+ "200,32,992,0.0179,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,996,0.0178,0,0,0\n",
+ "200,32,996,0.0182,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,1000,0.0177,0,0,0\n",
+ "200,32,1000,0.0181,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,1004,0.0178,0,0,0\n",
+ "200,32,1004,0.0182,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,1008,0.0178,0,0,0\n",
+ "200,32,1008,0.0182,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,1012,0.0181,0,0,0\n",
+ "200,32,1012,0.0184,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,1016,0.0180,0,0,0\n",
+ "200,32,1016,0.0184,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,1020,0.0182,0,0,0\n",
+ "200,32,1020,0.0186,0,0,0\n",
"iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,1024,0.0179,0,0,0\n",
- "mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.sflop.bin.csv .\n",
- "bsub -W 60 -nnodes 1 -Is -P GEN110 jsrun -n 1 -c 1 -g ALL_GPUS ./bench.sh poisson2d.vflop.bin /gpfs/wolf/gen110/scratch/aherten//poisson2d.vflop.bin.csv\n",
- "Job <4300> is submitted to default queue <batch>.\n",
+ "200,32,1024,0.0182,0,0,0\n",
+ "mv /gpfs/wolf/trn003/scratch/aherten//poisson2d.sflop.bin.csv .\n",
+ "bsub -W 60 -nnodes 1 -Is -P TRN003 jsrun -n 1 -c 1 -g ALL_GPUS ./bench.sh poisson2d.vflop.bin /gpfs/wolf/trn003/scratch/aherten//poisson2d.vflop.bin.csv\n",
+ "Job <24646> is submitted to default queue <batch>.\n",
"<<Waiting for dispatch ...>>\n",
"<<Starting on login1>>\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
@@ -3712,17 +3891,11 @@
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
"200,32,20,0.0013,438000,2190,2190\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,24,0.0014,534000,2670,2670\n",
+ "200,32,24,0.0013,534000,2670,2670\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
"200,32,28,0.0014,630000,3150,3150\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,32,0.0015,726000,3630,3630\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
+ "200,32,32,0.0015,726000,3630,3630\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
"200,32,36,0.0016,822000,4110,4110\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
@@ -3730,29 +3903,29 @@
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
"200,32,44,0.0017,1014000,5070,5070\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,48,0.0018,1110000,5550,5550\n",
+ "200,32,48,0.0017,1110000,5550,5550\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
"200,32,52,0.0018,1206000,6030,6030\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,56,0.0020,1302000,6510,6510\n",
+ "200,32,56,0.0019,1302000,6510,6510\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,60,0.0020,1398000,6990,6990\n",
+ "200,32,60,0.0019,1398000,6990,6990\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,64,0.0021,1494000,7470,7470\n",
+ "200,32,64,0.0020,1494000,7470,7470\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
"200,32,68,0.0022,1590000,7950,7950\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,72,0.0022,1686000,8430,8430\n",
+ "200,32,72,0.0021,1686000,8430,8430\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
"200,32,76,0.0022,1782000,8910,8910\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
"200,32,80,0.0023,1878000,9390,9390\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,84,0.0024,1974000,9870,9870\n",
+ "200,32,84,0.0025,1974000,9870,9870\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
"200,32,88,0.0024,2070000,10350,10350\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,92,0.0025,2166000,10830,10830\n",
+ "200,32,92,0.0026,2166000,10830,10830\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
"200,32,96,0.0025,2262000,11310,11310\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
@@ -3760,13 +3933,13 @@
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
"200,32,104,0.0027,2454000,12270,12270\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,108,0.0028,2550000,12750,12750\n",
+ "200,32,108,0.0027,2550000,12750,12750\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,112,0.0028,2646000,13230,13230\n",
+ "200,32,112,0.0029,2646000,13230,13230\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
"200,32,116,0.0029,2742000,13710,13710\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,120,0.0032,2838000,14190,14190\n",
+ "200,32,120,0.0029,2838000,14190,14190\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
"200,32,124,0.0030,2934000,14670,14670\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
@@ -3776,15 +3949,15 @@
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
"200,32,136,0.0032,3222000,16110,16110\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,140,0.0033,3318000,16590,16590\n",
+ "200,32,140,0.0032,3318000,16590,16590\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
"200,32,144,0.0033,3414000,17070,17070\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,148,0.0034,3510000,17550,17550\n",
+ "200,32,148,0.0036,3510000,17550,17550\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,152,0.0034,3606000,18030,18030\n",
+ "200,32,152,0.0035,3606000,18030,18030\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,156,0.0036,3702000,18510,18510\n",
+ "200,32,156,0.0035,3702000,18510,18510\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
"200,32,160,0.0036,3798000,18990,18990\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
@@ -3794,13 +3967,13 @@
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
"200,32,172,0.0038,4086000,20430,20430\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,176,0.0039,4182000,20910,20910\n",
+ "200,32,176,0.0038,4182000,20910,20910\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
"200,32,180,0.0039,4278000,21390,21390\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
"200,32,184,0.0040,4374000,21870,21870\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,188,0.0040,4470000,22350,22350\n",
+ "200,32,188,0.0041,4470000,22350,22350\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
"200,32,192,0.0041,4566000,22830,22830\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
@@ -3810,25 +3983,25 @@
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
"200,32,204,0.0043,4854000,24270,24270\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,208,0.0043,4950000,24750,24750\n",
+ "200,32,208,0.0044,4950000,24750,24750\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
"200,32,212,0.0044,5046000,25230,25230\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
"200,32,216,0.0045,5142000,25710,25710\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,220,0.0047,5238000,26190,26190\n",
+ "200,32,220,0.0046,5238000,26190,26190\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
"200,32,224,0.0046,5334000,26670,26670\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,228,0.0047,5430000,27150,27150\n",
+ "200,32,228,0.0048,5430000,27150,27150\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,232,0.0047,5526000,27630,27630\n",
+ "200,32,232,0.0049,5526000,27630,27630\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
"200,32,236,0.0048,5622000,28110,28110\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
"200,32,240,0.0049,5718000,28590,28590\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,244,0.0050,5814000,29070,29070\n",
+ "200,32,244,0.0049,5814000,29070,29070\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
"200,32,248,0.0050,5910000,29550,29550\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
@@ -3838,25 +4011,19 @@
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
"200,32,260,0.0052,6198000,30990,30990\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,264,0.0052,6294000,31470,31470\n",
+ "200,32,264,0.0053,6294000,31470,31470\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,268,0.0053,6390000,31950,31950\n",
+ "200,32,268,0.0054,6390000,31950,31950\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
"200,32,272,0.0054,6486000,32430,32430\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,276,0.0058,6582000,32910,32910\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
+ "200,32,276,0.0054,6582000,32910,32910\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
"200,32,280,0.0055,6678000,33390,33390\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
"200,32,284,0.0056,6774000,33870,33870\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,288,0.0056,6870000,34350,34350\n",
+ "200,32,288,0.0057,6870000,34350,34350\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
"200,32,292,0.0057,6966000,34830,34830\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
@@ -3864,23 +4031,23 @@
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
"200,32,300,0.0059,7158000,35790,35790\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,304,0.0060,7254000,36270,36270\n",
+ "200,32,304,0.0059,7254000,36270,36270\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
"200,32,308,0.0060,7350000,36750,36750\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,312,0.0061,7446000,37230,37230\n",
+ "200,32,312,0.0062,7446000,37230,37230\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
"200,32,316,0.0061,7542000,37710,37710\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
"200,32,320,0.0062,7638000,38190,38190\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,324,0.0063,7734000,38670,38670\n",
+ "200,32,324,0.0062,7734000,38670,38670\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,328,0.0064,7830000,39150,39150\n",
+ "200,32,328,0.0063,7830000,39150,39150\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
"200,32,332,0.0064,7926000,39630,39630\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,336,0.0064,8022000,40110,40110\n",
+ "200,32,336,0.0065,8022000,40110,40110\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
"200,32,340,0.0065,8118000,40590,40590\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
@@ -3888,21 +4055,21 @@
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
"200,32,348,0.0066,8310000,41550,41550\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,352,0.0068,8406000,42030,42030\n",
+ "200,32,352,0.0067,8406000,42030,42030\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,356,0.0069,8502000,42510,42510\n",
+ "200,32,356,0.0068,8502000,42510,42510\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
"200,32,360,0.0068,8598000,42990,42990\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
"200,32,364,0.0069,8694000,43470,43470\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,368,0.0069,8790000,43950,43950\n",
+ "200,32,368,0.0070,8790000,43950,43950\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
"200,32,372,0.0070,8886000,44430,44430\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
"200,32,376,0.0071,8982000,44910,44910\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,380,0.0071,9078000,45390,45390\n",
+ "200,32,380,0.0072,9078000,45390,45390\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
"200,32,384,0.0072,9174000,45870,45870\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
@@ -3920,23 +4087,23 @@
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
"200,32,412,0.0077,9846000,49230,49230\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,416,0.0077,9942000,49710,49710\n",
+ "200,32,416,0.0079,9942000,49710,49710\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
"200,32,420,0.0078,10038000,50190,50190\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,424,0.0079,10134000,50670,50670\n",
+ "200,32,424,0.0080,10134000,50670,50670\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,428,0.0079,10230000,51150,51150\n",
+ "200,32,428,0.0080,10230000,51150,51150\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
"200,32,432,0.0080,10326000,51630,51630\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,436,0.0080,10422000,52110,52110\n",
+ "200,32,436,0.0083,10422000,52110,52110\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,440,0.0081,10518000,52590,52590\n",
+ "200,32,440,0.0082,10518000,52590,52590\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,444,0.0082,10614000,53070,53070\n",
+ "200,32,444,0.0083,10614000,53070,53070\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,448,0.0082,10710000,53550,53550\n",
+ "200,32,448,0.0083,10710000,53550,53550\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
"200,32,452,0.0083,10806000,54030,54030\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
@@ -3948,302 +4115,284 @@
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
"200,32,468,0.0086,11190000,55950,55950\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,472,0.0088,11286000,56430,56430\n",
+ "200,32,472,0.0087,11286000,56430,56430\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,476,0.0089,11382000,56910,56910\n",
+ "200,32,476,0.0087,11382000,56910,56910\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
"200,32,480,0.0088,11478000,57390,57390\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,484,0.0088,11574000,57870,57870\n",
+ "200,32,484,0.0089,11574000,57870,57870\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
"200,32,488,0.0089,11670000,58350,58350\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,492,0.0090,11766000,58830,58830\n",
+ "200,32,492,0.0091,11766000,58830,58830\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,496,0.0090,11862000,59310,59310\n",
+ "200,32,496,0.0091,11862000,59310,59310\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
"200,32,500,0.0091,11958000,59790,59790\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
"200,32,504,0.0092,12054000,60270,60270\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,508,0.0094,12150000,60750,60750\n",
+ "200,32,508,0.0093,12150000,60750,60750\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,512,0.0092,12246000,61230,61230\n",
+ "200,32,512,0.0094,12246000,61230,61230\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,516,0.0093,12342000,61710,61710\n",
+ "200,32,516,0.0096,12342000,61710,61710\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,520,0.0093,12438000,62190,62190\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
+ "200,32,520,0.0096,12438000,62190,62190\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,524,0.0094,12534000,62670,62670\n",
+ "200,32,524,0.0095,12534000,62670,62670\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,528,0.0094,12630000,63150,63150\n",
+ "200,32,528,0.0098,12630000,63150,63150\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,532,0.0095,12726000,63630,63630\n",
+ "200,32,532,0.0097,12726000,63630,63630\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,536,0.0096,12822000,64110,64110\n",
+ "200,32,536,0.0097,12822000,64110,64110\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,540,0.0100,12918000,64590,64590\n",
+ "200,32,540,0.0098,12918000,64590,64590\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,544,0.0097,13014000,65070,65070\n",
+ "200,32,544,0.0100,13014000,65070,65070\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,548,0.0098,13110000,65550,65550\n",
+ "200,32,548,0.0102,13110000,65550,65550\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,552,0.0099,13206000,66030,66030\n",
+ "200,32,552,0.0102,13206000,66030,66030\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,556,0.0100,13302000,66510,66510\n",
+ "200,32,556,0.0101,13302000,66510,66510\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,560,0.0101,13398000,66990,66990\n",
+ "200,32,560,0.0103,13398000,66990,66990\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,564,0.0102,13494000,67470,67470\n",
+ "200,32,564,0.0103,13494000,67470,67470\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,568,0.0103,13590000,67950,67950\n",
+ "200,32,568,0.0104,13590000,67950,67950\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,572,0.0103,13686000,68430,68430\n",
+ "200,32,572,0.0105,13686000,68430,68430\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,576,0.0103,13782000,68910,68910\n",
+ "200,32,576,0.0105,13782000,68910,68910\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,580,0.0105,13878000,69390,69390\n",
+ "200,32,580,0.0107,13878000,69390,69390\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,584,0.0105,13974000,69870,69870\n",
+ "200,32,584,0.0108,13974000,69870,69870\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,588,0.0106,14070000,70350,70350\n",
+ "200,32,588,0.0107,14070000,70350,70350\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,592,0.0106,14166000,70830,70830\n",
+ "200,32,592,0.0108,14166000,70830,70830\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,596,0.0106,14262000,71310,71310\n",
+ "200,32,596,0.0109,14262000,71310,71310\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,600,0.0108,14358000,71790,71790\n",
+ "200,32,600,0.0110,14358000,71790,71790\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,604,0.0109,14454000,72270,72270\n",
+ "200,32,604,0.0110,14454000,72270,72270\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,608,0.0109,14550000,72750,72750\n",
+ "200,32,608,0.0111,14550000,72750,72750\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,612,0.0109,14646000,73230,73230\n",
+ "200,32,612,0.0114,14646000,73230,73230\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,616,0.0111,14742000,73710,73710\n",
+ "200,32,616,0.0112,14742000,73710,73710\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,620,0.0111,14838000,74190,74190\n",
+ "200,32,620,0.0113,14838000,74190,74190\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,624,0.0112,14934000,74670,74670\n",
+ "200,32,624,0.0114,14934000,74670,74670\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,628,0.0112,15030000,75150,75150\n",
+ "200,32,628,0.0116,15030000,75150,75150\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,632,0.0112,15126000,75630,75630\n",
+ "200,32,632,0.0115,15126000,75630,75630\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,636,0.0114,15222000,76110,76110\n",
+ "200,32,636,0.0117,15222000,76110,76110\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,640,0.0114,15318000,76590,76590\n",
+ "200,32,640,0.0116,15318000,76590,76590\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,644,0.0114,15414000,77070,77070\n",
+ "200,32,644,0.0118,15414000,77070,77070\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,648,0.0115,15510000,77550,77550\n",
+ "200,32,648,0.0117,15510000,77550,77550\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,652,0.0117,15606000,78030,78030\n",
+ "200,32,652,0.0119,15606000,78030,78030\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,656,0.0117,15702000,78510,78510\n",
+ "200,32,656,0.0119,15702000,78510,78510\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,660,0.0117,15798000,78990,78990\n",
+ "200,32,660,0.0120,15798000,78990,78990\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,664,0.0118,15894000,79470,79470\n",
+ "200,32,664,0.0120,15894000,79470,79470\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,668,0.0120,15990000,79950,79950\n",
+ "200,32,668,0.0121,15990000,79950,79950\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,672,0.0120,16086000,80430,80430\n",
+ "200,32,672,0.0121,16086000,80430,80430\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,676,0.0121,16182000,80910,80910\n",
+ "200,32,676,0.0123,16182000,80910,80910\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,680,0.0120,16278000,81390,81390\n",
+ "200,32,680,0.0122,16278000,81390,81390\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,684,0.0121,16374000,81870,81870\n",
+ "200,32,684,0.0125,16374000,81870,81870\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,688,0.0122,16470000,82350,82350\n",
+ "200,32,688,0.0124,16470000,82350,82350\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,692,0.0122,16566000,82830,82830\n",
+ "200,32,692,0.0126,16566000,82830,82830\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,696,0.0124,16662000,83310,83310\n",
+ "200,32,696,0.0125,16662000,83310,83310\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,700,0.0124,16758000,83790,83790\n",
+ "200,32,700,0.0127,16758000,83790,83790\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,704,0.0124,16854000,84270,84270\n",
+ "200,32,704,0.0128,16854000,84270,84270\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,708,0.0125,16950000,84750,84750\n",
+ "200,32,708,0.0128,16950000,84750,84750\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,712,0.0125,17046000,85230,85230\n",
+ "200,32,712,0.0128,17046000,85230,85230\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,716,0.0126,17142000,85710,85710\n",
+ "200,32,716,0.0128,17142000,85710,85710\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,720,0.0126,17238000,86190,86190\n",
+ "200,32,720,0.0129,17238000,86190,86190\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,724,0.0127,17334000,86670,86670\n",
+ "200,32,724,0.0130,17334000,86670,86670\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,728,0.0128,17430000,87150,87150\n",
+ "200,32,728,0.0130,17430000,87150,87150\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,732,0.0130,17526000,87630,87630\n",
+ "200,32,732,0.0132,17526000,87630,87630\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,736,0.0129,17622000,88110,88110\n",
+ "200,32,736,0.0132,17622000,88110,88110\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,740,0.0129,17718000,88590,88590\n",
+ "200,32,740,0.0133,17718000,88590,88590\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,744,0.0130,17814000,89070,89070\n",
+ "200,32,744,0.0133,17814000,89070,89070\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,748,0.0131,17910000,89550,89550\n",
+ "200,32,748,0.0134,17910000,89550,89550\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,752,0.0132,18006000,90030,90030\n",
+ "200,32,752,0.0134,18006000,90030,90030\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,756,0.0132,18102000,90510,90510\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
+ "200,32,756,0.0136,18102000,90510,90510\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,760,0.0133,18198000,90990,90990\n",
+ "200,32,760,0.0136,18198000,90990,90990\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,764,0.0134,18294000,91470,91470\n",
+ "200,32,764,0.0136,18294000,91470,91470\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,768,0.0135,18390000,91950,91950\n",
+ "200,32,768,0.0137,18390000,91950,91950\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,772,0.0136,18486000,92430,92430\n",
+ "200,32,772,0.0139,18486000,92430,92430\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,776,0.0136,18582000,92910,92910\n",
+ "200,32,776,0.0139,18582000,92910,92910\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,780,0.0137,18678000,93390,93390\n",
+ "200,32,780,0.0139,18678000,93390,93390\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,784,0.0137,18774000,93870,93870\n",
+ "200,32,784,0.0140,18774000,93870,93870\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,788,0.0138,18870000,94350,94350\n",
+ "200,32,788,0.0140,18870000,94350,94350\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,792,0.0138,18966000,94830,94830\n",
+ "200,32,792,0.0142,18966000,94830,94830\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,796,0.0140,19062000,95310,95310\n",
+ "200,32,796,0.0142,19062000,95310,95310\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,800,0.0140,19158000,95790,95790\n",
+ "200,32,800,0.0144,19158000,95790,95790\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,804,0.0140,19254000,96270,96270\n",
+ "200,32,804,0.0143,19254000,96270,96270\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,808,0.0141,19350000,96750,96750\n",
+ "200,32,808,0.0144,19350000,96750,96750\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,812,0.0142,19446000,97230,97230\n",
+ "200,32,812,0.0145,19446000,97230,97230\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,816,0.0143,19542000,97710,97710\n",
+ "200,32,816,0.0145,19542000,97710,97710\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,820,0.0143,19638000,98190,98190\n",
+ "200,32,820,0.0146,19638000,98190,98190\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,824,0.0144,19734000,98670,98670\n",
+ "200,32,824,0.0147,19734000,98670,98670\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,828,0.0146,19830000,99150,99150\n",
+ "200,32,828,0.0147,19830000,99150,99150\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,832,0.0146,19926000,99630,99630\n",
+ "200,32,832,0.0148,19926000,99630,99630\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,836,0.0146,20022000,100110,100110\n",
+ "200,32,836,0.0151,20022000,100110,100110\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,840,0.0147,20118000,100590,100590\n",
+ "200,32,840,0.0150,20118000,100590,100590\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,844,0.0147,20214000,101070,101070\n",
+ "200,32,844,0.0150,20214000,101070,101070\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,848,0.0148,20310000,101550,101550\n",
+ "200,32,848,0.0151,20310000,101550,101550\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,852,0.0148,20406000,102030,102030\n",
+ "200,32,852,0.0152,20406000,102030,102030\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,856,0.0150,20502000,102510,102510\n",
+ "200,32,856,0.0152,20502000,102510,102510\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,860,0.0150,20598000,102990,102990\n",
+ "200,32,860,0.0152,20598000,102990,102990\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,864,0.0151,20694000,103470,103470\n",
+ "200,32,864,0.0153,20694000,103470,103470\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,868,0.0151,20790000,103950,103950\n",
+ "200,32,868,0.0154,20790000,103950,103950\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,872,0.0152,20886000,104430,104430\n",
+ "200,32,872,0.0155,20886000,104430,104430\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,876,0.0153,20982000,104910,104910\n",
+ "200,32,876,0.0155,20982000,104910,104910\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,880,0.0154,21078000,105390,105390\n",
+ "200,32,880,0.0157,21078000,105390,105390\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,884,0.0154,21174000,105870,105870\n",
+ "200,32,884,0.0157,21174000,105870,105870\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,888,0.0154,21270000,106350,106350\n",
+ "200,32,888,0.0158,21270000,106350,106350\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,892,0.0155,21366000,106830,106830\n",
+ "200,32,892,0.0158,21366000,106830,106830\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,896,0.0157,21462000,107310,107310\n",
+ "200,32,896,0.0159,21462000,107310,107310\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,900,0.0156,21558000,107790,107790\n",
+ "200,32,900,0.0161,21558000,107790,107790\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,904,0.0158,21654000,108270,108270\n",
+ "200,32,904,0.0162,21654000,108270,108270\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,908,0.0159,21750000,108750,108750\n",
+ "200,32,908,0.0161,21750000,108750,108750\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,912,0.0159,21846000,109230,109230\n",
+ "200,32,912,0.0163,21846000,109230,109230\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,916,0.0161,21942000,109710,109710\n",
+ "200,32,916,0.0164,21942000,109710,109710\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,920,0.0161,22038000,110190,110190\n",
+ "200,32,920,0.0165,22038000,110190,110190\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,924,0.0162,22134000,110670,110670\n",
+ "200,32,924,0.0164,22134000,110670,110670\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,928,0.0164,22230000,111150,111150\n",
+ "200,32,928,0.0166,22230000,111150,111150\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,932,0.0164,22326000,111630,111630\n",
+ "200,32,932,0.0166,22326000,111630,111630\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,936,0.0164,22422000,112110,112110\n",
+ "200,32,936,0.0167,22422000,112110,112110\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,940,0.0164,22518000,112590,112590\n",
+ "200,32,940,0.0168,22518000,112590,112590\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,944,0.0165,22614000,113070,113070\n",
+ "200,32,944,0.0168,22614000,113070,113070\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,948,0.0167,22710000,113550,113550\n",
+ "200,32,948,0.0169,22710000,113550,113550\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,952,0.0168,22806000,114030,114030\n",
+ "200,32,952,0.0170,22806000,114030,114030\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,956,0.0168,22902000,114510,114510\n",
+ "200,32,956,0.0170,22902000,114510,114510\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,960,0.0168,22998000,114990,114990\n",
+ "200,32,960,0.0171,22998000,114990,114990\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,964,0.0174,23094000,115470,115470\n",
+ "200,32,964,0.0176,23094000,115470,115470\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,968,0.0172,23190000,115950,115950\n",
+ "200,32,968,0.0176,23190000,115950,115950\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,972,0.0173,23286000,116430,116430\n",
+ "200,32,972,0.0177,23286000,116430,116430\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,976,0.0172,23382000,116910,116910\n",
+ "200,32,976,0.0177,23382000,116910,116910\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,980,0.0174,23478000,117390,117390\n",
+ "200,32,980,0.0178,23478000,117390,117390\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,984,0.0174,23574000,117870,117870\n",
+ "200,32,984,0.0178,23574000,117870,117870\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,988,0.0176,23670000,118350,118350\n",
+ "200,32,988,0.0179,23670000,118350,118350\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,992,0.0176,23766000,118830,118830\n",
+ "200,32,992,0.0180,23766000,118830,118830\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,996,0.0179,23862000,119310,119310\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
+ "200,32,996,0.0181,23862000,119310,119310\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,1000,0.0177,23958000,119790,119790\n",
+ "200,32,1000,0.0182,23958000,119790,119790\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,1004,0.0178,24054000,120270,120270\n",
+ "200,32,1004,0.0182,24054000,120270,120270\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,1008,0.0178,24150000,120750,120750\n",
+ "200,32,1008,0.0182,24150000,120750,120750\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,1012,0.0180,24246000,121230,121230\n",
+ "200,32,1012,0.0184,24246000,121230,121230\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,1016,0.0180,24342000,121710,121710\n",
+ "200,32,1016,0.0185,24342000,121710,121710\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,1020,0.0181,24438000,122190,122190\n",
+ "200,32,1020,0.0184,24438000,122190,122190\n",
"iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,1024,0.0178,24534000,122670,122670\n",
- "mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.vflop.bin.csv .\n"
+ "200,32,1024,0.0182,24534000,122670,122670\n",
+ "mv /gpfs/wolf/trn003/scratch/aherten//poisson2d.vflop.bin.csv .\n"
]
}
],
@@ -4253,51 +4402,225 @@
},
{
"cell_type": "code",
- "execution_count": 47,
+ "execution_count": 39,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>nx</th>\n",
+ " <th>iter</th>\n",
+ " <th>ny</th>\n",
+ " <th>Runtime</th>\n",
+ " <th>PM_SCALAR_FLOP_CMPL (total)</th>\n",
+ " <th>PM_SCALAR_FLOP_CMPL (min)</th>\n",
+ " <th>PM_SCALAR_FLOP_CMPL (max)</th>\n",
+ " <th>PM_VECTOR_FLOP_CMPL (total)</th>\n",
+ " <th>PM_VECTOR_FLOP_CMPL (min)</th>\n",
+ " <th>PM_VECTOR_FLOP_CMPL (max)</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>4</td>\n",
+ " <td>200</td>\n",
+ " <td>32</td>\n",
+ " <td>0.0010</td>\n",
+ " <td>96000</td>\n",
+ " <td>480</td>\n",
+ " <td>480</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>8</td>\n",
+ " <td>200</td>\n",
+ " <td>32</td>\n",
+ " <td>0.0011</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>150000</td>\n",
+ " <td>750</td>\n",
+ " <td>750</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>12</td>\n",
+ " <td>200</td>\n",
+ " <td>32</td>\n",
+ " <td>0.0012</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>246000</td>\n",
+ " <td>1230</td>\n",
+ " <td>1230</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>16</td>\n",
+ " <td>200</td>\n",
+ " <td>32</td>\n",
+ " <td>0.0012</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>342000</td>\n",
+ " <td>1710</td>\n",
+ " <td>1710</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>20</td>\n",
+ " <td>200</td>\n",
+ " <td>32</td>\n",
+ " <td>0.0013</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>438000</td>\n",
+ " <td>2190</td>\n",
+ " <td>2190</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " nx iter ny Runtime PM_SCALAR_FLOP_CMPL (total) \\\n",
+ "0 4 200 32 0.0010 96000 \n",
+ "1 8 200 32 0.0011 0 \n",
+ "2 12 200 32 0.0012 0 \n",
+ "3 16 200 32 0.0012 0 \n",
+ "4 20 200 32 0.0013 0 \n",
+ "\n",
+ " PM_SCALAR_FLOP_CMPL (min) PM_SCALAR_FLOP_CMPL (max) \\\n",
+ "0 480 480 \n",
+ "1 0 0 \n",
+ "2 0 0 \n",
+ "3 0 0 \n",
+ "4 0 0 \n",
+ "\n",
+ " PM_VECTOR_FLOP_CMPL (total) PM_VECTOR_FLOP_CMPL (min) \\\n",
+ "0 0 0 \n",
+ "1 150000 750 \n",
+ "2 246000 1230 \n",
+ "3 342000 1710 \n",
+ "4 438000 2190 \n",
+ "\n",
+ " PM_VECTOR_FLOP_CMPL (max) \n",
+ "0 0 \n",
+ "1 750 \n",
+ "2 1230 \n",
+ "3 1710 \n",
+ "4 2190 "
+ ]
+ },
+ "execution_count": 39,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"df_sflop = pd.read_csv(\"poisson2d.sflop.bin.csv\", skiprows=range(2, 50000, 2))\n",
"df_vflop = pd.read_csv(\"poisson2d.vflop.bin.csv\", skiprows=range(2, 50000, 2))\n",
- "df_flop = pd.concat([df_sflop.set_index(\"nx\"), df_vflop.set_index(\"nx\")[['PM_VECTOR_FLOP_CMPL (total)', 'PM_VECTOR_FLOP_CMPL (min)', ' PM_VECTOR_FLOP_CMPL (max)']]], axis=1).reset_index()"
+ "df_flop = pd.concat([df_sflop.set_index(\"nx\"), df_vflop.set_index(\"nx\")[['PM_VECTOR_FLOP_CMPL (total)', 'PM_VECTOR_FLOP_CMPL (min)', ' PM_VECTOR_FLOP_CMPL (max)']]], axis=1).reset_index()\n",
+ "df_flop.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "The name of the vector counter is a bit misleading; not floating point operations are measured but floating point instructions. To get *real* floating point operations, each value needs to be multiplied by the vector width (2). We can plot the values afterwards (non-interactive: `make graph_task4`)."
+ "Again, the name of the vector counter is a bit misleading; not floating point operations are measured but floating point instructions. To get *real* floating point operations, each value needs to be multiplied by the vector width (2). We can plot the values afterwards (non-interactive: `make graph_task4`)."
]
},
{
"cell_type": "code",
- "execution_count": 49,
+ "execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
- "common.normalize(df_flop, \"PM_SCALAR_FLOP_CMPL (min)\", \"Scalar FlOps / Loop Iteration\")\n",
- "common.normalize(df_flop, \"PM_VECTOR_FLOP_CMPL (min)\", \"Vector Instructions / Loop Iteration\")\n",
- "df_flop[\"Vector FlOps / Loop Iteration\"] = df_flop[\"Vector Instructions / Loop Iteration\"] * 2"
+ "df_flop[\"Grid Points\"] = df_flop[\"nx\"] * df_flop[\"ny\"]\n",
+ "df_flop[\"Vector FlOps (min)\"] = df_flop[\"PM_VECTOR_FLOP_CMPL (min)\"] * 2\n",
+ "df_flop[\"Scalar FlOps (min)\"] = df_flop[\"PM_SCALAR_FLOP_CMPL (min)\"]"
]
},
{
"cell_type": "code",
- "execution_count": 50,
+ "execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 1008x432 with 1 Axes>"
]
},
- "metadata": {},
+ "metadata": {
+ "needs_background": "light"
+ },
"output_type": "display_data"
}
],
"source": [
- "df_flop.set_index(\"nx\")[[\"Scalar FlOps / Loop Iteration\", \"Vector FlOps / Loop Iteration\"]].plot();"
+ "df_flop.set_index(\"Grid Points\")[[\"Scalar FlOps (min)\", \"Vector FlOps (min)\"]].plot();"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Counter Scalar FlOps (min) is proportional to the grid points (nx*ny) by a factor of -0.0003 (± 0.0002)\n",
+ "Counter Vector FlOps (min) is proportional to the grid points (nx*ny) by a factor of 7.5004 (± 0.0002)\n"
+ ]
+ }
+ ],
+ "source": [
+ "_fit, _cov = common.print_and_return_fit(\n",
+ " [\"Scalar FlOps (min)\", \"Vector FlOps (min)\"], \n",
+ " df_flop.set_index(\"Grid Points\"), \n",
+ " linear_function\n",
+ ")\n",
+ "fit_parameters = {**fit_parameters, **_fit}\n",
+ "fit_covariance = {**fit_covariance, **_cov}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "exercise": "solution"
+ },
+ "source": [
+ "Interesting! We seem to be using the vector registers of our system very well. Basically all operations are vector operations!"
]
},
{
@@ -4317,29 +4640,31 @@
},
{
"cell_type": "code",
- "execution_count": 66,
+ "execution_count": 56,
"metadata": {},
"outputs": [],
"source": [
- "I_flop_scalar = df_flop.set_index(\"nx\")[\"Scalar FlOps / Loop Iteration\"]\n",
- "I_flop_vector = df_flop.set_index(\"nx\")[\"Vector FlOps / Loop Iteration\"]\n",
- "I_mem_load = df_byte[\"Loads / Loop Iteration\"]\n",
- "I_mem_store = df_byte[\"Stores / Loop Iteration\"]"
+ "I_flop_scalar = df_flop.set_index(\"Grid Points\")[\"Scalar FlOps (min)\"]\n",
+ "I_flop_vector = df_flop.set_index(\"Grid Points\")[\"Vector FlOps (min)\"]\n",
+ "I_mem_load = df_byte[\"Loads\"]\n",
+ "I_mem_store = df_byte[\"Stores\"]"
]
},
{
"cell_type": "code",
- "execution_count": 75,
+ "execution_count": 57,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 1008x432 with 1 Axes>"
]
},
- "metadata": {},
+ "metadata": {
+ "needs_background": "light"
+ },
"output_type": "display_data"
}
],
@@ -4366,6 +4691,8 @@
"\n",
"If you still still have time, you might venture into your own benchmarking adventure.\n",
"\n",
+ "Maybe you noticed already, for instance in Task 2 C: At the very right to very large numbers of grid points, the behaviour of the graph changed. Something is happening there!\n",
+ "\n",
"\n",
"**TASK**: Revisit the counters measured above for a larger range of `nx`. Right now, we only studied `nx` until 1000. New effects appear above that value – partly only well above, though ($nx > 15000$).\n",
"\n",
@@ -4393,9 +4720,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.1"
+ "version": "3.7.0"
}
},
"nbformat": 4,
- "nbformat_minor": 2
-}
\ No newline at end of file
+ "nbformat_minor": 4
+}
diff --git a/2-Performance_Counters/Handson/.master/Makefile b/2-Performance_Counters/Handson/.master/Makefile
index 6f3849f4d54147c860ede7b0ff427176284a83ff..1db4b2f76ed5e40ed11f543e3d3837e46fa33080 100644
--- a/2-Performance_Counters/Handson/.master/Makefile
+++ b/2-Performance_Counters/Handson/.master/Makefile
@@ -82,32 +82,25 @@ graph_task2c: plot-task2c.pdf
graph_task4: plot-task4.pdf
graph_task4-2: plot-task4-2.pdf
plot-task1.pdf: poisson2d.ins_cyc.bin.csv
- @test "$$SC19_MODULE_ACTIVE_1" -eq "1" || "Please load the module of this task, sc18/handson1."
python3 -c "import graphing; graphing.task1()"
@test -n "$$DISPLAY" || "No X forwarding found. Either reconnect with X forwarding (-X / -Y) or download $@ with scp."
display $@
plot-task2a.pdf: poisson2d.ld_st.bin.csv
- @test "$$SC19_MODULE_ACTIVE_1" -eq "1" || "Please load the module of this task, sc18/handson1."
python3 -c "import graphing; graphing.task2a()"
display $@
plot-task2b.pdf: poisson2d.vld.bin.csv poisson2d.vst.bin.csv
- @test "$$SC19_MODULE_ACTIVE_1" -eq "1" || "Please load the module of this task, sc18/handson1."
python3 -c "import graphing; graphing.task2b()"
display $@
plot-task2b-2.pdf: poisson2d.vld.bin.csv poisson2d.vst.bin.csv poisson2d.ld_st.bin.csv
- @test "$$SC19_MODULE_ACTIVE_1" -eq "1" || "Please load the module of this task, sc18/handson1."
python3 -c "import graphing; graphing.task2b(bytes=True)"
display $@
plot-task2c.pdf: poisson2d.vld.bin.csv poisson2d.vst.bin.csv poisson2d.ld_st.bin.csv poisson2d.ins_cyc.bin.csv
- @test "$$SC19_MODULE_ACTIVE_1" -eq "1" || "Please load the module of this task, sc18/handson1."
python3 -c "import graphing; graphing.task2c()"
display $@
plot-task4.pdf: poisson2d.sflop.bin.csv poisson2d.vflop.bin.csv
- @test "$$SC19_MODULE_ACTIVE_1" -eq "1" || "Please load the module of this task, sc18/handson1."
python3 -c "import graphing; graphing.task4()"
display $@
plot-task4-2.pdf: poisson2d.sflop.bin.csv poisson2d.vflop.bin.csv
- @test "$$SC19_MODULE_ACTIVE_1" -eq "1" || "Please load the module of this task, sc18/handson1."
python3 -c "import graphing; graphing.task4(ai=True)"
display $@
diff --git a/2-Performance_Counters/Handson/.master/README.md b/2-Performance_Counters/Handson/.master/README.md
index 3ee0057da9448f49ece6e6afededeafee38ff907..8887dd78d9f2db7644f575df47c48c4c41ed655e 100644
--- a/2-Performance_Counters/Handson/.master/README.md
+++ b/2-Performance_Counters/Handson/.master/README.md
@@ -2,7 +2,5 @@
This folder holds the files for the first hands-on exercise about Performance Counters on POWER9.
-Make sure to load all modules of this session by typing `module load sc18/handson1` into the shell.
-
All task description is in an accompanying Jupyter Notebook. Open it interactively on Ascent with port forwarding. If that is impossible to do, use the static convert to HTML or PDF of the Notebook and follow along accordingly.
diff --git a/2-Performance_Counters/Handson/.master/common.py b/2-Performance_Counters/Handson/.master/common.py
index 1891a0341f369f7564b4a29b3f4a60e314f4bc9b..9033865e014fce9ece4137cdb11a42884acceae4 100644
--- a/2-Performance_Counters/Handson/.master/common.py
+++ b/2-Performance_Counters/Handson/.master/common.py
@@ -1,2 +1,22 @@
def normalize(df, old_column, new_column):
df[new_column] = df[old_column] / (df["ny"] * df["nx"])
+
+def print_and_return_fit(list_of_quantities, dataframe, function, format_value=">7.4f", format_uncertainty="f", _print=True):
+ """Use `curve_fit` to fit each quantity in `list_of_quantity` wrt to `dataframe.index`. Print (selectable) and return the result."""
+ import numpy as np
+ from scipy.optimize import curve_fit
+ _fit_parameters = {}
+ _fit_covariance = {}
+ _quantity_padding = np.max([len(_str) for _str in list_of_quantities])
+ for quantity in list_of_quantities:
+ _fit_parameters[quantity], _fit_covariance[quantity] = curve_fit(function, dataframe.index, dataframe[quantity])
+ if (_print):
+ print("Counter {:>{_quantity_padding}} is proportional to the grid points (nx*ny) by a factor of {:{format_value}} (± {:{format_uncertainty}})".format(
+ quantity,
+ _fit_parameters[quantity][0],
+ np.sqrt(np.diag(_fit_covariance[quantity]))[0],
+ _quantity_padding=_quantity_padding,
+ format_value=format_value,
+ format_uncertainty=format_uncertainty
+ ))
+ return (_fit_parameters, _fit_covariance)
\ No newline at end of file
diff --git a/2-Performance_Counters/Handson/.master/copyNotebook.mk b/2-Performance_Counters/Handson/.master/copyNotebook.mk
index a90882b672855d41aa4e36861cf5fe6d5f248d6f..8432d91d30fcf721a6b0a0d12a4f462c94a897e5 100755
--- a/2-Performance_Counters/Handson/.master/copyNotebook.mk
+++ b/2-Performance_Counters/Handson/.master/copyNotebook.mk
@@ -21,17 +21,17 @@ solutions: $(TGT_SOLUTIONS)
tasks: $(TGT_BLANK)
$(addprefix ../,$(addsuffix .html,$(basename $(SRC)))): $(SRC)
- jupyter nbconvert --to html --output $@ --ClearOutputPreprocessor.enabled=True $<
+ notebook-splitter --remove solution --keep task $< | jupyter nbconvert --to html --output $@ --ClearOutputPreprocessor.enabled=True --stdin
$(addprefix ../,$(addsuffix .pdf,$(basename $(SRC)))): $(SRC)
- jupyter nbconvert --to pdf --output $@ --template better-article.tplx --ClearOutputPreprocessor.enabled=True $<
+ notebook-splitter --remove solution --keep task $< | jupyter nbconvert --to pdf --output $@ --template better-article.tplx --ClearOutputPreprocessor.enabled=True --stdin
mv $@.pdf $@
$(addprefix ../,$(SRC)): $(SRC)
- jupyter nbconvert --to ipynb --output $@ --ClearOutputPreprocessor.enabled=True $<
+ notebook-splitter --remove solution --keep task $< | jupyter nbconvert --to ipynb --output $@ --ClearOutputPreprocessor.enabled=True --stdin
$(addprefix ../Solutions/,$(addsuffix .html,$(basename $(SRC)))): $(SRC)
- jupyter nbconvert --to html --output $@ $<
+ notebook-splitter --remove task --keep solution $< | jupyter nbconvert --to html --output $@ --stdin
$(addprefix ../Solutions/,$(addsuffix .pdf,$(basename $(SRC)))): $(SRC)
- jupyter nbconvert --to pdf --output $@ --template better-article.tplx $<
+ notebook-splitter --remove task --keep solution $< | jupyter nbconvert --to pdf --output $@ --template better-article.tplx --stdin
mv $@.pdf $@
$(addprefix ../Solutions/,$(SRC)): $(SRC)
- cp $< $@
+ notebook-splitter --remove task --keep solution $< -o $@
diff --git a/2-Performance_Counters/Handson/Hands-On-Performance-Counters.html b/2-Performance_Counters/Handson/Hands-On-Performance-Counters.html
index 9be777b398d8d1c7dc240c0021aa1957eec3b17b..8db553e9c7efa7a2ed66b3b43569c67ec4a20af7 100644
--- a/2-Performance_Counters/Handson/Hands-On-Performance-Counters.html
+++ b/2-Performance_Counters/Handson/Hands-On-Performance-Counters.html
@@ -2,7 +2,7 @@
<html>
<head><meta charset="utf-8" />
-<title>Hands-On-Performance-Counters</title>
+<title>Notebook</title>
<script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/2.0.3/jquery.min.js"></script>
@@ -13116,7 +13116,7 @@ div#notebook {
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
-<h1 id="Hands-On:-Performance-Counters">Hands-On: Performance Counters<a class="anchor-link" href="#Hands-On:-Performance-Counters">¶</a></h1><p>This Notebook is part of the exercises for the SC18 Tutorial »Application Porting and Optimization on GPU-accelerated POWER Architectures«. It is to be run on a POWER9 machine; in the tutorial: on Ascent, the POWER9 training cluster of Oak Ridge National Lab.</p>
+<h1 id="Hands-On:-Performance-Counters">Hands-On: Performance Counters<a class="anchor-link" href="#Hands-On:-Performance-Counters">¶</a></h1><p>This Notebook is part of the exercises for the SC19 Tutorial »Application Porting and Optimization on GPU-accelerated POWER Architectures«. It is to be run on a POWER9 machine; in the tutorial: on Ascent, the POWER9 training cluster of Oak Ridge National Lab.</p>
<p>This Notebook can be run interactively on Ascent. If this capability is unavailable to you, use it as a description for executing the tasks on Ascent via a shell access. During data evaluation, the Notebook mentions the corresponding commands to execute in case you are not able to run the Notebook interactively directly on Ascent.</p>
<h2 id="Table-of-Contents">Table of Contents<a class="anchor-link" href="#Table-of-Contents">¶</a></h2><p><a name="toc"></a></p>
<ul>
@@ -13149,15 +13149,29 @@ div#notebook {
<span class="p">}</span>
<span class="p">}</span>
</pre></div>
-<p>After <code>PAPI_add_named_event()</code> is used to add named PMU events outside of the relaxation iteration, <code>PAPI_start()</code>
+<p>The code is instrumented using PAPI. The API routine <code>PAPI_add_named_event()</code> is used to add <em>named</em> PMU events outside of the relaxation iteration. After that, calls to <code>PAPI_start()</code>
and <code>PAPI_stop()</code> can be used to count how often a PMU event is incremented.</p>
-<p>For the first task, we will measure quantities often used to characterize an application, cycles and instructions.</p>
-<p><strong>TASK</strong>: Please measure counters for completed instructions and run cycles. See the TODOs in <a href="/edit/Tasks/poisson2d.ins_cyc.c"><code>poisson2d.ins_cyc.c</code></a>. Either edit with Jupyter capabilities by clicking on the link of the file or use a dedicated editor (<code>vim</code> is available). The names of the counters to be implemented are <code>PM_INST_CMPL</code> and <code>PM_RUN_CYC</code>.</p>
-<p>After changing the source code, compile it with <code>make task1</code> or by executing the following cell (we need to change directories first, though).</p>
+<p>For the first task, we will measure quantities often used to characterize an application: cycles and instructions.</p>
+<p><strong>TASK</strong>: Please measure counters for completed instructions and run cycles. See the TODOs in file <a href="poisson2d.ins_cyc.c"><code>poisson2d.ins_cyc.c</code></a>. You can either edit the files with Jupyter capabilities by clicking on the link of the file or selecting it in the file drawer on the left; or use a dedicated editor on the system(<code>vim</code> is available). The names of the counters to be implemented are <code>PM_INST_CMPL</code> and <code>PM_RUN_CYC</code>.</p>
+<p>After changing the source code, compile it with <code>make task1</code> or by executing the following cell (we need to change directories first, though).<br>
+<em>(Using the <code>Makefile</code> we have hidden quite a few intricacies from you in order to focus on the relevant content at hand. Don't worry too much about it right now – we'll un-hide it gradually during the course of the tutorial.)</em></p>
<p><a href="#toc">Back to top</a></p>
</div>
</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [ ]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="o">!</span>pwd
+</pre></div>
+
+ </div>
+</div>
+</div>
+
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
@@ -13189,7 +13203,7 @@ and <code>PAPI_stop()</code> can be used to count how often a PMU event is incre
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
-<p>Make sure your program is measuring correctly, by invoking it, for instance with these arguments: <code>./poisson2d.ins_cyc.bin 100 64 32</code> – see the next cell. The <code>100</code> specifies the number of iterations to perform, <code>64</code> and <code>32</code> are the size of the grid in y and x direction, respectively.</p>
+<p>Before we launch our measurement campaign we should make sure that the program is measuring correctly. Let's invoking it, for instance, with these arguments: <code>./poisson2d.ins_cyc.bin 100 64 32</code> – see the next cell. The <code>100</code> specifies the number of iterations to perform, <code>64</code> and <code>32</code> are the size of the grid in y and x direction, respectively.</p>
</div>
</div>
@@ -13211,7 +13225,8 @@ and <code>PAPI_stop()</code> can be used to count how often a PMU event is incre
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
-<p>For the following runs, we are going to use Ascent's compute backend nodes which are not shared amongst users and also have six GPUs available. We use the available batch scheduler <em>IBM Spectrum LSF</em> for this. For convenience, a call to the batch submission system is stored in the environment variable <code>$SC18_SUBMIT_CMD</code>. You are welcome to adapt it once you get more familiar with the system.</p>
+<p>Alright! That should return a comma-seperated list of measurements.</p>
+<p>For the following runs, we are going to use Ascent's compute backend nodes which are not shared amongst users and also have six GPUs available (each!). We use the available batch scheduler <em>IBM Spectrum LSF</em> for this. For convenience, a call to the batch submission system is stored in the environment variable <code>$SC19_SUBMIT_CMD</code>. You are welcome to adapt it once you get more familiar with the system.</p>
<p>For now, we want to run our first benchmarking run and measure cycles and instructions for different data sizes, as a function of <code>nx</code>. The Makefile holds a target for this, call it with <code>make bench_task1</code>:</p>
</div>
@@ -13233,7 +13248,7 @@ and <code>PAPI_stop()</code> can be used to count how often a PMU event is incre
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
-<p>Once the run is completed, let's have a look at the data!</p>
+<p>Once the run is completed, let's study the data!</p>
<p>This can be done best in the interactive version of the Jupyter Notebook. In case this version of the description is unavailable to you, call the Makefile target <code>make graph_task1</code> (either with X forwarding, or download the resulting PDF).</p>
</div>
@@ -13244,7 +13259,8 @@ and <code>PAPI_stop()</code> can be used to count how often a PMU event is incre
<div class="prompt input_prompt">In [ ]:</div>
<div class="inner_cell">
<div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
+<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">common</span>
@@ -13257,6 +13273,27 @@ and <code>PAPI_stop()</code> can be used to count how often a PMU event is incre
</div>
</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div><div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Execute the following cell if you want to switch to color-blind-safer colors</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [ ]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sns</span><span class="o">.</span><span class="n">set_palette</span><span class="p">(</span><span class="s2">"colorblind"</span><span class="p">)</span>
+</pre></div>
+
+ </div>
+</div>
+</div>
+
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
@@ -13265,8 +13302,7 @@ and <code>PAPI_stop()</code> can be used to count how often a PMU event is incre
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">'figure.figsize'</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="mi">14</span><span class="p">,</span> <span class="mi">6</span><span class="p">]</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">"poisson2d.ins_cyc.bin.csv"</span><span class="p">,</span> <span class="n">skiprows</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">50000</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span> <span class="c1"># Read in the CSV file from the bench run; parse with Pandas</span>
-<span class="n">common</span><span class="o">.</span><span class="n">normalize</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="s2">"PM_INST_CMPL (min)"</span><span class="p">,</span> <span class="s2">"Instructions / Loop Iteration"</span><span class="p">)</span> <span class="c1"># Normalize to each grid cell</span>
-<span class="n">common</span><span class="o">.</span><span class="n">normalize</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="s2">"PM_RUN_CYC (min)"</span><span class="p">,</span> <span class="s2">"Cycles / Loop Iteration"</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">"Grid Points"</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">"nx"</span><span class="p">]</span> <span class="o">*</span> <span class="n">df</span><span class="p">[</span><span class="s2">"ny"</span><span class="p">]</span> <span class="c1"># Add a new column of the number of grid points (the product of nx and ny)</span>
<span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span> <span class="c1"># Display the head of the Pandas dataframe</span>
</pre></div>
@@ -13274,16 +13310,95 @@ and <code>PAPI_stop()</code> can be used to count how often a PMU event is incre
</div>
</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div><div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Let's have a look at the counters we've just measured and see how they scaling with increasing number of grid points.</p>
+<p><em>In the following, we are always using the minimal value of the counter (indicated by »(min)«) as this should give us an estimate of the best achievable result of the architecture.</em></p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [ ]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="p">(</span><span class="n">ax1</span><span class="p">,</span> <span class="n">ax2</span><span class="p">)</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">df</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"Grid Points"</span><span class="p">)[</span><span class="s2">"PM_RUN_CYC (min)"</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">True</span><span class="p">);</span>
+<span class="n">df</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"Grid Points"</span><span class="p">)[</span><span class="s2">"PM_INST_CMPL (min)"</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax2</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">True</span><span class="p">);</span>
+</pre></div>
+
+ </div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div><div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Although some slight variations can be seen for run cycles for many grid points, the correlation looks quite linear (as one would naively expect). Let's test that by fitting a linear function!</p>
+<p><em>The details of the fitting have been extracted into dedicated function, <code>print_and_return_fit()</code>, of the <code>common.py</code> helper file. If you're interested, <a href="common.py">go have a look at it</a>.</em></p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [ ]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">linear_function</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">):</span>
+ <span class="k">return</span> <span class="n">a</span><span class="o">*</span><span class="n">x</span><span class="o">+</span><span class="n">b</span>
+</pre></div>
+
+ </div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [ ]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fit_parameters</span><span class="p">,</span> <span class="n">fit_covariance</span> <span class="o">=</span> <span class="n">common</span><span class="o">.</span><span class="n">print_and_return_fit</span><span class="p">(</span>
+ <span class="p">[</span><span class="s2">"PM_RUN_CYC (min)"</span><span class="p">,</span> <span class="s2">"PM_INST_CMPL (min)"</span><span class="p">],</span>
+ <span class="n">df</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"Grid Points"</span><span class="p">),</span>
+ <span class="n">linear_function</span><span class="p">,</span>
+ <span class="n">format_uncertainty</span><span class="o">=</span><span class="s2">".4f"</span>
+<span class="p">)</span>
+</pre></div>
+
+ </div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div><div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Let's overlay our fits to the graphs from before.</p>
+
+</div>
+</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [ ]:</div>
<div class="inner_cell">
<div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Plot Cycles and Instructions - both per grid cell</span>
-<span class="n">fig</span><span class="p">,</span> <span class="p">(</span><span class="n">ax1</span><span class="p">,</span> <span class="n">ax2</span><span class="p">)</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="n">df</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"nx"</span><span class="p">)[</span><span class="s2">"Cycles / Loop Iteration"</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">True</span><span class="p">);</span>
-<span class="n">df</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"nx"</span><span class="p">)[</span><span class="s2">"Instructions / Loop Iteration"</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax2</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">True</span><span class="p">);</span>
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="p">(</span><span class="n">ax1</span><span class="p">,</span> <span class="n">ax2</span><span class="p">)</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="k">for</span> <span class="n">ax</span><span class="p">,</span> <span class="n">pmu_counter</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">([</span><span class="n">ax1</span><span class="p">,</span> <span class="n">ax2</span><span class="p">],</span> <span class="p">[</span><span class="s2">"PM_RUN_CYC (min)"</span><span class="p">,</span> <span class="s2">"PM_INST_CMPL (min)"</span><span class="p">]):</span>
+ <span class="n">df</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"Grid Points"</span><span class="p">)[</span><span class="n">pmu_counter</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">True</span><span class="p">);</span>
+ <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
+ <span class="n">df</span><span class="p">[</span><span class="s2">"Grid Points"</span><span class="p">],</span>
+ <span class="n">linear_function</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s2">"Grid Points"</span><span class="p">],</span> <span class="o">*</span><span class="n">fit_parameters</span><span class="p">[</span><span class="n">pmu_counter</span><span class="p">]),</span>
+ <span class="n">linestyle</span><span class="o">=</span><span class="s2">"--"</span><span class="p">,</span>
+ <span class="n">label</span><span class="o">=</span><span class="s2">"Fit: </span><span class="si">{:.2f}</span><span class="s2"> * x + </span><span class="si">{:.2f}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="o">*</span><span class="n">fit_parameters</span><span class="p">[</span><span class="n">pmu_counter</span><span class="p">])</span>
+ <span class="p">)</span>
+ <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">();</span>
</pre></div>
</div>
@@ -13294,7 +13409,38 @@ and <code>PAPI_stop()</code> can be used to count how often a PMU event is incre
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
-<p>What is your result? What value do the graphs come asymptotically close too?</p>
+<p>Please execute the next cell to summarize the first task.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [ ]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">"The algorithm under investigation runs about </span><span class="si">{:.0f}</span><span class="s2"> cycles and executes about </span><span class="si">{:.0f}</span><span class="s2"> instructions per grid point"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+ <span class="o">*</span><span class="p">[</span><span class="n">fit_parameters</span><span class="p">[</span><span class="n">pmu_counter</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">pmu_counter</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">"PM_RUN_CYC (min)"</span><span class="p">,</span> <span class="s2">"PM_INST_CMPL (min)"</span><span class="p">]]</span>
+<span class="p">))</span>
+</pre></div>
+
+ </div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div><div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><strong>Bonus:</strong></p>
+<p>The linear fits also calculate a y intersection (»<code>b</code>«). How do you interpret this value?</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div><div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
<p>We are revisiting the graph in a little while.</p>
<p><a href="#toc">Back to top</a></p>
@@ -13307,7 +13453,8 @@ and <code>PAPI_stop()</code> can be used to count how often a PMU event is incre
<h2 id="Task-2:-Measuring-Loads-and-Stores">Task 2: Measuring Loads and Stores<a class="anchor-link" href="#Task-2:-Measuring-Loads-and-Stores">¶</a></h2><p><a name="task2"></a></p>
<p>Looking at the source code, how many loads and stores from / to memory do you expect? Have a look at the loop which we instrumented.</p>
<p>Let's compare your estimate to what the system actually does!</p>
-<p><a name="task2-a"></a><strong>TASK A</strong>: Please measure counters for loads and stores. See the TODOs in <a href="/edit/Tasks/poisson2d.ld_st.c"><code>poisson2d.ld_st.c</code></a>. This time, implement <code>PM_LD_CMPL</code> and <code>PM_ST_CMPL</code>.</p>
+<h3 id="Task-A">Task A<a class="anchor-link" href="#Task-A">¶</a></h3><p><a name="task2-a"></a></p>
+<p>Please measure counters for loads and stores. See the TODOs in <a href="/edit/Tasks/poisson2d.ld_st.c"><code>poisson2d.ld_st.c</code></a>. This time, implement <code>PM_LD_CMPL</code> and <code>PM_ST_CMPL</code>.</p>
<p>Compile with <code>make task2</code>, test your program with a single run with <code>make run_task2</code>, and then finally submit a benchmarking run to the batch system with <code>make bench_task2</code>. The following cell will take care of all this.</p>
<p><a href="#toc">Back to top</a></p>
@@ -13330,7 +13477,7 @@ and <code>PAPI_stop()</code> can be used to count how often a PMU event is incre
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
-<p>Once the run finished, let's plot it again with the following cell (non-interactive: <code>make graph_task2a</code>).</p>
+<p>Once the run finished, let's plot it again in the course of the following cells (non-interactive: <code>make graph_task2a</code>).</p>
</div>
</div>
@@ -13341,8 +13488,7 @@ and <code>PAPI_stop()</code> can be used to count how often a PMU event is incre
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_ldst</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">"poisson2d.ld_st.bin.csv"</span><span class="p">,</span> <span class="n">skiprows</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">50000</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
-<span class="n">common</span><span class="o">.</span><span class="n">normalize</span><span class="p">(</span><span class="n">df_ldst</span><span class="p">,</span> <span class="s2">"PM_LD_CMPL (min)"</span><span class="p">,</span> <span class="s2">"Loads / Loop Iteration"</span><span class="p">)</span>
-<span class="n">common</span><span class="o">.</span><span class="n">normalize</span><span class="p">(</span><span class="n">df_ldst</span><span class="p">,</span> <span class="s2">"PM_ST_CMPL (min)"</span><span class="p">,</span> <span class="s2">"Stores / Loop Iteration"</span><span class="p">)</span>
+<span class="n">df_ldst</span><span class="p">[</span><span class="s2">"Grid Points"</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_ldst</span><span class="p">[</span><span class="s2">"nx"</span><span class="p">]</span> <span class="o">*</span> <span class="n">df_ldst</span><span class="p">[</span><span class="s2">"ny"</span><span class="p">]</span>
<span class="n">df_ldst</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>
@@ -13357,8 +13503,66 @@ and <code>PAPI_stop()</code> can be used to count how often a PMU event is incre
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="p">(</span><span class="n">ax1</span><span class="p">,</span> <span class="n">ax2</span><span class="p">)</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="n">df_ldst</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"nx"</span><span class="p">)[</span><span class="s2">"Loads / Loop Iteration"</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">True</span><span class="p">);</span>
-<span class="n">df_ldst</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"nx"</span><span class="p">)[</span><span class="s2">"Stores / Loop Iteration"</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax2</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">True</span><span class="p">);</span>
+<span class="n">df_ldst</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"Grid Points"</span><span class="p">)[</span><span class="s2">"PM_LD_CMPL (min)"</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">True</span><span class="p">);</span>
+<span class="n">df_ldst</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"Grid Points"</span><span class="p">)[</span><span class="s2">"PM_ST_CMPL (min)"</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax2</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">True</span><span class="p">);</span>
+</pre></div>
+
+ </div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div><div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Also this behaviour looks – at a first glance – linear. We can again fit a first-order polynom (and re-use our previously defined function <code>curve_fit</code>)!</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [ ]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">_fit</span><span class="p">,</span> <span class="n">_cov</span> <span class="o">=</span> <span class="n">common</span><span class="o">.</span><span class="n">print_and_return_fit</span><span class="p">(</span>
+ <span class="p">[</span><span class="s2">"PM_LD_CMPL (min)"</span><span class="p">,</span> <span class="s2">"PM_ST_CMPL (min)"</span><span class="p">],</span>
+ <span class="n">df_ldst</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"Grid Points"</span><span class="p">),</span>
+ <span class="n">linear_function</span><span class="p">,</span>
+ <span class="n">format_value</span><span class="o">=</span><span class="s2">".4f"</span>
+<span class="p">)</span>
+<span class="n">fit_parameters</span> <span class="o">=</span> <span class="p">{</span><span class="o">**</span><span class="n">fit_parameters</span><span class="p">,</span> <span class="o">**</span><span class="n">_fit</span><span class="p">}</span>
+<span class="n">fit_covariance</span> <span class="o">=</span> <span class="p">{</span><span class="o">**</span><span class="n">fit_covariance</span><span class="p">,</span> <span class="o">**</span><span class="n">_cov</span><span class="p">}</span>
+</pre></div>
+
+ </div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div><div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Let's overlay this in one common plot:</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [ ]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="p">(</span><span class="n">ax1</span><span class="p">,</span> <span class="n">ax2</span><span class="p">)</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="k">for</span> <span class="n">ax</span><span class="p">,</span> <span class="n">pmu_counter</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">([</span><span class="n">ax1</span><span class="p">,</span> <span class="n">ax2</span><span class="p">],</span> <span class="p">[</span><span class="s2">"PM_LD_CMPL (min)"</span><span class="p">,</span> <span class="s2">"PM_ST_CMPL (min)"</span><span class="p">]):</span>
+ <span class="n">df_ldst</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"Grid Points"</span><span class="p">)[</span><span class="n">pmu_counter</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">True</span><span class="p">);</span>
+ <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
+ <span class="n">df_ldst</span><span class="p">[</span><span class="s2">"Grid Points"</span><span class="p">],</span>
+ <span class="n">linear_function</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s2">"Grid Points"</span><span class="p">],</span> <span class="o">*</span><span class="n">fit_parameters</span><span class="p">[</span><span class="n">pmu_counter</span><span class="p">]),</span>
+ <span class="n">linestyle</span><span class="o">=</span><span class="s2">"--"</span><span class="p">,</span>
+ <span class="n">label</span><span class="o">=</span><span class="s2">"Fit: </span><span class="si">{:.2f}</span><span class="s2"> * x + </span><span class="si">{:.2f}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="o">*</span><span class="n">fit_parameters</span><span class="p">[</span><span class="n">pmu_counter</span><span class="p">])</span>
+ <span class="p">)</span>
+ <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">();</span>
</pre></div>
</div>
@@ -13370,8 +13574,9 @@ and <code>PAPI_stop()</code> can be used to count how often a PMU event is incre
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>Did you expect more?</p>
-<p>The reason is simple: Among the load and store instructions counted by <code>PM_LD_CMPL</code> and <code>PM_ST_CMPL</code> are vector instructions which can load and store multiple (two) values at a time. To see how many <em>bytes</em> are loaded and stored, we need to measure counters for vectorized loads and stores as well.</p>
-<p><a name="task2-b"></a><strong>TASK B</strong>: Please measure counters for <em>vectorized</em> loads and <em>vectorized</em> stores. See the TODOs in <a href="/edit/Tasks/poisson2d.vld.c"><code>poisson2d.vld.c</code></a> and <a href="/edit/Tasks/poisson2d.vst.c"><code>poisson2d.vst.c</code></a> (<em>Note: These vector counters can not be measured together and need separate files and runs</em>). Can you find out the name of the counters yourself, using <code>papi_native_avail | grep VECTOR_</code>?</p>
+<p>The reason is simple: Among the load and store instructions counted by <code>PM_LD_CMPL</code> and <code>PM_ST_CMPL</code> are vector instructions which can load and store multiple (in this case: two) values at a time. To see how many <em>bytes</em> are loaded and stored, we need to measure counters for vectorized loads and stores as well.</p>
+<h3 id="TASK-B">TASK B<a class="anchor-link" href="#TASK-B">¶</a></h3><p><a name="task2-b"></a></p>
+<p>Please measure counters for <em>vectorized</em> loads and <em>vectorized</em> stores. See the TODOs in <a href="poisson2d.vld.c"><code>poisson2d.vld.c</code></a> and <a href="poisson2d.vst.c"><code>poisson2d.vst.c</code></a> (<em>Note: These vector counters can not be measured together and need separate files and runs</em>). Can you find out the name of the counters yourself, using <code>papi_native_avail | grep VECTOR_</code>?</p>
<p>Compile, test, and bench-run your program again.</p>
<p><a href="#toc">Back to top</a></p>
@@ -13416,7 +13621,7 @@ and <code>PAPI_stop()</code> can be used to count how often a PMU event is incre
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>Let's plot it again, as soon as the run finishes! Non-interactively, call <code>graph_task2b</code>.</p>
-<p><em>We need to read in two CSV files now, which we combine to one common dataframe <code>df_vldvst</code>.</em></p>
+<p><em>Because we couldn't measure the two vector counters at the same time, we have two CSV files to read in now. We combine them into one common dataframe <code>df_vldvst</code> in the following.</em></p>
</div>
</div>
@@ -13441,8 +13646,7 @@ and <code>PAPI_stop()</code> can be used to count how often a PMU event is incre
<div class="prompt input_prompt">In [ ]:</div>
<div class="inner_cell">
<div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">common</span><span class="o">.</span><span class="n">normalize</span><span class="p">(</span><span class="n">df_vldvst</span><span class="p">,</span> <span class="s2">"PM_VECTOR_LD_CMPL (min)"</span><span class="p">,</span> <span class="s2">"Vector Loads / Loop Iteration"</span><span class="p">)</span>
-<span class="n">common</span><span class="o">.</span><span class="n">normalize</span><span class="p">(</span><span class="n">df_vldvst</span><span class="p">,</span> <span class="s2">"PM_VECTOR_ST_CMPL (min)"</span><span class="p">,</span> <span class="s2">"Vector Stores / Loop Iteration"</span><span class="p">)</span>
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_vldvst</span><span class="p">[</span><span class="s2">"Grid Points"</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_vldvst</span><span class="p">[</span><span class="s2">"nx"</span><span class="p">]</span> <span class="o">*</span> <span class="n">df_vldvst</span><span class="p">[</span><span class="s2">"ny"</span><span class="p">]</span>
<span class="n">df_vldvst</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>
@@ -13457,8 +13661,58 @@ and <code>PAPI_stop()</code> can be used to count how often a PMU event is incre
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="p">(</span><span class="n">ax1</span><span class="p">,</span> <span class="n">ax2</span><span class="p">)</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="n">df_vldvst</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"nx"</span><span class="p">)[</span><span class="s2">"Vector Loads / Loop Iteration"</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">True</span><span class="p">);</span>
-<span class="n">df_vldvst</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"nx"</span><span class="p">)[</span><span class="s2">"Vector Stores / Loop Iteration"</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax2</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">True</span><span class="p">);</span>
+<span class="n">df_vldvst</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"Grid Points"</span><span class="p">)[</span><span class="s2">"PM_VECTOR_LD_CMPL (min)"</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">True</span><span class="p">);</span>
+<span class="n">df_vldvst</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"Grid Points"</span><span class="p">)[</span><span class="s2">"PM_VECTOR_ST_CMPL (min)"</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax2</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">True</span><span class="p">);</span>
+</pre></div>
+
+ </div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div><div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Also here seems to be a linear correlation. Let's do our fitting and plot directly.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [ ]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">_fit</span><span class="p">,</span> <span class="n">_cov</span> <span class="o">=</span> <span class="n">common</span><span class="o">.</span><span class="n">print_and_return_fit</span><span class="p">(</span>
+ <span class="p">[</span><span class="s2">"PM_VECTOR_LD_CMPL (min)"</span><span class="p">,</span> <span class="s2">"PM_VECTOR_ST_CMPL (min)"</span><span class="p">],</span>
+ <span class="n">df_vldvst</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"Grid Points"</span><span class="p">),</span>
+ <span class="n">linear_function</span><span class="p">,</span>
+ <span class="n">format_value</span><span class="o">=</span><span class="s2">".4f"</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">fit_parameters</span> <span class="o">=</span> <span class="p">{</span><span class="o">**</span><span class="n">fit_parameters</span><span class="p">,</span> <span class="o">**</span><span class="n">_fit</span><span class="p">}</span>
+<span class="n">fit_covariance</span> <span class="o">=</span> <span class="p">{</span><span class="o">**</span><span class="n">fit_covariance</span><span class="p">,</span> <span class="o">**</span><span class="n">_cov</span><span class="p">}</span>
+</pre></div>
+
+ </div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [ ]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="p">(</span><span class="n">ax1</span><span class="p">,</span> <span class="n">ax2</span><span class="p">)</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="k">for</span> <span class="n">ax</span><span class="p">,</span> <span class="n">pmu_counter</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">([</span><span class="n">ax1</span><span class="p">,</span> <span class="n">ax2</span><span class="p">],</span> <span class="p">[</span><span class="s2">"PM_VECTOR_LD_CMPL (min)"</span><span class="p">,</span> <span class="s2">"PM_VECTOR_ST_CMPL (min)"</span><span class="p">]):</span>
+ <span class="n">df_vldvst</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"Grid Points"</span><span class="p">)[</span><span class="n">pmu_counter</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">True</span><span class="p">);</span>
+ <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
+ <span class="n">df_vldvst</span><span class="p">[</span><span class="s2">"Grid Points"</span><span class="p">],</span>
+ <span class="n">linear_function</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s2">"Grid Points"</span><span class="p">],</span> <span class="o">*</span><span class="n">fit_parameters</span><span class="p">[</span><span class="n">pmu_counter</span><span class="p">]),</span>
+ <span class="n">linestyle</span><span class="o">=</span><span class="s2">"--"</span><span class="p">,</span>
+ <span class="n">label</span><span class="o">=</span><span class="s2">"Fit: </span><span class="si">{:.2f}</span><span class="s2"> * x + </span><span class="si">{:.2f}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="o">*</span><span class="n">fit_parameters</span><span class="p">[</span><span class="n">pmu_counter</span><span class="p">])</span>
+ <span class="p">)</span>
+ <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">();</span>
</pre></div>
</div>
@@ -13492,32 +13746,51 @@ n_\text{ld}^\text{scalar} &= n_\text{ld}^\text{total} - n_\text{ld}^\text{ve
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_byte</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">()</span>
-<span class="n">df_byte</span><span class="p">[</span><span class="s2">"Loads / Loop Iteration"</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">df_vldvst</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"nx"</span><span class="p">)[</span><span class="s2">"Vector Loads / Loop Iteration"</span><span class="p">]</span> <span class="o">+</span> <span class="n">df_ldst</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"nx"</span><span class="p">)[</span><span class="s2">"Loads / Loop Iteration"</span><span class="p">])</span><span class="o">*</span><span class="mi">8</span>
-<span class="n">df_byte</span><span class="p">[</span><span class="s2">"Stores / Loop Iteration"</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">df_vldvst</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"nx"</span><span class="p">)[</span><span class="s2">"Vector Stores / Loop Iteration"</span><span class="p">]</span> <span class="o">+</span> <span class="n">df_ldst</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"nx"</span><span class="p">)[</span><span class="s2">"Stores / Loop Iteration"</span><span class="p">])</span><span class="o">*</span><span class="mi">8</span>
+<span class="n">df_byte</span><span class="p">[</span><span class="s2">"Loads"</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">df_vldvst</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"Grid Points"</span><span class="p">)[</span><span class="s2">"PM_VECTOR_LD_CMPL (min)"</span><span class="p">]</span> <span class="o">+</span> <span class="n">df_ldst</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"Grid Points"</span><span class="p">)[</span><span class="s2">"PM_LD_CMPL (min)"</span><span class="p">])</span><span class="o">*</span><span class="mi">8</span>
+<span class="n">df_byte</span><span class="p">[</span><span class="s2">"Stores"</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">df_vldvst</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"Grid Points"</span><span class="p">)[</span><span class="s2">"PM_VECTOR_ST_CMPL (min)"</span><span class="p">]</span> <span class="o">+</span> <span class="n">df_ldst</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"Grid Points"</span><span class="p">)[</span><span class="s2">"PM_ST_CMPL (min)"</span><span class="p">])</span><span class="o">*</span><span class="mi">8</span>
<span class="n">ax</span> <span class="o">=</span> <span class="n">df_byte</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">"Bytes / Loop Iteration"</span><span class="p">);</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">"Bytes"</span><span class="p">);</span>
</pre></div>
</div>
</div>
</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div><div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Let's quantify the difference by, again, fitting a linear function to the data.</p>
+
+</div>
+</div>
</div>
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<div class="prompt input_prompt">In [ ]:</div>
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-<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="n">mean_byte_ld</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">polyfit</span><span class="p">(</span><span class="n">df_byte</span><span class="p">[</span><span class="n">df_byte</span><span class="o">.</span><span class="n">index</span> <span class="o">></span> <span class="mi">200</span><span class="p">]</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">df_byte</span><span class="p">[</span><span class="n">df_byte</span><span class="o">.</span><span class="n">index</span> <span class="o">></span> <span class="mi">200</span><span class="p">][</span><span class="s2">"Loads / Loop Iteration"</span><span class="p">],</span> <span class="mi">0</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-<span class="n">mean_byte_st</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">polyfit</span><span class="p">(</span><span class="n">df_byte</span><span class="p">[</span><span class="n">df_byte</span><span class="o">.</span><span class="n">index</span> <span class="o">></span> <span class="mi">200</span><span class="p">]</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">df_byte</span><span class="p">[</span><span class="n">df_byte</span><span class="o">.</span><span class="n">index</span> <span class="o">></span> <span class="mi">200</span><span class="p">][</span><span class="s2">"Stores / Loop Iteration"</span><span class="p">],</span> <span class="mi">0</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">"Mean byte loaded: </span><span class="si">{}</span><span class="se">\t</span><span class="s2">Mean byte stored: </span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">mean_byte_ld</span><span class="p">,</span> <span class="n">mean_byte_st</span><span class="p">))</span>
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">_fit</span><span class="p">,</span> <span class="n">_cov</span> <span class="o">=</span> <span class="n">common</span><span class="o">.</span><span class="n">print_and_return_fit</span><span class="p">(</span>
+ <span class="p">[</span><span class="s2">"Loads"</span><span class="p">,</span> <span class="s2">"Stores"</span><span class="p">],</span>
+ <span class="n">df_byte</span><span class="p">,</span>
+ <span class="n">linear_function</span>
+<span class="p">)</span>
+<span class="n">fit_parameters</span> <span class="o">=</span> <span class="p">{</span><span class="o">**</span><span class="n">fit_parameters</span><span class="p">,</span> <span class="o">**</span><span class="n">_fit</span><span class="p">}</span>
+<span class="n">fit_covariance</span> <span class="o">=</span> <span class="p">{</span><span class="o">**</span><span class="n">fit_covariance</span><span class="p">,</span> <span class="o">**</span><span class="n">_cov</span><span class="p">}</span>
</pre></div>
</div>
</div>
</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div><div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Analagously to the proportionality factors, this mich is loaded/stored per grid point.</p>
+
+</div>
+</div>
</div>
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</div><div class="inner_cell">
@@ -13533,7 +13806,7 @@ n_\text{ld}^\text{scalar} &= n_\text{ld}^\text{total} - n_\text{ld}^\text{ve
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_bandwidth</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">()</span>
-<span class="n">df_bandwidth</span><span class="p">[</span><span class="s2">"Bandwidth / Byte/Cycle"</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">df_byte</span><span class="p">[</span><span class="s2">"Loads / Loop Iteration"</span><span class="p">]</span> <span class="o">+</span> <span class="n">df_byte</span><span class="p">[</span><span class="s2">"Stores / Loop Iteration"</span><span class="p">])</span> <span class="o">/</span> <span class="n">df</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"nx"</span><span class="p">)[</span><span class="s2">"Cycles / Loop Iteration"</span><span class="p">]</span>
+<span class="n">df_bandwidth</span><span class="p">[</span><span class="s2">"Bandwidth / Byte/Cycle"</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">df_byte</span><span class="p">[</span><span class="s2">"Loads"</span><span class="p">]</span> <span class="o">+</span> <span class="n">df_byte</span><span class="p">[</span><span class="s2">"Stores"</span><span class="p">])</span> <span class="o">/</span> <span class="n">df</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"Grid Points"</span><span class="p">)[</span><span class="s2">"PM_RUN_CYC (min)"</span><span class="p">]</span>
</pre></div>
</div>
@@ -13544,7 +13817,7 @@ n_\text{ld}^\text{scalar} &= n_\text{ld}^\text{total} - n_\text{ld}^\text{ve
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-<p>Let's display it as a function of <code>nx</code>. And also compare it to the available L1 cache bandwidth in a second (sub-)plot. Non-interactive users, call <code>make graph_task2c</code>.</p>
+<p>Let's display it as a function of grid points. And also compare it to the available L1 cache bandwidth in a second (sub-)plot. Non-interactive users, call <code>make graph_task2c</code>.</p>
</div>
</div>
@@ -13580,7 +13853,7 @@ n_\text{ld}^\text{scalar} &= n_\text{ld}^\text{total} - n_\text{ld}^\text{ve
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<h2 id="Task-E1:-Measuring-FlOps">Task E1: Measuring FlOps<a class="anchor-link" href="#Task-E1:-Measuring-FlOps">¶</a></h2><p><a name="taske1"></a></p>
<p>If you still have time, feel free to work on the following extended task.</p>
-<p><strong>TASK</strong>: Please measure counters for <em>vectorized</em> floating point operations and <em>scalar</em> floating point operations. The two counters can also not be measured during the same run. So please see the TODOs in <a href="/edit/Tasks/poisson2d.sflops.c"><code>poisson2d.sflops.c</code></a> and <a href="/edit/Tasks/poisson2d.vflops.c"><code>poisson2d.vflops.c</code></a>. By now you should be able to find out the names of the counters by yourself (<em>Hint: they include the words scalar and vector…</em>).</p>
+<p><strong>TASK</strong>: Please measure counters for <em>vectorized</em> floating point operations and <em>scalar</em> floating point operations. The two counters can also not be measured during the same run. So please see the TODOs in <a href="/edit/Tasks/poisson2d.sflops.c"><code>poisson2d.sflops.c</code></a> and <a href="/edit/Tasks/poisson2d.vflops.c"><code>poisson2d.vflops.c</code></a>. By now you should be able to find out the names of the counters by yourself (<em>Hint: they include the words »scalar« and »vector«…</em>).</p>
<p>As usual, compile, test, and bench-run your program.</p>
<p><a href="#toc">Back to top</a></p>
@@ -13608,6 +13881,7 @@ n_\text{ld}^\text{scalar} &= n_\text{ld}^\text{total} - n_\text{ld}^\text{ve
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_sflop</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">"poisson2d.sflop.bin.csv"</span><span class="p">,</span> <span class="n">skiprows</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">50000</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
<span class="n">df_vflop</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">"poisson2d.vflop.bin.csv"</span><span class="p">,</span> <span class="n">skiprows</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">50000</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
<span class="n">df_flop</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">df_sflop</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"nx"</span><span class="p">),</span> <span class="n">df_vflop</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"nx"</span><span class="p">)[[</span><span class="s1">'PM_VECTOR_FLOP_CMPL (total)'</span><span class="p">,</span> <span class="s1">'PM_VECTOR_FLOP_CMPL (min)'</span><span class="p">,</span> <span class="s1">' PM_VECTOR_FLOP_CMPL (max)'</span><span class="p">]]],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span>
+<span class="n">df_flop</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>
</div>
@@ -13618,7 +13892,7 @@ n_\text{ld}^\text{scalar} &= n_\text{ld}^\text{total} - n_\text{ld}^\text{ve
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
-<p>The name of the vector counter is a bit misleading; not floating point operations are measured but floating point instructions. To get <em>real</em> floating point operations, each value needs to be multiplied by the vector width (2). We can plot the values afterwards (non-interactive: <code>make graph_task4</code>).</p>
+<p>Again, the name of the vector counter is a bit misleading; not floating point operations are measured but floating point instructions. To get <em>real</em> floating point operations, each value needs to be multiplied by the vector width (2). We can plot the values afterwards (non-interactive: <code>make graph_task4</code>).</p>
</div>
</div>
@@ -13628,9 +13902,22 @@ n_\text{ld}^\text{scalar} &= n_\text{ld}^\text{total} - n_\text{ld}^\text{ve
<div class="prompt input_prompt">In [ ]:</div>
<div class="inner_cell">
<div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">common</span><span class="o">.</span><span class="n">normalize</span><span class="p">(</span><span class="n">df_flop</span><span class="p">,</span> <span class="s2">"PM_SCALAR_FLOP_CMPL (min)"</span><span class="p">,</span> <span class="s2">"Scalar FlOps / Loop Iteration"</span><span class="p">)</span>
-<span class="n">common</span><span class="o">.</span><span class="n">normalize</span><span class="p">(</span><span class="n">df_flop</span><span class="p">,</span> <span class="s2">"PM_VECTOR_FLOP_CMPL (min)"</span><span class="p">,</span> <span class="s2">"Vector Instructions / Loop Iteration"</span><span class="p">)</span>
-<span class="n">df_flop</span><span class="p">[</span><span class="s2">"Vector FlOps / Loop Iteration"</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_flop</span><span class="p">[</span><span class="s2">"Vector Instructions / Loop Iteration"</span><span class="p">]</span> <span class="o">*</span> <span class="mi">2</span>
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_flop</span><span class="p">[</span><span class="s2">"Grid Points"</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_flop</span><span class="p">[</span><span class="s2">"nx"</span><span class="p">]</span> <span class="o">*</span> <span class="n">df_flop</span><span class="p">[</span><span class="s2">"ny"</span><span class="p">]</span>
+<span class="n">df_flop</span><span class="p">[</span><span class="s2">"Vector FlOps (min)"</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_flop</span><span class="p">[</span><span class="s2">"PM_VECTOR_FLOP_CMPL (min)"</span><span class="p">]</span> <span class="o">*</span> <span class="mi">2</span>
+<span class="n">df_flop</span><span class="p">[</span><span class="s2">"Scalar FlOps (min)"</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_flop</span><span class="p">[</span><span class="s2">"PM_SCALAR_FLOP_CMPL (min)"</span><span class="p">]</span>
+</pre></div>
+
+ </div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [ ]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_flop</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"Grid Points"</span><span class="p">)[[</span><span class="s2">"Scalar FlOps (min)"</span><span class="p">,</span> <span class="s2">"Vector FlOps (min)"</span><span class="p">]]</span><span class="o">.</span><span class="n">plot</span><span class="p">();</span>
</pre></div>
</div>
@@ -13643,7 +13930,13 @@ n_\text{ld}^\text{scalar} &= n_\text{ld}^\text{total} - n_\text{ld}^\text{ve
<div class="prompt input_prompt">In [ ]:</div>
<div class="inner_cell">
<div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_flop</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"nx"</span><span class="p">)[[</span><span class="s2">"Scalar FlOps / Loop Iteration"</span><span class="p">,</span> <span class="s2">"Vector FlOps / Loop Iteration"</span><span class="p">]]</span><span class="o">.</span><span class="n">plot</span><span class="p">();</span>
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">_fit</span><span class="p">,</span> <span class="n">_cov</span> <span class="o">=</span> <span class="n">common</span><span class="o">.</span><span class="n">print_and_return_fit</span><span class="p">(</span>
+ <span class="p">[</span><span class="s2">"Scalar FlOps (min)"</span><span class="p">,</span> <span class="s2">"Vector FlOps (min)"</span><span class="p">],</span>
+ <span class="n">df_flop</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"Grid Points"</span><span class="p">),</span>
+ <span class="n">linear_function</span>
+<span class="p">)</span>
+<span class="n">fit_parameters</span> <span class="o">=</span> <span class="p">{</span><span class="o">**</span><span class="n">fit_parameters</span><span class="p">,</span> <span class="o">**</span><span class="n">_fit</span><span class="p">}</span>
+<span class="n">fit_covariance</span> <span class="o">=</span> <span class="p">{</span><span class="o">**</span><span class="n">fit_covariance</span><span class="p">,</span> <span class="o">**</span><span class="n">_cov</span><span class="p">}</span>
</pre></div>
</div>
@@ -13668,10 +13961,10 @@ n_\text{ld}^\text{scalar} &= n_\text{ld}^\text{total} - n_\text{ld}^\text{ve
<div class="prompt input_prompt">In [ ]:</div>
<div class="inner_cell">
<div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">I_flop_scalar</span> <span class="o">=</span> <span class="n">df_flop</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"nx"</span><span class="p">)[</span><span class="s2">"Scalar FlOps / Loop Iteration"</span><span class="p">]</span>
-<span class="n">I_flop_vector</span> <span class="o">=</span> <span class="n">df_flop</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"nx"</span><span class="p">)[</span><span class="s2">"Vector FlOps / Loop Iteration"</span><span class="p">]</span>
-<span class="n">I_mem_load</span> <span class="o">=</span> <span class="n">df_byte</span><span class="p">[</span><span class="s2">"Loads / Loop Iteration"</span><span class="p">]</span>
-<span class="n">I_mem_store</span> <span class="o">=</span> <span class="n">df_byte</span><span class="p">[</span><span class="s2">"Stores / Loop Iteration"</span><span class="p">]</span>
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">I_flop_scalar</span> <span class="o">=</span> <span class="n">df_flop</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"Grid Points"</span><span class="p">)[</span><span class="s2">"Scalar FlOps (min)"</span><span class="p">]</span>
+<span class="n">I_flop_vector</span> <span class="o">=</span> <span class="n">df_flop</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"Grid Points"</span><span class="p">)[</span><span class="s2">"Vector FlOps (min)"</span><span class="p">]</span>
+<span class="n">I_mem_load</span> <span class="o">=</span> <span class="n">df_byte</span><span class="p">[</span><span class="s2">"Loads"</span><span class="p">]</span>
+<span class="n">I_mem_store</span> <span class="o">=</span> <span class="n">df_byte</span><span class="p">[</span><span class="s2">"Stores"</span><span class="p">]</span>
</pre></div>
</div>
@@ -13708,6 +14001,7 @@ n_\text{ld}^\text{scalar} &= n_\text{ld}^\text{total} - n_\text{ld}^\text{ve
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Task-E2:-Measuring-a-Larger-Range">Task E2: Measuring a Larger Range<a class="anchor-link" href="#Task-E2:-Measuring-a-Larger-Range">¶</a></h2><p><a name="taske2"></a></p>
<p>If you still still have time, you might venture into your own benchmarking adventure.</p>
+<p>Maybe you noticed already, for instance in Task 2 C: At the very right to very large numbers of grid points, the behaviour of the graph changed. Something is happening there!</p>
<p><strong>TASK</strong>: Revisit the counters measured above for a larger range of <code>nx</code>. Right now, we only studied <code>nx</code> until 1000. New effects appear above that value – partly only well above, though ($nx > 15000$).</p>
<p>You're on your own here. Edit the <code>bench.sh</code> script to change the range and the stepping increments.</p>
<p><strong>Good luck!</strong></p>
diff --git a/2-Performance_Counters/Handson/Hands-On-Performance-Counters.ipynb b/2-Performance_Counters/Handson/Hands-On-Performance-Counters.ipynb
index 7942959e4d442db3198e4a5178f0cbe4da613c5f..c704269f97e25628079d4c31f8ef107f457e69d9 100644
--- a/2-Performance_Counters/Handson/Hands-On-Performance-Counters.ipynb
+++ b/2-Performance_Counters/Handson/Hands-On-Performance-Counters.ipynb
@@ -6,7 +6,7 @@
"source": [
"# Hands-On: Performance Counters\n",
"\n",
- "This Notebook is part of the exercises for the SC18 Tutorial »Application Porting and Optimization on GPU-accelerated POWER Architectures«. It is to be run on a POWER9 machine; in the tutorial: on Ascent, the POWER9 training cluster of Oak Ridge National Lab.\n",
+ "This Notebook is part of the exercises for the SC19 Tutorial »Application Porting and Optimization on GPU-accelerated POWER Architectures«. It is to be run on a POWER9 machine; in the tutorial: on Ascent, the POWER9 training cluster of Oak Ridge National Lab.\n",
"\n",
"This Notebook can be run interactively on Ascent. If this capability is unavailable to you, use it as a description for executing the tasks on Ascent via a shell access. During data evaluation, the Notebook mentions the corresponding commands to execute in case you are not able to run the Notebook interactively directly on Ascent.\n",
"\n",
@@ -43,18 +43,28 @@
"}\n",
"```\n",
"\n",
- "After `PAPI_add_named_event()` is used to add named PMU events outside of the relaxation iteration, `PAPI_start()`\n",
+ "The code is instrumented using PAPI. The API routine `PAPI_add_named_event()` is used to add *named* PMU events outside of the relaxation iteration. After that, calls to `PAPI_start()`\n",
"and `PAPI_stop()` can be used to count how often a PMU event is incremented.\n",
"\n",
- "For the first task, we will measure quantities often used to characterize an application, cycles and instructions.\n",
+ "For the first task, we will measure quantities often used to characterize an application: cycles and instructions.\n",
"\n",
- "**TASK**: Please measure counters for completed instructions and run cycles. See the TODOs in [`poisson2d.ins_cyc.c`](/edit/Tasks/poisson2d.ins_cyc.c). Either edit with Jupyter capabilities by clicking on the link of the file or use a dedicated editor (`vim` is available). The names of the counters to be implemented are `PM_INST_CMPL` and `PM_RUN_CYC`.\n",
+ "**TASK**: Please measure counters for completed instructions and run cycles. See the TODOs in file [`poisson2d.ins_cyc.c`](poisson2d.ins_cyc.c). You can either edit the files with Jupyter capabilities by clicking on the link of the file or selecting it in the file drawer on the left; or use a dedicated editor on the system(`vim` is available). The names of the counters to be implemented are `PM_INST_CMPL` and `PM_RUN_CYC`.\n",
"\n",
- "After changing the source code, compile it with `make task1` or by executing the following cell (we need to change directories first, though).\n",
+ "After changing the source code, compile it with `make task1` or by executing the following cell (we need to change directories first, though). \n",
+ "*(Using the `Makefile` we have hidden quite a few intricacies from you in order to focus on the relevant content at hand. Don't worry too much about it right now – we'll un-hide it gradually during the course of the tutorial.)*\n",
"\n",
"[Back to top](#toc)"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!pwd"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -78,7 +88,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Make sure your program is measuring correctly, by invoking it, for instance with these arguments: `./poisson2d.ins_cyc.bin 100 64 32` – see the next cell. The `100` specifies the number of iterations to perform, `64` and `32` are the size of the grid in y and x direction, respectively."
+ "Before we launch our measurement campaign we should make sure that the program is measuring correctly. Let's invoking it, for instance, with these arguments: `./poisson2d.ins_cyc.bin 100 64 32` – see the next cell. The `100` specifies the number of iterations to perform, `64` and `32` are the size of the grid in y and x direction, respectively."
]
},
{
@@ -95,7 +105,9 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "For the following runs, we are going to use Ascent's compute backend nodes which are not shared amongst users and also have six GPUs available. We use the available batch scheduler *IBM Spectrum LSF* for this. For convenience, a call to the batch submission system is stored in the environment variable `$SC18_SUBMIT_CMD`. You are welcome to adapt it once you get more familiar with the system.\n",
+ "Alright! That should return a comma-seperated list of measurements.\n",
+ "\n",
+ "For the following runs, we are going to use Ascent's compute backend nodes which are not shared amongst users and also have six GPUs available (each!). We use the available batch scheduler *IBM Spectrum LSF* for this. For convenience, a call to the batch submission system is stored in the environment variable `$SC19_SUBMIT_CMD`. You are welcome to adapt it once you get more familiar with the system.\n",
"\n",
"For now, we want to run our first benchmarking run and measure cycles and instructions for different data sizes, as a function of `nx`. The Makefile holds a target for this, call it with `make bench_task1`:"
]
@@ -113,7 +125,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Once the run is completed, let's have a look at the data!\n",
+ "Once the run is completed, let's study the data!\n",
"\n",
"This can be done best in the interactive version of the Jupyter Notebook. In case this version of the description is unavailable to you, call the Makefile target `make graph_task1` (either with X forwarding, or download the resulting PDF)."
]
@@ -124,6 +136,7 @@
"metadata": {},
"outputs": [],
"source": [
+ "import numpy as np\n",
"import seaborn as sns\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
@@ -133,6 +146,22 @@
"plt.rcParams['figure.figsize'] = [14, 6]"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Execute the following cell if you want to switch to color-blind-safer colors"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sns.set_palette(\"colorblind\")"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -141,29 +170,119 @@
"source": [
"plt.rcParams['figure.figsize'] = [14, 6]\n",
"df = pd.read_csv(\"poisson2d.ins_cyc.bin.csv\", skiprows=range(2, 50000, 2)) # Read in the CSV file from the bench run; parse with Pandas\n",
- "common.normalize(df, \"PM_INST_CMPL (min)\", \"Instructions / Loop Iteration\") # Normalize to each grid cell\n",
- "common.normalize(df, \"PM_RUN_CYC (min)\", \"Cycles / Loop Iteration\")\n",
+ "df[\"Grid Points\"] = df[\"nx\"] * df[\"ny\"] # Add a new column of the number of grid points (the product of nx and ny)\n",
"df.head() # Display the head of the Pandas dataframe"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's have a look at the counters we've just measured and see how they scaling with increasing number of grid points.\n",
+ "\n",
+ "*In the following, we are always using the minimal value of the counter (indicated by »(min)«) as this should give us an estimate of the best achievable result of the architecture.*"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
- "# Plot Cycles and Instructions - both per grid cell\n",
"fig, (ax1, ax2) = plt.subplots(nrows=2, sharex=True)\n",
- "df.set_index(\"nx\")[\"Cycles / Loop Iteration\"].plot(ax=ax1, legend=True);\n",
- "df.set_index(\"nx\")[\"Instructions / Loop Iteration\"].plot(ax=ax2, legend=True);"
+ "df.set_index(\"Grid Points\")[\"PM_RUN_CYC (min)\"].plot(ax=ax1, legend=True);\n",
+ "df.set_index(\"Grid Points\")[\"PM_INST_CMPL (min)\"].plot(ax=ax2, legend=True);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Although some slight variations can be seen for run cycles for many grid points, the correlation looks quite linear (as one would naively expect). Let's test that by fitting a linear function!\n",
+ "\n",
+ "*The details of the fitting have been extracted into dedicated function, `print_and_return_fit()`, of the `common.py` helper file. If you're interested, [go have a look at it](common.py).* "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def linear_function(x, a, b):\n",
+ " return a*x+b"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fit_parameters, fit_covariance = common.print_and_return_fit(\n",
+ " [\"PM_RUN_CYC (min)\", \"PM_INST_CMPL (min)\"], \n",
+ " df.set_index(\"Grid Points\"), \n",
+ " linear_function,\n",
+ " format_uncertainty=\".4f\"\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's overlay our fits to the graphs from before."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fig, (ax1, ax2) = plt.subplots(nrows=2, sharex=True)\n",
+ "for ax, pmu_counter in zip([ax1, ax2], [\"PM_RUN_CYC (min)\", \"PM_INST_CMPL (min)\"]):\n",
+ " df.set_index(\"Grid Points\")[pmu_counter].plot(ax=ax, legend=True);\n",
+ " ax.plot(\n",
+ " df[\"Grid Points\"], \n",
+ " linear_function(df[\"Grid Points\"], *fit_parameters[pmu_counter]), \n",
+ " linestyle=\"--\", \n",
+ " label=\"Fit: {:.2f} * x + {:.2f}\".format(*fit_parameters[pmu_counter])\n",
+ " )\n",
+ " ax.legend();"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Please execute the next cell to summarize the first task."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(\"The algorithm under investigation runs about {:.0f} cycles and executes about {:.0f} instructions per grid point\".format(\n",
+ " *[fit_parameters[pmu_counter][0] for pmu_counter in [\"PM_RUN_CYC (min)\", \"PM_INST_CMPL (min)\"]]\n",
+ "))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "What is your result? What value do the graphs come asymptotically close too?\n",
+ "**Bonus:**\n",
"\n",
+ "The linear fits also calculate a y intersection (»`b`«). How do you interpret this value?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
"We are revisiting the graph in a little while.\n",
"\n",
"[Back to top](#toc)"
@@ -180,7 +299,10 @@
"\n",
"Let's compare your estimate to what the system actually does!\n",
"\n",
- "<a name=\"task2-a\"></a>**TASK A**: Please measure counters for loads and stores. See the TODOs in [`poisson2d.ld_st.c`](/edit/Tasks/poisson2d.ld_st.c). This time, implement `PM_LD_CMPL` and `PM_ST_CMPL`.\n",
+ "### Task A\n",
+ "<a name=\"task2-a\"></a>\n",
+ "\n",
+ "Please measure counters for loads and stores. See the TODOs in [`poisson2d.ld_st.c`](/edit/Tasks/poisson2d.ld_st.c). This time, implement `PM_LD_CMPL` and `PM_ST_CMPL`.\n",
"\n",
"Compile with `make task2`, test your program with a single run with `make run_task2`, and then finally submit a benchmarking run to the batch system with `make bench_task2`. The following cell will take care of all this.\n",
"\n",
@@ -200,7 +322,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Once the run finished, let's plot it again with the following cell (non-interactive: `make graph_task2a`)."
+ "Once the run finished, let's plot it again in the course of the following cells (non-interactive: `make graph_task2a`)."
]
},
{
@@ -210,8 +332,7 @@
"outputs": [],
"source": [
"df_ldst = pd.read_csv(\"poisson2d.ld_st.bin.csv\", skiprows=range(2, 50000, 2))\n",
- "common.normalize(df_ldst, \"PM_LD_CMPL (min)\", \"Loads / Loop Iteration\")\n",
- "common.normalize(df_ldst, \"PM_ST_CMPL (min)\", \"Stores / Loop Iteration\")\n",
+ "df_ldst[\"Grid Points\"] = df_ldst[\"nx\"] * df_ldst[\"ny\"] \n",
"df_ldst.head()"
]
},
@@ -222,8 +343,56 @@
"outputs": [],
"source": [
"fig, (ax1, ax2) = plt.subplots(nrows=2, sharex=True)\n",
- "df_ldst.set_index(\"nx\")[\"Loads / Loop Iteration\"].plot(ax=ax1, legend=True);\n",
- "df_ldst.set_index(\"nx\")[\"Stores / Loop Iteration\"].plot(ax=ax2, legend=True);"
+ "df_ldst.set_index(\"Grid Points\")[\"PM_LD_CMPL (min)\"].plot(ax=ax1, legend=True);\n",
+ "df_ldst.set_index(\"Grid Points\")[\"PM_ST_CMPL (min)\"].plot(ax=ax2, legend=True);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Also this behaviour looks – at a first glance – linear. We can again fit a first-order polynom (and re-use our previously defined function `curve_fit`)!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "_fit, _cov = common.print_and_return_fit(\n",
+ " [\"PM_LD_CMPL (min)\", \"PM_ST_CMPL (min)\"], \n",
+ " df_ldst.set_index(\"Grid Points\"), \n",
+ " linear_function,\n",
+ " format_value=\".4f\"\n",
+ ")\n",
+ "fit_parameters = {**fit_parameters, **_fit}\n",
+ "fit_covariance = {**fit_covariance, **_cov}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's overlay this in one common plot:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fig, (ax1, ax2) = plt.subplots(nrows=2, sharex=True)\n",
+ "for ax, pmu_counter in zip([ax1, ax2], [\"PM_LD_CMPL (min)\", \"PM_ST_CMPL (min)\"]):\n",
+ " df_ldst.set_index(\"Grid Points\")[pmu_counter].plot(ax=ax, legend=True);\n",
+ " ax.plot(\n",
+ " df_ldst[\"Grid Points\"], \n",
+ " linear_function(df[\"Grid Points\"], *fit_parameters[pmu_counter]), \n",
+ " linestyle=\"--\", \n",
+ " label=\"Fit: {:.2f} * x + {:.2f}\".format(*fit_parameters[pmu_counter])\n",
+ " )\n",
+ " ax.legend();"
]
},
{
@@ -232,9 +401,12 @@
"source": [
"Did you expect more?\n",
"\n",
- "The reason is simple: Among the load and store instructions counted by `PM_LD_CMPL` and `PM_ST_CMPL` are vector instructions which can load and store multiple (two) values at a time. To see how many *bytes* are loaded and stored, we need to measure counters for vectorized loads and stores as well.\n",
+ "The reason is simple: Among the load and store instructions counted by `PM_LD_CMPL` and `PM_ST_CMPL` are vector instructions which can load and store multiple (in this case: two) values at a time. To see how many *bytes* are loaded and stored, we need to measure counters for vectorized loads and stores as well.\n",
"\n",
- "<a name=\"task2-b\"></a>**TASK B**: Please measure counters for _vectorized_ loads and _vectorized_ stores. See the TODOs in [`poisson2d.vld.c`](/edit/Tasks/poisson2d.vld.c) and [`poisson2d.vst.c`](/edit/Tasks/poisson2d.vst.c) (*Note: These vector counters can not be measured together and need separate files and runs*). Can you find out the name of the counters yourself, using `papi_native_avail | grep VECTOR_`?\n",
+ "### TASK B\n",
+ "<a name=\"task2-b\"></a>\n",
+ "\n",
+ "Please measure counters for _vectorized_ loads and _vectorized_ stores. See the TODOs in [`poisson2d.vld.c`](poisson2d.vld.c) and [`poisson2d.vst.c`](poisson2d.vst.c) (*Note: These vector counters can not be measured together and need separate files and runs*). Can you find out the name of the counters yourself, using `papi_native_avail | grep VECTOR_`?\n",
"\n",
"Compile, test, and bench-run your program again.\n",
"\n",
@@ -272,7 +444,7 @@
"source": [
"Let's plot it again, as soon as the run finishes! Non-interactively, call `graph_task2b`.\n",
"\n",
- "*We need to read in two CSV files now, which we combine to one common dataframe `df_vldvst`.*"
+ "*Because we couldn't measure the two vector counters at the same time, we have two CSV files to read in now. We combine them into one common dataframe `df_vldvst` in the following.*"
]
},
{
@@ -292,8 +464,7 @@
"metadata": {},
"outputs": [],
"source": [
- "common.normalize(df_vldvst, \"PM_VECTOR_LD_CMPL (min)\", \"Vector Loads / Loop Iteration\")\n",
- "common.normalize(df_vldvst, \"PM_VECTOR_ST_CMPL (min)\", \"Vector Stores / Loop Iteration\")\n",
+ "df_vldvst[\"Grid Points\"] = df_vldvst[\"nx\"] * df_vldvst[\"ny\"] \n",
"df_vldvst.head()"
]
},
@@ -304,8 +475,49 @@
"outputs": [],
"source": [
"fig, (ax1, ax2) = plt.subplots(nrows=2, sharex=True)\n",
- "df_vldvst.set_index(\"nx\")[\"Vector Loads / Loop Iteration\"].plot(ax=ax1, legend=True);\n",
- "df_vldvst.set_index(\"nx\")[\"Vector Stores / Loop Iteration\"].plot(ax=ax2, legend=True);"
+ "df_vldvst.set_index(\"Grid Points\")[\"PM_VECTOR_LD_CMPL (min)\"].plot(ax=ax1, legend=True);\n",
+ "df_vldvst.set_index(\"Grid Points\")[\"PM_VECTOR_ST_CMPL (min)\"].plot(ax=ax2, legend=True);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Also here seems to be a linear correlation. Let's do our fitting and plot directly."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "_fit, _cov = common.print_and_return_fit(\n",
+ " [\"PM_VECTOR_LD_CMPL (min)\", \"PM_VECTOR_ST_CMPL (min)\"], \n",
+ " df_vldvst.set_index(\"Grid Points\"), \n",
+ " linear_function,\n",
+ " format_value=\".4f\",\n",
+ ")\n",
+ "fit_parameters = {**fit_parameters, **_fit}\n",
+ "fit_covariance = {**fit_covariance, **_cov}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fig, (ax1, ax2) = plt.subplots(nrows=2, sharex=True)\n",
+ "for ax, pmu_counter in zip([ax1, ax2], [\"PM_VECTOR_LD_CMPL (min)\", \"PM_VECTOR_ST_CMPL (min)\"]):\n",
+ " df_vldvst.set_index(\"Grid Points\")[pmu_counter].plot(ax=ax, legend=True);\n",
+ " ax.plot(\n",
+ " df_vldvst[\"Grid Points\"], \n",
+ " linear_function(df[\"Grid Points\"], *fit_parameters[pmu_counter]), \n",
+ " linestyle=\"--\", \n",
+ " label=\"Fit: {:.2f} * x + {:.2f}\".format(*fit_parameters[pmu_counter])\n",
+ " )\n",
+ " ax.legend();"
]
},
{
@@ -339,10 +551,17 @@
"outputs": [],
"source": [
"df_byte = pd.DataFrame()\n",
- "df_byte[\"Loads / Loop Iteration\"] = (df_vldvst.set_index(\"nx\")[\"Vector Loads / Loop Iteration\"] + df_ldst.set_index(\"nx\")[\"Loads / Loop Iteration\"])*8\n",
- "df_byte[\"Stores / Loop Iteration\"] = (df_vldvst.set_index(\"nx\")[\"Vector Stores / Loop Iteration\"] + df_ldst.set_index(\"nx\")[\"Stores / Loop Iteration\"])*8\n",
+ "df_byte[\"Loads\"] = (df_vldvst.set_index(\"Grid Points\")[\"PM_VECTOR_LD_CMPL (min)\"] + df_ldst.set_index(\"Grid Points\")[\"PM_LD_CMPL (min)\"])*8\n",
+ "df_byte[\"Stores\"] = (df_vldvst.set_index(\"Grid Points\")[\"PM_VECTOR_ST_CMPL (min)\"] + df_ldst.set_index(\"Grid Points\")[\"PM_ST_CMPL (min)\"])*8\n",
"ax = df_byte.plot()\n",
- "ax.set_ylabel(\"Bytes / Loop Iteration\");"
+ "ax.set_ylabel(\"Bytes\");"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's quantify the difference by, again, fitting a linear function to the data."
]
},
{
@@ -351,10 +570,20 @@
"metadata": {},
"outputs": [],
"source": [
- "import numpy as np\n",
- "mean_byte_ld = np.polyfit(df_byte[df_byte.index > 200].index, df_byte[df_byte.index > 200][\"Loads / Loop Iteration\"], 0)[0]\n",
- "mean_byte_st = np.polyfit(df_byte[df_byte.index > 200].index, df_byte[df_byte.index > 200][\"Stores / Loop Iteration\"], 0)[0]\n",
- "print(\"Mean byte loaded: {}\\tMean byte stored: {}\".format(mean_byte_ld, mean_byte_st))"
+ "_fit, _cov = common.print_and_return_fit(\n",
+ " [\"Loads\", \"Stores\"], \n",
+ " df_byte, \n",
+ " linear_function\n",
+ ")\n",
+ "fit_parameters = {**fit_parameters, **_fit}\n",
+ "fit_covariance = {**fit_covariance, **_cov}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Analagously to the proportionality factors, this mich is loaded/stored per grid point."
]
},
{
@@ -371,14 +600,14 @@
"outputs": [],
"source": [
"df_bandwidth = pd.DataFrame()\n",
- "df_bandwidth[\"Bandwidth / Byte/Cycle\"] = (df_byte[\"Loads / Loop Iteration\"] + df_byte[\"Stores / Loop Iteration\"]) / df.set_index(\"nx\")[\"Cycles / Loop Iteration\"]"
+ "df_bandwidth[\"Bandwidth / Byte/Cycle\"] = (df_byte[\"Loads\"] + df_byte[\"Stores\"]) / df.set_index(\"Grid Points\")[\"PM_RUN_CYC (min)\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "Let's display it as a function of `nx`. And also compare it to the available L1 cache bandwidth in a second (sub-)plot. Non-interactive users, call `make graph_task2c`."
+ "Let's display it as a function of grid points. And also compare it to the available L1 cache bandwidth in a second (sub-)plot. Non-interactive users, call `make graph_task2c`."
]
},
{
@@ -412,7 +641,7 @@
"If you still have time, feel free to work on the following extended task.\n",
"\n",
"\n",
- "**TASK**: Please measure counters for _vectorized_ floating point operations and _scalar_ floating point operations. The two counters can also not be measured during the same run. So please see the TODOs in [`poisson2d.sflops.c`](/edit/Tasks/poisson2d.sflops.c) and [`poisson2d.vflops.c`](/edit/Tasks/poisson2d.vflops.c). By now you should be able to find out the names of the counters by yourself (*Hint: they include the words scalar and vector…*).\n",
+ "**TASK**: Please measure counters for _vectorized_ floating point operations and _scalar_ floating point operations. The two counters can also not be measured during the same run. So please see the TODOs in [`poisson2d.sflops.c`](/edit/Tasks/poisson2d.sflops.c) and [`poisson2d.vflops.c`](/edit/Tasks/poisson2d.vflops.c). By now you should be able to find out the names of the counters by yourself (*Hint: they include the words »scalar« and »vector«…*).\n",
"\n",
"As usual, compile, test, and bench-run your program.\n",
"\n",
@@ -436,14 +665,26 @@
"source": [
"df_sflop = pd.read_csv(\"poisson2d.sflop.bin.csv\", skiprows=range(2, 50000, 2))\n",
"df_vflop = pd.read_csv(\"poisson2d.vflop.bin.csv\", skiprows=range(2, 50000, 2))\n",
- "df_flop = pd.concat([df_sflop.set_index(\"nx\"), df_vflop.set_index(\"nx\")[['PM_VECTOR_FLOP_CMPL (total)', 'PM_VECTOR_FLOP_CMPL (min)', ' PM_VECTOR_FLOP_CMPL (max)']]], axis=1).reset_index()"
+ "df_flop = pd.concat([df_sflop.set_index(\"nx\"), df_vflop.set_index(\"nx\")[['PM_VECTOR_FLOP_CMPL (total)', 'PM_VECTOR_FLOP_CMPL (min)', ' PM_VECTOR_FLOP_CMPL (max)']]], axis=1).reset_index()\n",
+ "df_flop.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "The name of the vector counter is a bit misleading; not floating point operations are measured but floating point instructions. To get *real* floating point operations, each value needs to be multiplied by the vector width (2). We can plot the values afterwards (non-interactive: `make graph_task4`)."
+ "Again, the name of the vector counter is a bit misleading; not floating point operations are measured but floating point instructions. To get *real* floating point operations, each value needs to be multiplied by the vector width (2). We can plot the values afterwards (non-interactive: `make graph_task4`)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_flop[\"Grid Points\"] = df_flop[\"nx\"] * df_flop[\"ny\"]\n",
+ "df_flop[\"Vector FlOps (min)\"] = df_flop[\"PM_VECTOR_FLOP_CMPL (min)\"] * 2\n",
+ "df_flop[\"Scalar FlOps (min)\"] = df_flop[\"PM_SCALAR_FLOP_CMPL (min)\"]"
]
},
{
@@ -452,9 +693,7 @@
"metadata": {},
"outputs": [],
"source": [
- "common.normalize(df_flop, \"PM_SCALAR_FLOP_CMPL (min)\", \"Scalar FlOps / Loop Iteration\")\n",
- "common.normalize(df_flop, \"PM_VECTOR_FLOP_CMPL (min)\", \"Vector Instructions / Loop Iteration\")\n",
- "df_flop[\"Vector FlOps / Loop Iteration\"] = df_flop[\"Vector Instructions / Loop Iteration\"] * 2"
+ "df_flop.set_index(\"Grid Points\")[[\"Scalar FlOps (min)\", \"Vector FlOps (min)\"]].plot();"
]
},
{
@@ -463,7 +702,13 @@
"metadata": {},
"outputs": [],
"source": [
- "df_flop.set_index(\"nx\")[[\"Scalar FlOps / Loop Iteration\", \"Vector FlOps / Loop Iteration\"]].plot();"
+ "_fit, _cov = common.print_and_return_fit(\n",
+ " [\"Scalar FlOps (min)\", \"Vector FlOps (min)\"], \n",
+ " df_flop.set_index(\"Grid Points\"), \n",
+ " linear_function\n",
+ ")\n",
+ "fit_parameters = {**fit_parameters, **_fit}\n",
+ "fit_covariance = {**fit_covariance, **_cov}"
]
},
{
@@ -487,10 +732,10 @@
"metadata": {},
"outputs": [],
"source": [
- "I_flop_scalar = df_flop.set_index(\"nx\")[\"Scalar FlOps / Loop Iteration\"]\n",
- "I_flop_vector = df_flop.set_index(\"nx\")[\"Vector FlOps / Loop Iteration\"]\n",
- "I_mem_load = df_byte[\"Loads / Loop Iteration\"]\n",
- "I_mem_store = df_byte[\"Stores / Loop Iteration\"]"
+ "I_flop_scalar = df_flop.set_index(\"Grid Points\")[\"Scalar FlOps (min)\"]\n",
+ "I_flop_vector = df_flop.set_index(\"Grid Points\")[\"Vector FlOps (min)\"]\n",
+ "I_mem_load = df_byte[\"Loads\"]\n",
+ "I_mem_store = df_byte[\"Stores\"]"
]
},
{
@@ -521,6 +766,8 @@
"\n",
"If you still still have time, you might venture into your own benchmarking adventure.\n",
"\n",
+ "Maybe you noticed already, for instance in Task 2 C: At the very right to very large numbers of grid points, the behaviour of the graph changed. Something is happening there!\n",
+ "\n",
"\n",
"**TASK**: Revisit the counters measured above for a larger range of `nx`. Right now, we only studied `nx` until 1000. New effects appear above that value – partly only well above, though ($nx > 15000$).\n",
"\n",
@@ -548,9 +795,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.1"
+ "version": "3.7.0"
}
},
"nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
}
diff --git a/2-Performance_Counters/Handson/Hands-On-Performance-Counters.pdf b/2-Performance_Counters/Handson/Hands-On-Performance-Counters.pdf
index 73a4faac63aa8e05cd229237c0810f168ad6fcd9..570da387d6836ef559a0c47b1a0a53bc19b847b6 100644
Binary files a/2-Performance_Counters/Handson/Hands-On-Performance-Counters.pdf and b/2-Performance_Counters/Handson/Hands-On-Performance-Counters.pdf differ
diff --git a/2-Performance_Counters/Handson/README.md b/2-Performance_Counters/Handson/README.md
index 3ee0057da9448f49ece6e6afededeafee38ff907..8887dd78d9f2db7644f575df47c48c4c41ed655e 100644
--- a/2-Performance_Counters/Handson/README.md
+++ b/2-Performance_Counters/Handson/README.md
@@ -2,7 +2,5 @@
This folder holds the files for the first hands-on exercise about Performance Counters on POWER9.
-Make sure to load all modules of this session by typing `module load sc18/handson1` into the shell.
-
All task description is in an accompanying Jupyter Notebook. Open it interactively on Ascent with port forwarding. If that is impossible to do, use the static convert to HTML or PDF of the Notebook and follow along accordingly.
diff --git a/2-Performance_Counters/Handson/Solutions/Hands-On-Performance-Counters.html b/2-Performance_Counters/Handson/Solutions/Hands-On-Performance-Counters.html
index 9880bb07996842b407fb2ee33df39f8c14388edf..70a67890f406b9636d1397ce7ca82b2e66642e19 100644
--- a/2-Performance_Counters/Handson/Solutions/Hands-On-Performance-Counters.html
+++ b/2-Performance_Counters/Handson/Solutions/Hands-On-Performance-Counters.html
@@ -2,7 +2,7 @@
<html>
<head><meta charset="utf-8" />
-<title>Hands-On-Performance-Counters</title>
+<title>Notebook</title>
<script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/2.0.3/jquery.min.js"></script>
@@ -13116,7 +13116,7 @@ div#notebook {
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
-<h1 id="Hands-On:-Performance-Counters">Hands-On: Performance Counters<a class="anchor-link" href="#Hands-On:-Performance-Counters">¶</a></h1><p>This Notebook is part of the exercises for the SC18 Tutorial »Application Porting and Optimization on GPU-accelerated POWER Architectures«. It is to be run on a POWER9 machine; in the tutorial: on Ascent, the POWER9 training cluster of Oak Ridge National Lab.</p>
+<h1 id="Hands-On:-Performance-Counters">Hands-On: Performance Counters<a class="anchor-link" href="#Hands-On:-Performance-Counters">¶</a></h1><p>This Notebook is part of the exercises for the SC19 Tutorial »Application Porting and Optimization on GPU-accelerated POWER Architectures«. It is to be run on a POWER9 machine; in the tutorial: on Ascent, the POWER9 training cluster of Oak Ridge National Lab.</p>
<p>This Notebook can be run interactively on Ascent. If this capability is unavailable to you, use it as a description for executing the tasks on Ascent via a shell access. During data evaluation, the Notebook mentions the corresponding commands to execute in case you are not able to run the Notebook interactively directly on Ascent.</p>
<h2 id="Table-of-Contents">Table of Contents<a class="anchor-link" href="#Table-of-Contents">¶</a></h2><p><a name="toc"></a></p>
<ul>
@@ -13149,15 +13149,47 @@ div#notebook {
<span class="p">}</span>
<span class="p">}</span>
</pre></div>
-<p>After <code>PAPI_add_named_event()</code> is used to add named PMU events outside of the relaxation iteration, <code>PAPI_start()</code>
+<p>The code is instrumented using PAPI. The API routine <code>PAPI_add_named_event()</code> is used to add <em>named</em> PMU events outside of the relaxation iteration. After that, calls to <code>PAPI_start()</code>
and <code>PAPI_stop()</code> can be used to count how often a PMU event is incremented.</p>
-<p>For the first task, we will measure quantities often used to characterize an application, cycles and instructions.</p>
-<p><strong>TASK</strong>: Please measure counters for completed instructions and run cycles. See the TODOs in <a href="/edit/Tasks/poisson2d.ins_cyc.c"><code>poisson2d.ins_cyc.c</code></a>. Either edit with Jupyter capabilities by clicking on the link of the file or use a dedicated editor (<code>vim</code> is available). The names of the counters to be implemented are <code>PM_INST_CMPL</code> and <code>PM_RUN_CYC</code>.</p>
-<p>After changing the source code, compile it with <code>make task1</code> or by executing the following cell (we need to change directories first, though).</p>
+<p>For the first task, we will measure quantities often used to characterize an application: cycles and instructions.</p>
+<p><strong>TASK</strong>: Please measure counters for completed instructions and run cycles. See the TODOs in file <a href="poisson2d.ins_cyc.c"><code>poisson2d.ins_cyc.c</code></a>. You can either edit the files with Jupyter capabilities by clicking on the link of the file or selecting it in the file drawer on the left; or use a dedicated editor on the system(<code>vim</code> is available). The names of the counters to be implemented are <code>PM_INST_CMPL</code> and <code>PM_RUN_CYC</code>.</p>
+<p>After changing the source code, compile it with <code>make task1</code> or by executing the following cell (we need to change directories first, though).<br>
+<em>(Using the <code>Makefile</code> we have hidden quite a few intricacies from you in order to focus on the relevant content at hand. Don't worry too much about it right now – we'll un-hide it gradually during the course of the tutorial.)</em></p>
<p><a href="#toc">Back to top</a></p>
</div>
</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [1]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="o">!</span>pwd
+</pre></div>
+
+ </div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+ <div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>/autofs/nccsopen-svm1_home/aherten/OpenPOWER-SC19/Prototyping/2-Performance_Counters/Handson/Solutions
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
@@ -13193,7 +13225,7 @@ and <code>PAPI_stop()</code> can be used to count how often a PMU event is incre
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
-<div class="prompt input_prompt">In [20]:</div>
+<div class="prompt input_prompt">In [2]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">!</span>make task1
@@ -13213,7 +13245,7 @@ and <code>PAPI_stop()</code> can be used to count how often a PMU event is incre
<div class="output_subarea output_stream output_stdout output_text">
-<pre>gcc -DUSE_DOUBLE -Ofast -std=c99 -lm -lpapi poisson2d.ins_cyc.c -o poisson2d.ins_cyc.bin
+<pre>gcc -DUSE_DOUBLE -Ofast -std=c99 -lm -lpapi poisson2d.ins_cyc.c -o poisson2d.ins_cyc.bin
</pre>
</div>
</div>
@@ -13225,14 +13257,14 @@ and <code>PAPI_stop()</code> can be used to count how often a PMU event is incre
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
-<p>Make sure your program is measuring correctly, by invoking it, for instance with these arguments: <code>./poisson2d.ins_cyc.bin 100 64 32</code> – see the next cell. The <code>100</code> specifies the number of iterations to perform, <code>64</code> and <code>32</code> are the size of the grid in y and x direction, respectively.</p>
+<p>Before we launch our measurement campaign we should make sure that the program is measuring correctly. Let's invoking it, for instance, with these arguments: <code>./poisson2d.ins_cyc.bin 100 64 32</code> – see the next cell. The <code>100</code> specifies the number of iterations to perform, <code>64</code> and <code>32</code> are the size of the grid in y and x direction, respectively.</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
-<div class="prompt input_prompt">In [25]:</div>
+<div class="prompt input_prompt">In [1]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">!</span>./poisson2d.ins_cyc.bin <span class="m">100</span> <span class="m">64</span> <span class="m">32</span>
@@ -13253,8 +13285,8 @@ and <code>PAPI_stop()</code> can be used to count how often a PMU event is incre
<div class="output_subarea output_stream output_stdout output_text">
-<pre>iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-100,64,32,0.0011,3324000,33229,34329,1902422,18803,27821
+<pre>iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
+100,64,32,0.0011,3324225,33235,33960,1859440,18357,25033
</pre>
</div>
</div>
@@ -13266,7 +13298,8 @@ and <code>PAPI_stop()</code> can be used to count how often a PMU event is incre
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
-<p>For the following runs, we are going to use Ascent's compute backend nodes which are not shared amongst users and also have six GPUs available. We use the available batch scheduler <em>IBM Spectrum LSF</em> for this. For convenience, a call to the batch submission system is stored in the environment variable <code>$SC18_SUBMIT_CMD</code>. You are welcome to adapt it once you get more familiar with the system.</p>
+<p>Alright! That should return a comma-seperated list of measurements.</p>
+<p>For the following runs, we are going to use Ascent's compute backend nodes which are not shared amongst users and also have six GPUs available (each!). We use the available batch scheduler <em>IBM Spectrum LSF</em> for this. For convenience, a call to the batch submission system is stored in the environment variable <code>$SC19_SUBMIT_CMD</code>. You are welcome to adapt it once you get more familiar with the system.</p>
<p>For now, we want to run our first benchmarking run and measure cycles and instructions for different data sizes, as a function of <code>nx</code>. The Makefile holds a target for this, call it with <code>make bench_task1</code>:</p>
</div>
@@ -13274,7 +13307,7 @@ and <code>PAPI_stop()</code> can be used to count how often a PMU event is incre
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
-<div class="prompt input_prompt">In [80]:</div>
+<div class="prompt input_prompt">In [2]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">!</span>make bench_task1
@@ -13294,524 +13327,523 @@ and <code>PAPI_stop()</code> can be used to count how often a PMU event is incre
<div class="output_subarea output_stream output_stdout output_text">
-<pre>gcc -DUSE_DOUBLE -Ofast -std=c99 -lm -lpapi poisson2d.ins_cyc.c -o poisson2d.ins_cyc.bin
-bsub -W 60 -nnodes 1 -Is -P GEN110 jsrun -n 1 -c 1 -g ALL_GPUS ./bench.sh poisson2d.ins_cyc.bin /gpfs/wolf/gen110/scratch/aherten//poisson2d.ins_cyc.bin.csv
-Job <4318> is submitted to default queue <batch>.
+<pre>bsub -W 60 -nnodes 1 -Is -P TRN003 jsrun -n 1 -c 1 -g ALL_GPUS ./bench.sh poisson2d.ins_cyc.bin /gpfs/wolf/trn003/scratch/aherten//poisson2d.ins_cyc.bin.csv
+Job <24059> is submitted to default queue <batch>.
<<Waiting for dispatch ...>>
<<Starting on login1>>
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,4,0.0012,548153,2735,3888,266504,1243,4753
+200,32,4,0.0012,572978,2861,3639,261330,1235,4684
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,8,0.0014,1082153,5405,6558,668070,3227,6573
+200,32,8,0.0014,1082978,5411,6189,601962,2914,5099
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,12,0.0014,1442153,7205,8358,872094,4181,12974
+200,32,12,0.0014,1442978,7211,7989,811603,3992,5761
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,16,0.0015,1802153,9005,10158,1074585,5230,7975
+200,32,16,0.0014,1802978,9011,9789,1017305,4988,7017
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,20,0.0015,2162153,10805,11958,1281118,6236,14107
+200,32,20,0.0015,2162978,10811,11589,1221559,6002,7999
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,24,0.0016,2522153,12605,13758,1479347,7222,10037
+200,32,24,0.0016,2522978,12611,13389,1435167,7037,9259
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,28,0.0019,2882153,14405,15558,1682827,8251,11219
+200,32,28,0.0016,2882978,14411,15189,1633061,8054,9789
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,32,0.0017,3242153,16205,17358,1871170,9210,12109
+200,32,32,0.0017,3242978,16211,16989,1842895,9092,10889
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,36,0.0018,3602153,18005,19158,2075730,10193,13063
+200,32,36,0.0018,3602978,18011,18789,2042894,10108,12457
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,40,0.0019,3962153,19805,20958,2272736,11258,14491
+200,32,40,0.0019,3962978,19811,20589,2261332,11191,14233
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,44,0.0019,4322153,21605,22758,2491982,12249,17554
+200,32,44,0.0020,4322978,21611,22389,2458267,12112,14375
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,48,0.0020,4682153,23405,24558,2692600,13292,16003
+200,32,48,0.0020,4682978,23411,24189,2658621,13164,15613
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,52,0.0020,5042153,25205,26358,2878730,14277,17055
+200,32,52,0.0020,5042978,25211,25989,2866175,14190,16864
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,56,0.0021,5402153,27005,28158,3084915,15295,18583
+200,32,56,0.0021,5402978,27011,27789,3080357,15237,21565
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,60,0.0022,5762153,28805,29958,3291836,16330,19233
+200,32,60,0.0022,5762978,28811,29589,3283103,16278,18799
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,64,0.0023,6122153,30605,31758,3622134,17946,20887
+200,32,64,0.0022,6122978,30611,31389,3587582,17820,19681
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,68,0.0024,6482153,32405,33558,3930512,19200,22297
+200,32,68,0.0025,6482978,32411,33189,3893368,19284,20847
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,72,0.0027,6842153,34205,35358,4270649,20402,22797
+200,32,72,0.0025,6842978,34211,34989,4289441,21278,22715
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,76,0.0025,7202153,36005,37158,4209408,20894,24035
+200,32,76,0.0024,7202978,36011,36789,4208700,20936,22677
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,80,0.0025,7562153,37805,38958,4410712,21911,24986
+200,32,80,0.0025,7562978,37811,38589,4409613,21897,23855
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,84,0.0026,7922153,39605,40758,4631259,23020,25649
+200,32,84,0.0026,7922978,39611,40389,4611755,22921,24910
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,88,0.0027,8282153,41405,42558,4814218,23914,26743
+200,32,88,0.0026,8282978,41411,42189,4821904,23974,26087
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,92,0.0027,8642153,43205,44358,5039020,24944,37612
+200,32,92,0.0028,8642978,43211,43989,5104722,25036,38488
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,96,0.0030,9002153,45005,46158,5247046,26072,29012
+200,32,96,0.0028,9002978,45011,45789,5238952,26060,27927
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,100,0.0029,9362153,46805,47958,5426721,26963,29831
+200,32,100,0.0028,9362978,46811,47589,5441545,27049,29275
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,104,0.0029,9722153,48605,49758,5619647,27963,31679
+200,32,104,0.0030,9722978,48611,49389,5920763,28136,72679
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,108,0.0030,10082153,50405,51558,5828776,28956,31626
+200,32,108,0.0030,10082978,50411,51189,5853554,29106,31403
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,112,0.0031,10442153,52205,53358,6033005,30029,32674
+200,32,112,0.0030,10442978,52211,52989,6053498,30123,32279
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,116,0.0031,10802153,54005,55158,6244763,30994,35257
+200,32,116,0.0031,10802978,54011,54789,6296056,31338,33377
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,120,0.0032,11162153,55805,56958,6425499,31972,34572
+200,32,120,0.0033,11162978,55811,56589,6468115,32146,33869
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,124,0.0033,11522153,57605,58758,6654149,33094,35931
+200,32,124,0.0032,11522978,57611,58389,6675248,33233,35075
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,128,0.0033,11882153,59405,60558,6851733,34090,36755
+200,32,128,0.0033,11882978,59411,60189,6894325,34338,36207
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,132,0.0034,12242153,61205,62358,7052529,35058,39834
+200,32,132,0.0034,12242978,61211,61989,7093543,35299,37463
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,136,0.0035,12602153,63005,64158,7241645,36039,38957
+200,32,136,0.0034,12602978,63011,63789,7312105,36353,48105
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,140,0.0035,12962153,64805,65958,7438548,37024,39702
+200,32,140,0.0035,12962978,64811,65589,7503757,37375,39247
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,144,0.0036,13322153,66605,67758,7649807,38039,46041
+200,32,144,0.0036,13322978,66611,67389,7692611,38277,40419
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,148,0.0037,13682153,68405,69558,7837686,39006,41671
+200,32,148,0.0037,13682978,68411,69189,7968094,39656,42113
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,152,0.0037,14042153,70205,71358,8039582,40031,42707
+200,32,152,0.0037,14042978,70211,70989,8122466,40468,42706
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,156,0.0038,14402153,72005,73158,8272212,41195,43645
+200,32,156,0.0038,14402978,72011,72789,8328043,41484,45104
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,160,0.0040,14762153,73805,74958,8471858,42200,44594
+200,32,160,0.0040,14762978,73811,74589,8547674,42493,54216
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,164,0.0039,15122153,75605,76758,8657085,43103,45699
+200,32,164,0.0039,15122978,75611,76389,8738805,43542,45427
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,168,0.0039,15482153,77405,78558,8856462,44110,46863
+200,32,168,0.0040,15482978,77411,78189,8948025,44560,46819
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,172,0.0040,15842153,79205,80358,9050337,45084,47600
+200,32,172,0.0040,15842978,79211,79989,9186567,45735,47659
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,176,0.0041,16202153,81005,82158,9267755,46142,55546
+200,32,176,0.0041,16202978,81011,81789,9391949,46573,70131
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,180,0.0042,16562153,82805,83958,9452041,47058,49763
+200,32,180,0.0042,16562978,82811,83589,9549568,47559,54271
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,184,0.0042,16922153,84605,85758,9655929,48043,50875
+200,32,184,0.0042,16922978,84611,85389,9766306,48609,58645
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,188,0.0043,17282153,86405,87558,9906002,49331,52491
+200,32,188,0.0043,17282978,86411,87189,9974165,49613,56721
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,192,0.0043,17642153,88205,89358,10089481,50268,52937
+200,32,192,0.0044,17642978,88211,88989,10187263,50734,52953
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,196,0.0044,18002153,90005,91158,10292606,51256,54507
+200,32,196,0.0044,18002978,90011,90789,10386920,51763,53773
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,200,0.0045,18362153,91805,92958,10466174,52144,54851
+200,32,200,0.0045,18362978,91811,92589,10593326,52744,54962
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,204,0.0045,18722153,93605,94758,10710242,53145,77999
+200,32,204,0.0045,18722978,93611,94389,10791966,53796,55775
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,208,0.0046,19082153,95405,96558,10872705,54177,57081
+200,32,208,0.0046,19082978,95411,96189,10993938,54691,56692
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,212,0.0047,19442153,97205,98358,11284063,56244,58937
+200,32,212,0.0047,19442978,97211,97989,11183564,55716,57663
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,216,0.0047,19802153,99005,100158,11267668,56162,58869
+200,32,216,0.0047,19802978,99011,99789,11413409,56842,65317
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,220,0.0048,20162153,100805,101958,11510801,57350,60362
+200,32,220,0.0049,20162978,100811,101589,11747337,57952,85917
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,224,0.0051,20522153,102605,103758,11730908,58406,61013
+200,32,224,0.0049,20522978,102611,103389,11967444,58993,147575
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,228,0.0050,20882153,104405,105558,11891323,59260,62051
+200,32,228,0.0050,20882978,104411,105189,12176974,59986,107137
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,232,0.0050,21242153,106205,107358,12083458,60220,63113
+200,32,232,0.0051,21242978,106211,106989,12243039,61011,62843
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,236,0.0050,21602153,108005,109158,12290078,61234,68599
+200,32,236,0.0051,21602978,108011,108789,12454738,61985,74677
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,240,0.0051,21962153,109805,110958,12547828,62267,88616
+200,32,240,0.0051,21962978,109811,110589,12632612,62912,64911
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,244,0.0052,22322153,111605,112758,12674066,63146,66333
+200,32,244,0.0052,22322978,111611,112389,12844679,63954,74316
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,248,0.0052,22682153,113405,114558,12882346,64155,67081
+200,32,248,0.0053,22682978,113411,114189,13049050,65048,67067
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,252,0.0053,23042153,115205,116358,13140221,65490,68231
+200,32,252,0.0054,23042978,115211,115989,13274577,66113,68093
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,256,0.0054,23402153,117005,118158,13331460,66431,69187
+200,32,256,0.0054,23402978,117011,117789,13479975,67191,69232
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,260,0.0054,23762153,118805,119958,13531478,67456,70141
+200,32,260,0.0055,23762978,118811,119589,13702476,68321,70257
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,264,0.0055,24122153,120605,121758,13710546,68246,81094
+200,32,264,0.0055,24122978,120611,121389,13885554,69178,71473
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,268,0.0055,24482153,122405,123558,13890638,69208,72412
+200,32,268,0.0056,24482978,122411,123189,14091173,70236,72538
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,272,0.0056,24842153,124205,125358,14130816,70366,88752
+200,32,272,0.0057,24842978,124211,124989,14277355,71142,73153
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,276,0.0057,25202153,126005,127158,14355067,71208,93990
+200,32,276,0.0057,25202978,126011,126789,14477479,72149,74585
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,280,0.0057,25562153,127805,128958,14513593,72251,85857
+200,32,280,0.0058,25562978,127811,128589,14807542,73365,106386
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,284,0.0059,25922153,129605,130758,14800806,73802,76775
+200,32,284,0.0059,25922978,129611,130389,14919273,74349,83988
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,288,0.0059,26282153,131405,132558,14959572,74579,77267
+200,32,288,0.0060,26282978,131411,132189,15262342,75369,108903
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,292,0.0059,26642153,133205,134358,15130033,75389,78361
+200,32,292,0.0061,26642978,133211,133989,15457489,76550,112579
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,296,0.0060,27002153,135005,136158,15314583,76370,79151
+200,32,296,0.0061,27002978,135011,135789,15587890,77470,113796
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,300,0.0061,27362153,136805,137958,15515700,77373,80055
+200,32,300,0.0063,27362978,136811,137589,15736737,78474,80976
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,304,0.0061,27722153,138605,139758,15739536,78395,81351
+200,32,304,0.0062,27722978,138611,139389,15931699,79424,85309
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,308,0.0062,28082153,140405,141558,15910915,79341,82085
+200,32,308,0.0064,28082978,140411,141189,16127895,80426,82181
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,312,0.0063,28442153,142205,143358,16119259,80297,83271
+200,32,312,0.0063,28442978,142211,142989,16353667,81487,91316
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,316,0.0063,28802153,144005,145158,16376727,81668,84481
+200,32,316,0.0064,28802978,144011,144789,16544730,82526,84583
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,320,0.0064,29162153,145805,146958,16575917,82685,85800
+200,32,320,0.0064,29162978,145811,146589,16778054,83692,85621
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,324,0.0065,29522153,147605,148758,16752101,83529,86861
+200,32,324,0.0065,29522978,147611,148389,16975790,84670,86933
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,328,0.0065,29882153,149405,150558,16931954,84456,87199
+200,32,328,0.0066,29882978,149411,150189,17193806,85651,95908
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,332,0.0066,30242153,151205,152358,17129562,85462,88022
+200,32,332,0.0067,30242978,151211,151989,17391042,86658,92746
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,336,0.0067,30602153,153005,154158,17522378,87337,90235
+200,32,336,0.0067,30602978,153011,153789,17579650,87566,101073
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,340,0.0067,30962153,154805,155958,17525540,87379,89947
+200,32,340,0.0068,30962978,154811,155589,17823659,88601,131503
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,344,0.0068,31322153,156605,157758,17811817,88413,169057
+200,32,344,0.0069,31322978,156611,157389,18045749,89720,131352
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,348,0.0069,31682153,158405,159558,17999372,89772,92601
+200,32,348,0.0069,31682978,158411,159189,18233228,90790,129666
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,352,0.0069,32042153,160205,161358,18204371,90776,101494
+200,32,352,0.0070,32042978,160211,160989,18429938,91908,93827
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,356,0.0070,32402153,162005,163158,18393456,91621,107055
+200,32,356,0.0071,32402978,162011,162789,18723870,92891,169000
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,360,0.0070,32762153,163805,164958,18567077,92476,114024
+200,32,360,0.0071,32762978,163811,164589,18839189,93872,104313
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,364,0.0072,33122153,165605,166758,18749614,93562,96291
+200,32,364,0.0072,33122978,165611,166389,19052230,94828,108456
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,368,0.0073,33482153,167405,168558,18957503,94465,97467
+200,32,368,0.0072,33482978,167411,168189,19224348,95828,106832
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,372,0.0072,33842153,169205,170358,19137907,95471,98421
+200,32,372,0.0073,33842978,169211,169989,19409746,96825,98825
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,376,0.0073,34202153,171005,172158,19350029,96457,99505
+200,32,376,0.0074,34202978,171011,171789,19635914,97934,100015
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,380,0.0075,34562153,172805,173958,19657158,97897,122483
+200,32,380,0.0075,34562978,172811,173589,19901265,99194,108856
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,384,0.0075,34922153,174605,175758,20019224,98872,199167
+200,32,384,0.0075,34922978,174611,175389,20087150,100132,113306
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,388,0.0075,35282153,176405,177558,19999785,99747,102911
+200,32,388,0.0076,35282978,176411,177189,20289560,101187,111225
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,392,0.0077,35642153,178205,179358,20188679,100586,121054
+200,32,392,0.0076,35642978,178211,178989,20478069,102158,104431
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,396,0.0076,36002153,180005,181158,20368637,101583,105060
+200,32,396,0.0077,36002978,180011,180789,20703541,103136,118462
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,400,0.0077,36362153,181805,182958,20628698,102607,152896
+200,32,400,0.0078,36362978,181811,182589,20889687,104097,116051
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,404,0.0078,36722153,183605,184758,20759711,103503,111551
+200,32,404,0.0078,36722978,183611,184389,21103371,105019,150497
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,408,0.0078,37082153,185405,186558,21008339,104552,136230
+200,32,408,0.0079,37082978,185411,186189,21343392,106235,146574
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,412,0.0080,37442153,187205,188358,21248565,105961,109252
+200,32,412,0.0080,37442978,187211,187989,21499750,107213,116228
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,416,0.0080,37802153,189005,190158,21446394,106998,110446
+200,32,416,0.0081,37802978,189011,189789,21769516,108354,153304
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,420,0.0081,38162153,190805,191958,21618503,107795,119989
+200,32,420,0.0082,38162978,190811,191589,22016040,109333,166344
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,424,0.0081,38522153,192605,193758,21778142,108604,112064
+200,32,424,0.0082,38522978,192611,193389,22124948,110298,112586
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,428,0.0081,38882153,194405,195558,21989784,109653,120306
+200,32,428,0.0083,38882978,194411,195189,22375892,111391,164691
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,432,0.0082,39242153,196205,197358,22191881,110730,113916
+200,32,432,0.0083,39242978,196211,196989,22605417,112244,161120
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,436,0.0083,39602153,198005,199158,22373426,111587,115657
+200,32,436,0.0084,39602978,198011,198789,22698406,113231,115888
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,440,0.0084,39962153,199805,200958,22596402,112638,130342
+200,32,440,0.0084,39962978,199811,200589,22946025,114347,124840
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,444,0.0084,40322153,201605,202758,22868323,114041,124888
+200,32,444,0.0085,40322978,201611,202389,23138571,115404,122324
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,448,0.0085,40682153,203405,204558,23084361,115132,128588
+200,32,448,0.0086,40682978,203411,204189,23382319,116666,118990
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,452,0.0086,41042153,205205,206358,23255449,115787,156348
+200,32,452,0.0086,41042978,205211,205989,23582320,117634,123005
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,456,0.0088,41402153,207005,208158,23400730,116742,119985
+200,32,456,0.0087,41402978,207011,207789,23777586,118606,121054
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,460,0.0087,41762153,208805,209958,23616057,117782,125672
+200,32,460,0.0088,41762978,208811,209589,24021078,119638,157473
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,464,0.0088,42122153,210605,211758,23845815,118769,150383
+200,32,464,0.0089,42122978,210611,211389,24177273,120536,137152
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,468,0.0089,42482153,212405,213558,23982677,119580,123029
+200,32,468,0.0089,42482978,212411,213189,24354431,121510,124378
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,472,0.0090,42842153,214205,215358,24183894,120688,124270
+200,32,472,0.0090,42842978,214211,214989,24680874,122798,163001
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,476,0.0090,43202153,216005,217158,24479273,122149,125974
+200,32,476,0.0092,43202978,216011,216789,24806941,123695,126112
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,480,0.0091,43562153,217805,218958,24768939,123125,164217
+200,32,480,0.0091,43562978,217811,218589,25036974,124855,131240
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,484,0.0092,43922153,219605,220758,24828983,123895,127390
+200,32,484,0.0092,43922978,219611,220389,25277560,125834,159926
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,488,0.0091,44282153,221405,222558,25011559,124768,128788
+200,32,488,0.0093,44282978,221411,222189,25492002,126931,169890
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,492,0.0092,44642153,223205,224358,25219550,125760,132732
+200,32,492,0.0094,44642978,223211,223989,25799993,127811,292316
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,496,0.0093,45002153,225005,226158,25447017,126853,140428
+200,32,496,0.0094,45002978,225011,225789,25879076,128748,186367
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,500,0.0093,45362153,226805,227958,25586059,127650,131094
+200,32,500,0.0094,45362978,226811,227589,26021482,129705,143377
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,504,0.0094,45722153,228605,229758,25796559,128739,131932
+200,32,504,0.0095,45722978,228611,229389,26309697,130875,185497
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,508,0.0095,46082153,230405,231558,26122261,130275,141242
+200,32,508,0.0096,46082978,230411,231189,26445482,131853,134810
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,512,0.0095,46442153,232205,233358,26303806,130890,135216
+200,32,512,0.0097,46442978,232211,232989,26722882,133313,135480
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,516,0.0096,46802153,234005,235158,26441241,131860,137807
+200,32,516,0.0097,46802978,234011,234789,26902984,134116,143429
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,520,0.0097,47162153,235805,236958,26620814,132726,144193
+200,32,520,0.0098,47162978,235811,236589,27143327,135173,182663
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,524,0.0097,47522153,237605,238758,26895547,133979,180810
+200,32,524,0.0101,47522978,237611,238389,27899728,139067,143412
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,528,0.0098,47882153,239405,240558,27103175,134594,195038
+200,32,528,0.0099,47882978,239411,240189,27539695,137281,153792
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,532,0.0099,48242153,241205,242358,27216804,135653,148537
+200,32,532,0.0100,48242978,241211,241989,27665652,137957,156345
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,536,0.0100,48602153,243005,244158,27609711,137157,225927
+200,32,536,0.0102,48602978,243011,243789,27888664,139123,142069
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,540,0.0101,48962153,244805,245958,27856165,138525,222412
+200,32,540,0.0102,48962978,244811,245589,28116288,140162,167093
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,544,0.0101,49322153,246605,247758,27949313,139206,146089
+200,32,544,0.0102,49322978,246611,247389,28395864,141365,191687
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,548,0.0102,49682153,248405,249558,28071639,140106,144061
+200,32,548,0.0105,49682978,248411,249189,28539300,142352,144923
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,552,0.0102,50042153,250205,251358,28221254,140771,147826
+200,32,552,0.0104,50042978,250211,250989,28772000,143499,153080
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,556,0.0103,50402153,252005,253158,28466442,141994,145849
+200,32,556,0.0104,50402978,252011,252789,28943938,144344,160802
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,560,0.0105,50762153,253805,254958,28785863,142904,194917
+200,32,560,0.0105,50762978,253811,254589,29192011,145318,205574
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,564,0.0105,51122153,255605,256758,28851831,143902,156411
+200,32,564,0.0106,51122978,255611,256389,29371768,146296,173660
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,568,0.0106,51482153,257405,258558,29223120,145608,162476
+200,32,568,0.0107,51482978,257411,258189,29607085,147402,185216
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,572,0.0108,51842153,259205,260358,29438332,146788,151895
+200,32,572,0.0109,51842978,259211,259989,29760468,148529,150992
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,576,0.0108,52202153,261005,262158,29557331,147210,151262
+200,32,576,0.0108,52202978,261011,261789,30001693,149671,152448
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,580,0.0108,52562153,262805,263958,29704990,148198,158557
+200,32,580,0.0109,52562978,262811,263589,30194219,150474,161954
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,584,0.0108,52922153,264605,265758,29996452,149016,250006
+200,32,584,0.0110,52922978,264611,265389,30465237,151575,196784
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,588,0.0109,53282153,266405,267558,30123135,150270,154069
+200,32,588,0.0112,53282978,266411,267189,30866027,152658,345805
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,592,0.0110,53642153,268205,269358,30283611,150978,165439
+200,32,592,0.0112,53642978,268211,268989,30806266,153631,162459
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,596,0.0110,54002153,270005,271158,30512807,152128,156216
+200,32,596,0.0112,54002978,270011,270789,31013348,154624,161083
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,600,0.0111,54362153,271805,272958,30713954,153227,157015
+200,32,600,0.0113,54362978,271811,272589,31227644,155782,158034
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,604,0.0113,54722153,273605,274758,31116246,155098,162946
+200,32,604,0.0115,54722978,273611,274389,31534633,156837,219588
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,608,0.0113,55082153,275405,276558,31292429,155792,166047
+200,32,608,0.0114,55082978,275411,276189,31675474,157869,168332
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,612,0.0113,55442153,277205,278358,31367681,156312,187819
+200,32,612,0.0115,55442978,277211,277989,31953436,158989,218652
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,616,0.0114,55802153,279005,280158,31509163,156923,173955
+200,32,616,0.0116,55802978,279011,279789,32108644,160138,180416
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,620,0.0115,56162153,280805,281958,31751550,158349,162413
+200,32,620,0.0116,56162978,280811,281589,32277424,160849,182393
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,624,0.0116,56522153,282605,283758,32010052,159426,164990
+200,32,624,0.0118,56522978,282611,283389,32423394,161797,164245
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,628,0.0116,56882153,284405,285558,32270071,160471,206182
+200,32,628,0.0117,56882978,284411,285189,32609412,162678,167394
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,632,0.0118,57242153,286205,287358,32379821,161317,166154
+200,32,632,0.0118,57242978,286211,286989,32869379,163975,168634
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,636,0.0118,57602153,288005,289158,32621237,162719,174455
+200,32,636,0.0119,57602978,288011,288789,33151217,165037,223167
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,640,0.0118,57962153,289805,290958,32760054,163283,174727
+200,32,640,0.0119,57962978,289811,290589,33341299,166215,181218
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,644,0.0119,58322153,291605,292758,32895462,163973,168568
+200,32,644,0.0121,58322978,291611,292389,33649260,167751,199967
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,648,0.0119,58682153,293405,294558,33046462,164805,176098
+200,32,648,0.0121,58682978,293411,294189,33719599,168221,178799
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,652,0.0120,59042153,295205,296358,33305627,166069,179927
+200,32,652,0.0122,59042978,295211,295989,34067206,169536,235514
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,656,0.0121,59402153,297005,298158,33611780,166989,248127
+200,32,656,0.0122,59402978,297011,297789,34164102,170144,235618
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,660,0.0121,59762153,298805,299958,33791922,168433,184984
+200,32,660,0.0123,59762978,298811,299589,34456636,171594,235316
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,664,0.0121,60122153,300605,301758,33927065,169140,182483
+200,32,664,0.0124,60122978,300611,301389,34541178,172177,211827
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,668,0.0124,60482153,302405,303558,34476798,171567,188679
+200,32,668,0.0124,60482978,302411,303189,34905159,173832,222673
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,672,0.0123,60842153,304205,305358,34350802,171240,175365
+200,32,672,0.0126,60842978,304211,304989,34988298,174422,188003
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,676,0.0123,61202153,306005,307158,34529315,172118,202239
+200,32,676,0.0126,61202978,306011,306789,35263092,175911,185984
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,680,0.0124,61562153,307805,308958,34716545,172878,244909
+200,32,680,0.0127,61562978,307811,308589,35503073,176323,305860
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,684,0.0126,61922153,309605,310758,35111667,174820,186347
+200,32,684,0.0128,61922978,309611,310389,35672483,178036,180851
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,688,0.0126,62282153,311405,312558,35200811,175517,179013
+200,32,688,0.0128,62282978,311411,312189,35790039,178289,217803
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,692,0.0126,62642153,313205,314358,35391859,176015,252609
+200,32,692,0.0128,62642978,313211,313989,36045752,179866,188983
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,696,0.0127,63002153,315005,316158,35696188,177815,200506
+200,32,696,0.0130,63002978,315011,315789,36175144,180438,195986
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,700,0.0128,63362153,316805,317958,35825556,178736,191521
+200,32,700,0.0131,63362978,316811,317589,36529049,182248,184897
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,704,0.0129,63722153,318605,319758,36008866,179237,218743
+200,32,704,0.0130,63722978,318611,319389,36611747,182765,185703
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,708,0.0129,64082153,320405,321558,36282257,180511,214158
+200,32,708,0.0130,64082978,320411,321189,36811496,183626,191140
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,712,0.0129,64442153,322205,323358,36251857,180793,191833
+200,32,712,0.0131,64442978,322211,322989,37060383,184588,255521
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,716,0.0131,64802153,324005,325158,36828270,182903,229477
+200,32,716,0.0132,64802978,324011,324789,37267356,185684,240236
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,720,0.0130,65162153,325805,326958,36775140,183107,213910
+200,32,720,0.0132,65162978,325811,326589,37393434,186562,204926
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,724,0.0131,65522153,327605,328758,36946255,184028,240244
+200,32,724,0.0133,65522978,327611,328389,37611724,187635,203956
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,728,0.0132,65882153,329405,330558,37189420,185485,206103
+200,32,728,0.0135,65882978,329411,330189,37844476,188685,217329
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,732,0.0133,66242153,331205,332358,37526856,187108,192940
+200,32,732,0.0136,66242978,331211,331989,38097715,189879,238003
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,736,0.0134,66602153,333005,334158,37747623,188004,201070
+200,32,736,0.0136,66602978,333011,333789,38249665,190960,193797
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,740,0.0134,66962153,334805,335958,37844347,188709,198675
+200,32,740,0.0137,66962978,334811,335589,38496135,191882,202980
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,744,0.0134,67322153,336605,337758,37874634,189009,203611
+200,32,744,0.0136,67322978,336611,337389,38643004,192776,211409
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,748,0.0136,67682153,338405,339558,38360815,190893,193995
+200,32,748,0.0138,67682978,338411,339189,38834497,193752,204307
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,752,0.0137,68042153,340205,341358,38702052,192377,222451
+200,32,752,0.0139,68042978,340211,340989,39026422,194674,207102
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,756,0.0136,68402153,342005,343158,38548177,192033,249435
+200,32,756,0.0139,68402978,342011,342789,39292510,195755,242534
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,760,0.0138,68762153,343805,344958,39152996,194437,272148
+200,32,760,0.0140,68762978,343811,344589,39445808,196904,199749
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,764,0.0138,69122153,345605,346758,39070056,194876,204988
+200,32,764,0.0140,69122978,345611,346389,39707448,198140,208159
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,768,0.0138,69482153,347405,348558,39192485,195337,208507
+200,32,768,0.0141,69482978,347411,348189,39961335,199314,213386
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,772,0.0139,69842153,349205,350358,39509976,197063,216644
+200,32,772,0.0142,69842978,349211,349989,40195551,200268,262442
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,776,0.0140,70202153,351005,352158,39643299,197720,238164
+200,32,776,0.0143,70202978,351011,351789,40369481,201262,243178
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,780,0.0141,70562153,352805,353958,40047395,199611,212284
+200,32,780,0.0143,70562978,352811,353589,40454251,201889,204769
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,784,0.0142,70922153,354605,355758,40474213,201350,218018
+200,32,784,0.0143,70922978,354611,355389,40804167,203132,292206
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,788,0.0143,71282153,356405,357558,40369690,200941,270257
+200,32,788,0.0144,71282978,356411,357189,40880258,203888,220805
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,792,0.0143,71642153,358205,359358,40667289,202430,244792
+200,32,792,0.0145,71642978,358211,358989,41141375,205195,222680
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,796,0.0145,72002153,360005,361158,41245212,205315,244622
+200,32,796,0.0145,72002978,360011,360789,41346667,205890,276619
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,800,0.0144,72362153,361805,362958,41042713,204407,249254
+200,32,800,0.0146,72362978,361811,362589,41586665,207290,248916
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,804,0.0145,72722153,363605,364758,41137099,205254,211445
+200,32,804,0.0147,72722978,363611,364389,41696398,208106,211465
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,808,0.0145,73082153,365405,366558,41267168,205869,210553
+200,32,808,0.0148,73082978,365411,366189,41978951,209272,255137
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,812,0.0146,73442153,367205,368358,41538016,207083,242270
+200,32,812,0.0148,73442978,367211,367989,42187366,209918,283393
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,816,0.0147,73802153,369005,370158,41856937,208198,257079
+200,32,816,0.0149,73802978,369011,369789,42482639,211214,322437
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,820,0.0149,74162153,370805,371958,42581251,211598,220361
+200,32,820,0.0149,74162978,370811,371589,42512865,212010,227823
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,824,0.0148,74522153,372605,373758,42106929,210144,214780
+200,32,824,0.0151,74522978,372611,373389,42861251,213412,278868
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,828,0.0151,74882153,374405,375558,42954101,213100,216189
+200,32,828,0.0151,74882978,374411,375189,42979335,214191,262439
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,832,0.0150,75242153,376205,377358,42591682,212393,217281
+200,32,832,0.0152,75242978,376211,376989,43402619,215543,296991
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,836,0.0150,75602153,378005,379158,42833889,213607,225147
+200,32,836,0.0152,75602978,378011,378789,43382253,216450,232179
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,840,0.0151,75962153,379805,380958,42888365,213833,258282
+200,32,840,0.0154,75962978,379811,380589,43665001,217538,261020
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,844,0.0151,76322153,381605,382758,43234463,215605,228741
+200,32,844,0.0154,76322978,381611,382389,43762162,218196,232967
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,848,0.0152,76682153,383405,384558,43340508,216058,240778
+200,32,848,0.0156,76682978,383411,384189,44077885,219619,233562
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,852,0.0154,77042153,385205,386358,43964132,218702,263707
+200,32,852,0.0155,77042978,385211,385989,44269902,220266,357562
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,856,0.0155,77402153,387005,388158,43738562,218168,230126
+200,32,856,0.0156,77402978,387011,387789,44458368,221658,275183
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,860,0.0154,77762153,388805,389958,44071523,219837,238185
+200,32,860,0.0156,77762978,388811,389589,44599845,222530,244104
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,864,0.0155,78122153,390605,391758,44411093,221177,232408
+200,32,864,0.0158,78122978,390611,391389,44856987,223898,229495
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,868,0.0157,78482153,392405,393558,44526424,222013,237960
+200,32,868,0.0157,78482978,392411,393189,45070339,224667,268426
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,872,0.0158,78842153,394205,395358,45188815,224084,346189
+200,32,872,0.0158,78842978,394211,394989,45243346,225686,238504
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,876,0.0156,79202153,396005,397158,44700630,222996,237268
+200,32,876,0.0160,79202978,396011,396789,45425044,226467,285843
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,880,0.0158,79562153,397805,398958,45208957,224813,328325
+200,32,880,0.0160,79562978,397811,398589,45637897,227585,255503
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,884,0.0159,79922153,399605,400758,45474656,226439,239215
+200,32,884,0.0163,79922978,399611,400389,45922301,228540,294854
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,888,0.0160,80282153,401405,402558,45766475,227867,240911
+200,32,888,0.0161,80282978,401411,402189,46210377,229936,317062
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,892,0.0160,80642153,403205,404358,45940503,228819,243891
+200,32,892,0.0161,80642978,403211,403989,46224897,230736,244030
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,896,0.0161,81002153,405005,406158,45973712,229111,241548
+200,32,896,0.0163,81002978,405011,405789,46706945,232252,393574
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,900,0.0162,81362153,406805,407958,46447521,230613,346027
+200,32,900,0.0163,81362978,406811,407589,46846573,233803,243774
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,904,0.0163,81722153,408605,409758,46859527,233117,305572
+200,32,904,0.0165,81722978,408611,409389,47211102,235424,247115
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,908,0.0164,82082153,410405,411558,47123610,234871,284329
+200,32,908,0.0165,82082978,410411,411189,47420647,236067,308146
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,912,0.0166,82442153,412205,413358,47816182,237201,366650
+200,32,912,0.0167,82442978,412211,412989,47664515,237299,252663
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,916,0.0166,82802153,414005,415158,47456504,236767,248921
+200,32,916,0.0166,82802978,414011,414789,47825500,238210,307878
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,920,0.0165,83162153,415805,416958,47592162,237459,265738
+200,32,920,0.0168,83162978,415811,416589,48024315,239591,249230
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,924,0.0167,83522153,417605,418758,48057683,239541,276783
+200,32,924,0.0168,83522978,417611,418389,48204506,240348,286103
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,928,0.0167,83882153,419405,420558,48171706,239841,277682
+200,32,928,0.0168,83882978,419411,420189,48474452,241766,272232
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,932,0.0170,84242153,421205,422358,48721591,242883,245719
+200,32,932,0.0169,84242978,421211,421989,48643328,242408,310910
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,936,0.0169,84602153,423005,424158,48377712,241387,254877
+200,32,936,0.0170,84602978,423011,423789,49041567,243670,350571
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,940,0.0169,84962153,424805,425958,48721762,242855,255300
+200,32,940,0.0171,84962978,424811,425589,49009612,244295,313509
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,944,0.0170,85322153,426605,427758,49035991,243372,370914
+200,32,944,0.0171,85322978,426611,427389,49257311,245620,259650
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,948,0.0171,85682153,428405,429558,49070436,244800,262067
+200,32,948,0.0172,85682978,428411,429189,49415667,246533,254714
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,952,0.0171,86042153,430205,431358,49234273,245636,258683
+200,32,952,0.0172,86042978,430211,430989,49711139,247671,319628
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,956,0.0172,86402153,432005,433158,49586922,247001,316148
+200,32,956,0.0174,86402978,432011,432789,49856592,248552,271876
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,960,0.0172,86762153,433805,434958,49640943,247637,284307
+200,32,960,0.0174,86762978,433811,434589,50136102,249978,265617
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,964,0.0177,87122153,435605,436758,51436885,256453,266477
+200,32,964,0.0176,87122978,435611,436389,50925446,253713,295499
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,968,0.0178,87482153,437405,438558,51146832,254991,267861
+200,32,968,0.0178,87482978,437411,438189,51035835,253858,318894
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,972,0.0177,87842153,439205,440358,51377929,256333,274159
+200,32,972,0.0177,87842978,439211,439989,51188317,255334,306288
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,976,0.0179,88202153,441005,442158,51360933,256336,265049
+200,32,976,0.0178,88202978,441011,441789,51436023,256205,289239
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,980,0.0179,88562153,442805,443958,51845435,258521,293602
+200,32,980,0.0179,88562978,442811,443589,51703656,257814,300077
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,984,0.0180,88922153,444605,445758,52129373,259818,262711
+200,32,984,0.0179,88922978,444611,445389,51801305,257947,349721
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,988,0.0181,89282153,446405,447558,52262963,260903,278224
+200,32,988,0.0181,89282978,446411,447189,52056854,259676,262216
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,992,0.0182,89642153,448205,449358,52407317,261432,272849
+200,32,992,0.0182,89642978,448211,448989,52237864,260535,269494
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,996,0.0184,90002153,450005,451158,53286503,265403,275404
+200,32,996,0.0183,90002978,450011,450789,52526126,262024,274178
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,1000,0.0182,90362153,451805,452958,53051777,264487,273734
+200,32,1000,0.0182,90362978,451811,452589,52578843,262284,265526
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,1004,0.0183,90722153,453605,454758,53153647,264834,340140
+200,32,1004,0.0183,90722978,453611,454389,52896370,263840,273834
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,1008,0.0183,91082153,455405,456558,53025643,264711,274578
+200,32,1008,0.0183,91082978,455411,456189,53074476,264385,308471
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,1012,0.0185,91442153,457205,458358,53709439,267192,353247
+200,32,1012,0.0184,91442978,457211,457989,53382079,266422,284446
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,1016,0.0186,91802153,459005,460158,54036527,268786,339099
+200,32,1016,0.0186,91802978,459011,459789,53434221,266486,275700
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,1020,0.0186,92162153,460805,461958,54154888,269844,327020
+200,32,1020,0.0186,92162978,460811,461589,53712164,268036,277528
iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)
-200,32,1024,0.0183,92522153,462605,463758,52875104,262839,332332
-mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.ins_cyc.bin.csv .
+200,32,1024,0.0187,92522978,462611,463389,53754294,268076,276795
+mv /gpfs/wolf/trn003/scratch/aherten//poisson2d.ins_cyc.bin.csv .
</pre>
</div>
</div>
@@ -13823,7 +13855,7 @@ mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.ins_cyc.bin.csv .
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
-<p>Once the run is completed, let's have a look at the data!</p>
+<p>Once the run is completed, let's study the data!</p>
<p>This can be done best in the interactive version of the Jupyter Notebook. In case this version of the description is unavailable to you, call the Makefile target <code>make graph_task1</code> (either with X forwarding, or download the resulting PDF).</p>
</div>
@@ -13831,10 +13863,11 @@ mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.ins_cyc.bin.csv .
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
-<div class="prompt input_prompt">In [3]:</div>
+<div class="prompt input_prompt">In [1]:</div>
<div class="inner_cell">
<div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
+<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">common</span>
@@ -13847,16 +13880,36 @@ mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.ins_cyc.bin.csv .
</div>
</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div><div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Execute the following cell if you want to switch to color-blind-safer colors</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [ ]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sns</span><span class="o">.</span><span class="n">set_palette</span><span class="p">(</span><span class="s2">"colorblind"</span><span class="p">)</span>
+</pre></div>
+
+ </div>
+</div>
+</div>
+
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
-<div class="prompt input_prompt">In [77]:</div>
+<div class="prompt input_prompt">In [2]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">'figure.figsize'</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="mi">14</span><span class="p">,</span> <span class="mi">6</span><span class="p">]</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">"poisson2d.ins_cyc.bin.csv"</span><span class="p">,</span> <span class="n">skiprows</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">50000</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span> <span class="c1"># Read in the CSV file from the bench run; parse with Pandas</span>
-<span class="n">common</span><span class="o">.</span><span class="n">normalize</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="s2">"PM_INST_CMPL (min)"</span><span class="p">,</span> <span class="s2">"Instructions / Loop Iteration"</span><span class="p">)</span> <span class="c1"># Normalize to each grid cell</span>
-<span class="n">common</span><span class="o">.</span><span class="n">normalize</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="s2">"PM_RUN_CYC (min)"</span><span class="p">,</span> <span class="s2">"Cycles / Loop Iteration"</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">"Grid Points"</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">"nx"</span><span class="p">]</span> <span class="o">*</span> <span class="n">df</span><span class="p">[</span><span class="s2">"ny"</span><span class="p">]</span> <span class="c1"># Add a new column of the number of grid points (the product of nx and ny)</span>
<span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span> <span class="c1"># Display the head of the Pandas dataframe</span>
</pre></div>
@@ -13870,7 +13923,7 @@ mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.ins_cyc.bin.csv .
<div class="output_area">
- <div class="prompt output_prompt">Out[77]:</div>
+ <div class="prompt output_prompt">Out[2]:</div>
@@ -13903,8 +13956,7 @@ mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.ins_cyc.bin.csv .
<th>PM_RUN_CYC (total)</th>
<th>PM_RUN_CYC (min)</th>
<th>PM_RUN_CYC (max)</th>
- <th>Instructions / Loop Iteration</th>
- <th>Cycles / Loop Iteration</th>
+ <th>Grid Points</th>
</tr>
</thead>
<tbody>
@@ -13914,14 +13966,13 @@ mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.ins_cyc.bin.csv .
<td>32</td>
<td>4</td>
<td>0.0012</td>
- <td>548153</td>
- <td>2735</td>
- <td>3888</td>
- <td>266883</td>
- <td>1237</td>
- <td>4793</td>
- <td>21.367188</td>
- <td>9.664062</td>
+ <td>572978</td>
+ <td>2861</td>
+ <td>3639</td>
+ <td>261330</td>
+ <td>1235</td>
+ <td>4684</td>
+ <td>128</td>
</tr>
<tr>
<th>1</th>
@@ -13929,14 +13980,13 @@ mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.ins_cyc.bin.csv .
<td>32</td>
<td>8</td>
<td>0.0014</td>
- <td>1082153</td>
- <td>5405</td>
- <td>6558</td>
- <td>668819</td>
- <td>3214</td>
- <td>6623</td>
- <td>21.113281</td>
- <td>12.554688</td>
+ <td>1082978</td>
+ <td>5411</td>
+ <td>6189</td>
+ <td>601962</td>
+ <td>2914</td>
+ <td>5099</td>
+ <td>256</td>
</tr>
<tr>
<th>2</th>
@@ -13944,44 +13994,41 @@ mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.ins_cyc.bin.csv .
<td>32</td>
<td>12</td>
<td>0.0014</td>
- <td>1442153</td>
- <td>7205</td>
- <td>8358</td>
- <td>872913</td>
- <td>4187</td>
- <td>11640</td>
- <td>18.763021</td>
- <td>10.903646</td>
+ <td>1442978</td>
+ <td>7211</td>
+ <td>7989</td>
+ <td>811603</td>
+ <td>3992</td>
+ <td>5761</td>
+ <td>384</td>
</tr>
<tr>
<th>3</th>
<td>200</td>
<td>32</td>
<td>16</td>
- <td>0.0015</td>
- <td>1802153</td>
- <td>9005</td>
- <td>10158</td>
- <td>1077532</td>
- <td>5254</td>
- <td>8147</td>
- <td>17.587891</td>
- <td>10.261719</td>
+ <td>0.0014</td>
+ <td>1802978</td>
+ <td>9011</td>
+ <td>9789</td>
+ <td>1017305</td>
+ <td>4988</td>
+ <td>7017</td>
+ <td>512</td>
</tr>
<tr>
<th>4</th>
<td>200</td>
<td>32</td>
<td>20</td>
- <td>0.0016</td>
- <td>2162153</td>
- <td>10805</td>
- <td>11958</td>
- <td>1277957</td>
- <td>6209</td>
- <td>9015</td>
- <td>16.882812</td>
- <td>9.701562</td>
+ <td>0.0015</td>
+ <td>2162978</td>
+ <td>10811</td>
+ <td>11589</td>
+ <td>1221559</td>
+ <td>6002</td>
+ <td>7999</td>
+ <td>640</td>
</tr>
</tbody>
</table>
@@ -13993,16 +14040,24 @@ mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.ins_cyc.bin.csv .
</div>
</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div><div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Let's have a look at the counters we've just measured and see how they scaling with increasing number of grid points.</p>
+<p><em>In the following, we are always using the minimal value of the counter (indicated by »(min)«) as this should give us an estimate of the best achievable result of the architecture.</em></p>
+
+</div>
+</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
-<div class="prompt input_prompt">In [78]:</div>
+<div class="prompt input_prompt">In [3]:</div>
<div class="inner_cell">
<div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Plot Cycles and Instructions - both per grid cell</span>
-<span class="n">fig</span><span class="p">,</span> <span class="p">(</span><span class="n">ax1</span><span class="p">,</span> <span class="n">ax2</span><span class="p">)</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="n">df</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"nx"</span><span class="p">)[</span><span class="s2">"Cycles / Loop Iteration"</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">True</span><span class="p">);</span>
-<span class="n">df</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"nx"</span><span class="p">)[</span><span class="s2">"Instructions / Loop Iteration"</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax2</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">True</span><span class="p">);</span>
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="p">(</span><span class="n">ax1</span><span class="p">,</span> <span class="n">ax2</span><span class="p">)</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">df</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"Grid Points"</span><span class="p">)[</span><span class="s2">"PM_RUN_CYC (min)"</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">True</span><span class="p">);</span>
+<span class="n">df</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"Grid Points"</span><span class="p">)[</span><span class="s2">"PM_INST_CMPL (min)"</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax2</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">True</span><span class="p">);</span>
</pre></div>
</div>
@@ -14021,7 +14076,7 @@ mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.ins_cyc.bin.csv .
<div class="output_png output_subarea ">
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
"
>
</div>
@@ -14035,7 +14090,176 @@ mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.ins_cyc.bin.csv .
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
-<p>What is your result? What value do the graphs come asymptotically close too?</p>
+<p>Although some slight variations can be seen for run cycles for many grid points, the correlation looks quite linear (as one would naively expect). Let's test that by fitting a linear function!</p>
+<p><em>The details of the fitting have been extracted into dedicated function, <code>print_and_return_fit()</code>, of the <code>common.py</code> helper file. If you're interested, <a href="common.py">go have a look at it</a>.</em></p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [4]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">linear_function</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">):</span>
+ <span class="k">return</span> <span class="n">a</span><span class="o">*</span><span class="n">x</span><span class="o">+</span><span class="n">b</span>
+</pre></div>
+
+ </div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [25]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fit_parameters</span><span class="p">,</span> <span class="n">fit_covariance</span> <span class="o">=</span> <span class="n">common</span><span class="o">.</span><span class="n">print_and_return_fit</span><span class="p">(</span>
+ <span class="p">[</span><span class="s2">"PM_RUN_CYC (min)"</span><span class="p">,</span> <span class="s2">"PM_INST_CMPL (min)"</span><span class="p">],</span>
+ <span class="n">df</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"Grid Points"</span><span class="p">),</span>
+ <span class="n">linear_function</span><span class="p">,</span>
+ <span class="n">format_uncertainty</span><span class="o">=</span><span class="s2">".4f"</span>
+<span class="p">)</span>
+</pre></div>
+
+ </div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+ <div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Counter PM_RUN_CYC (min) is proportional to the grid points (nx*ny) by a factor of 8.1021 (± 0.0057)
+Counter PM_INST_CMPL (min) is proportional to the grid points (nx*ny) by a factor of 14.0630 (± 0.0003)
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div><div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Let's overlay our fits to the graphs from before.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [6]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="p">(</span><span class="n">ax1</span><span class="p">,</span> <span class="n">ax2</span><span class="p">)</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="k">for</span> <span class="n">ax</span><span class="p">,</span> <span class="n">pmu_counter</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">([</span><span class="n">ax1</span><span class="p">,</span> <span class="n">ax2</span><span class="p">],</span> <span class="p">[</span><span class="s2">"PM_RUN_CYC (min)"</span><span class="p">,</span> <span class="s2">"PM_INST_CMPL (min)"</span><span class="p">]):</span>
+ <span class="n">df</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"Grid Points"</span><span class="p">)[</span><span class="n">pmu_counter</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">True</span><span class="p">);</span>
+ <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
+ <span class="n">df</span><span class="p">[</span><span class="s2">"Grid Points"</span><span class="p">],</span>
+ <span class="n">linear_function</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s2">"Grid Points"</span><span class="p">],</span> <span class="o">*</span><span class="n">fit_parameters</span><span class="p">[</span><span class="n">pmu_counter</span><span class="p">]),</span>
+ <span class="n">linestyle</span><span class="o">=</span><span class="s2">"--"</span><span class="p">,</span>
+ <span class="n">label</span><span class="o">=</span><span class="s2">"Fit: </span><span class="si">{:.2f}</span><span class="s2"> * x + </span><span class="si">{:.2f}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="o">*</span><span class="n">fit_parameters</span><span class="p">[</span><span class="n">pmu_counter</span><span class="p">])</span>
+ <span class="p">)</span>
+ <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">();</span>
+</pre></div>
+
+ </div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+ <div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img 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
+"
+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div><div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Please execute the next cell to summarize the first task.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [38]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">"The algorithm under investigation runs about </span><span class="si">{:.0f}</span><span class="s2"> cycles and executes about </span><span class="si">{:.0f}</span><span class="s2"> instructions per grid point"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+ <span class="o">*</span><span class="p">[</span><span class="n">fit_parameters</span><span class="p">[</span><span class="n">pmu_counter</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">pmu_counter</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">"PM_RUN_CYC (min)"</span><span class="p">,</span> <span class="s2">"PM_INST_CMPL (min)"</span><span class="p">]]</span>
+<span class="p">))</span>
+</pre></div>
+
+ </div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+ <div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>The algorithm under investigation runs about 8 cycles and executes about 14 instructions per grid point
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div><div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><strong>Bonus:</strong></p>
+<p>The linear fits also calculate a y intersection (»<code>b</code>«). How do you interpret this value?</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div><div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>The y axis intersection; that is, <code>b</code> of the linear fit, is the inherent overhead of the program execution. Even if our program would not compute any stencil operation at all for any grid point, it would still complete this many (~1800) instructions and run this many (~680) cycles. Interestingly, it is also the unparallelizable overhead of this (toy) example.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div><div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
<p>We are revisiting the graph in a little while.</p>
<p><a href="#toc">Back to top</a></p>
@@ -14048,7 +14272,8 @@ mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.ins_cyc.bin.csv .
<h2 id="Task-2:-Measuring-Loads-and-Stores">Task 2: Measuring Loads and Stores<a class="anchor-link" href="#Task-2:-Measuring-Loads-and-Stores">¶</a></h2><p><a name="task2"></a></p>
<p>Looking at the source code, how many loads and stores from / to memory do you expect? Have a look at the loop which we instrumented.</p>
<p>Let's compare your estimate to what the system actually does!</p>
-<p><a name="task2-a"></a><strong>TASK A</strong>: Please measure counters for loads and stores. See the TODOs in <a href="/edit/Tasks/poisson2d.ld_st.c"><code>poisson2d.ld_st.c</code></a>. This time, implement <code>PM_LD_CMPL</code> and <code>PM_ST_CMPL</code>.</p>
+<h3 id="Task-A">Task A<a class="anchor-link" href="#Task-A">¶</a></h3><p><a name="task2-a"></a></p>
+<p>Please measure counters for loads and stores. See the TODOs in <a href="/edit/Tasks/poisson2d.ld_st.c"><code>poisson2d.ld_st.c</code></a>. This time, implement <code>PM_LD_CMPL</code> and <code>PM_ST_CMPL</code>.</p>
<p>Compile with <code>make task2</code>, test your program with a single run with <code>make run_task2</code>, and then finally submit a benchmarking run to the batch system with <code>make bench_task2</code>. The following cell will take care of all this.</p>
<p><a href="#toc">Back to top</a></p>
@@ -14057,7 +14282,7 @@ mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.ins_cyc.bin.csv .
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
-<div class="prompt input_prompt">In [11]:</div>
+<div class="prompt input_prompt">In [3]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">!</span>make bench_task2
@@ -14077,524 +14302,523 @@ mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.ins_cyc.bin.csv .
<div class="output_subarea output_stream output_stdout output_text">
-<pre>gcc -DUSE_DOUBLE -Ofast -std=c99 -lm -lpapi poisson2d.ld_st.c -o poisson2d.ld_st.bin
-bsub -W 60 -nnodes 1 -Is jsrun -n 1 -c 1 -g ALL_GPUS ./bench.sh poisson2d.ld_st.bin /gpfs/wolf/gen110/scratch/aherten//poisson2d.ld_st.bin.csv
-Job <4032> is submitted to default queue <batch>.
+<pre>bsub -W 60 -nnodes 1 -Is -P TRN003 jsrun -n 1 -c 1 -g ALL_GPUS ./bench.sh poisson2d.ld_st.bin /gpfs/wolf/trn003/scratch/aherten//poisson2d.ld_st.bin.csv
+Job <24416> is submitted to default queue <batch>.
<<Waiting for dispatch ...>>
<<Starting on login1>>
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,4,0.0012,95115,474,789,21343,106,249
+200,32,4,0.0012,119819,598,817,32902,164,266
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,8,0.0014,137115,684,999,33343,166,309
+200,32,8,0.0013,161819,808,1027,56902,284,386
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,12,0.0014,197115,984,1299,45343,226,369
+200,32,12,0.0014,221819,1108,1327,71902,359,461
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,16,0.0015,257115,1284,1599,63343,316,459
+200,32,16,0.0015,281819,1408,1627,86902,434,536
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,20,0.0016,317115,1584,1899,75343,376,519
+200,32,20,0.0015,341819,1708,1927,101902,509,611
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,24,0.0016,377115,1884,2199,93343,466,609
+200,32,24,0.0016,401819,2008,2227,116902,584,686
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,28,0.0017,437115,2184,2499,105343,526,669
+200,32,28,0.0016,461819,2308,2527,131902,659,761
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,32,0.0017,497115,2484,2799,123343,616,759
+200,32,32,0.0018,521819,2608,2827,146902,734,836
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,36,0.0018,557115,2784,3099,135343,676,819
+200,32,36,0.0018,581819,2908,3127,161902,809,911
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,40,0.0020,617115,3084,3399,153343,766,909
+200,32,40,0.0018,641819,3208,3427,176902,884,986
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,44,0.0019,677115,3384,3699,165343,826,969
+200,32,44,0.0019,701819,3508,3727,191902,959,1061
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,48,0.0020,737115,3684,3999,183343,916,1059
+200,32,48,0.0020,761819,3808,4027,206902,1034,1136
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,52,0.0021,797115,3984,4299,195343,976,1119
+200,32,52,0.0020,821819,4108,4327,221902,1109,1211
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,56,0.0021,857115,4284,4599,213343,1066,1209
+200,32,56,0.0021,881819,4408,4627,236902,1184,1286
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,60,0.0023,917115,4584,4899,225343,1126,1269
+200,32,60,0.0022,941819,4708,4927,251902,1259,1361
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,64,0.0023,977115,4884,5199,243343,1216,1359
+200,32,64,0.0023,1001819,5008,5227,266902,1334,1436
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,68,0.0024,1037115,5184,5499,255343,1276,1419
+200,32,68,0.0023,1061819,5308,5527,281902,1409,1511
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,72,0.0025,1097115,5484,5799,273343,1366,1509
+200,32,72,0.0025,1121819,5608,5827,296902,1484,1586
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,76,0.0025,1157115,5784,6099,285343,1426,1569
+200,32,76,0.0028,1181819,5908,6127,311902,1559,1661
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,80,0.0025,1217115,6084,6399,303343,1516,1659
+200,32,80,0.0025,1241819,6208,6427,326902,1634,1736
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,84,0.0026,1277115,6384,6699,315343,1576,1719
+200,32,84,0.0026,1301819,6508,6727,341902,1709,1811
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,88,0.0027,1337115,6684,6999,333343,1666,1809
+200,32,88,0.0026,1361819,6808,7027,356902,1784,1886
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,92,0.0027,1397115,6984,7299,345343,1726,1869
+200,32,92,0.0027,1421819,7108,7327,371902,1859,1961
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,96,0.0028,1457115,7284,7599,363343,1816,1959
+200,32,96,0.0028,1481819,7408,7627,386902,1934,2036
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,100,0.0029,1517115,7584,7899,375343,1876,2019
+200,32,100,0.0029,1541819,7708,7927,401902,2009,2111
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,104,0.0029,1577115,7884,8199,393343,1966,2109
+200,32,104,0.0029,1601819,8008,8227,416902,2084,2186
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,108,0.0030,1637115,8184,8499,405343,2026,2169
+200,32,108,0.0031,1661819,8308,8527,431902,2159,2261
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,112,0.0030,1697115,8484,8799,423343,2116,2259
+200,32,112,0.0030,1721819,8608,8827,446902,2234,2336
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,116,0.0031,1757115,8784,9099,435343,2176,2319
+200,32,116,0.0031,1781819,8908,9127,461902,2309,2411
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,120,0.0033,1817115,9084,9399,453343,2266,2409
+200,32,120,0.0032,1841819,9208,9427,476902,2384,2486
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,124,0.0032,1877115,9384,9699,465343,2326,2469
+200,32,124,0.0033,1901819,9508,9727,491902,2459,2561
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,128,0.0033,1937115,9684,9999,483343,2416,2559
+200,32,128,0.0033,1961819,9808,10027,506902,2534,2636
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,132,0.0034,1997115,9984,10299,495343,2476,2619
+200,32,132,0.0034,2021819,10108,10327,521902,2609,2711
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,136,0.0035,2057115,10284,10599,513343,2566,2709
+200,32,136,0.0035,2081819,10408,10627,536902,2684,2786
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,140,0.0035,2117115,10584,10899,525343,2626,2769
+200,32,140,0.0036,2141819,10708,10927,551902,2759,2861
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,144,0.0036,2177115,10884,11199,543343,2716,2859
+200,32,144,0.0036,2201819,11008,11227,566902,2834,2936
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,148,0.0036,2237115,11184,11499,555343,2776,2919
+200,32,148,0.0036,2261819,11308,11527,581902,2909,3011
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,152,0.0037,2297115,11484,11799,573343,2866,3009
+200,32,152,0.0037,2321819,11608,11827,596902,2984,3086
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,156,0.0038,2357115,11784,12099,585343,2926,3069
+200,32,156,0.0038,2381819,11908,12127,611902,3059,3161
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,160,0.0038,2417115,12084,12399,603343,3016,3159
+200,32,160,0.0040,2441819,12208,12427,626902,3134,3236
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,164,0.0039,2477115,12384,12699,615343,3076,3219
+200,32,164,0.0039,2501819,12508,12727,641902,3209,3311
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,168,0.0039,2537115,12684,12999,633343,3166,3309
+200,32,168,0.0040,2561819,12808,13027,656902,3284,3386
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,172,0.0040,2597115,12984,13299,645343,3226,3369
+200,32,172,0.0040,2621819,13108,13327,671902,3359,3461
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,176,0.0041,2657115,13284,13599,663343,3316,3459
+200,32,176,0.0041,2681819,13408,13627,686902,3434,3536
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,180,0.0041,2717115,13584,13899,675343,3376,3519
+200,32,180,0.0041,2741819,13708,13927,701902,3509,3611
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,184,0.0042,2777115,13884,14199,693343,3466,3609
+200,32,184,0.0042,2801819,14008,14227,716902,3584,3686
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,188,0.0043,2837115,14184,14499,705343,3526,3669
+200,32,188,0.0044,2861819,14308,14527,731902,3659,3761
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,192,0.0043,2897115,14484,14799,723343,3616,3759
+200,32,192,0.0044,2921819,14608,14827,746902,3734,3836
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,196,0.0044,2957115,14784,15099,735343,3676,3819
+200,32,196,0.0045,2981819,14908,15127,761902,3809,3911
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,200,0.0045,3017115,15084,15399,753343,3766,3909
+200,32,200,0.0045,3041819,15208,15427,776902,3884,3986
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,204,0.0045,3077115,15384,15699,765343,3826,3969
+200,32,204,0.0045,3101819,15508,15727,791902,3959,4061
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,208,0.0046,3137115,15684,15999,783343,3916,4059
+200,32,208,0.0046,3161819,15808,16027,806902,4034,4136
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,212,0.0047,3197115,15984,16299,795343,3976,4119
+200,32,212,0.0047,3221819,16108,16327,821902,4109,4211
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,216,0.0047,3257115,16284,16599,813343,4066,4209
+200,32,216,0.0047,3281819,16408,16627,836902,4184,4286
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,220,0.0048,3317115,16584,16899,825343,4126,4269
+200,32,220,0.0048,3341819,16708,16927,851902,4259,4361
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,224,0.0049,3377115,16884,17199,843343,4216,4359
+200,32,224,0.0049,3401819,17008,17227,866902,4334,4436
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,228,0.0049,3437115,17184,17499,855343,4276,4419
+200,32,228,0.0050,3461819,17308,17527,881902,4409,4511
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,232,0.0050,3497115,17484,17799,873343,4366,4509
+200,32,232,0.0050,3521819,17608,17827,896902,4484,4586
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,236,0.0051,3557115,17784,18099,885343,4426,4569
+200,32,236,0.0051,3581819,17908,18127,911902,4559,4661
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,240,0.0052,3617115,18084,18399,903343,4516,4659
+200,32,240,0.0051,3641819,18208,18427,926902,4634,4736
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,244,0.0052,3677115,18384,18699,915343,4576,4719
+200,32,244,0.0052,3701819,18508,18727,941902,4709,4811
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,248,0.0052,3737115,18684,18999,933343,4666,4809
+200,32,248,0.0053,3761819,18808,19027,956902,4784,4886
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,252,0.0054,3797115,18984,19299,945343,4726,4869
+200,32,252,0.0053,3821819,19108,19327,971902,4859,4961
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,256,0.0054,3857115,19284,19599,963343,4816,4959
+200,32,256,0.0054,3881819,19408,19627,986902,4934,5036
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,260,0.0054,3917115,19584,19899,975343,4876,5019
+200,32,260,0.0055,3941819,19708,19927,1001902,5009,5111
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,264,0.0055,3977115,19884,20199,993343,4966,5109
+200,32,264,0.0055,4001819,20008,20227,1016902,5084,5186
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,268,0.0056,4037115,20184,20499,1005343,5026,5169
+200,32,268,0.0056,4061819,20308,20527,1031902,5159,5261
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,272,0.0056,4097115,20484,20799,1023343,5116,5259
+200,32,272,0.0057,4121819,20608,20827,1046902,5234,5336
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,276,0.0057,4157115,20784,21099,1035343,5176,5319
+200,32,276,0.0057,4181819,20908,21127,1061902,5309,5411
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,280,0.0057,4217115,21084,21399,1053343,5266,5409
+200,32,280,0.0058,4241819,21208,21427,1076902,5384,5486
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,284,0.0058,4277115,21384,21699,1065343,5326,5469
+200,32,284,0.0059,4301819,21508,21727,1091902,5459,5561
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,288,0.0059,4337115,21684,21999,1083343,5416,5559
+200,32,288,0.0059,4361819,21808,22027,1106902,5534,5636
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,292,0.0059,4397115,21984,22299,1095343,5476,5619
+200,32,292,0.0060,4421819,22108,22327,1121902,5609,5711
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,296,0.0061,4457115,22284,22599,1113343,5566,5709
+200,32,296,0.0061,4481819,22408,22627,1136902,5684,5786
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,300,0.0061,4517115,22584,22899,1125343,5626,5769
+200,32,300,0.0061,4541819,22708,22927,1151902,5759,5861
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,304,0.0061,4577115,22884,23199,1143343,5716,5859
+200,32,304,0.0062,4601819,23008,23227,1166902,5834,5936
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,308,0.0062,4637115,23184,23499,1155343,5776,5919
+200,32,308,0.0063,4661819,23308,23527,1181902,5909,6011
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,312,0.0063,4697115,23484,23799,1173343,5866,6009
+200,32,312,0.0064,4721819,23608,23827,1196902,5984,6086
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,316,0.0064,4757115,23784,24099,1185343,5926,6069
+200,32,316,0.0066,4781819,23908,24127,1211902,6059,6161
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,320,0.0064,4817115,24084,24399,1203343,6016,6159
+200,32,320,0.0065,4841819,24208,24427,1226902,6134,6236
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,324,0.0065,4877115,24384,24699,1215343,6076,6219
+200,32,324,0.0065,4901819,24508,24727,1241902,6209,6311
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,328,0.0065,4937115,24684,24999,1233343,6166,6309
+200,32,328,0.0069,4961819,24808,25027,1256902,6284,6386
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,332,0.0066,4997115,24984,25299,1245343,6226,6369
+200,32,332,0.0066,5021819,25108,25327,1271902,6359,6461
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,336,0.0066,5057115,25284,25599,1263343,6316,6459
+200,32,336,0.0067,5081819,25408,25627,1286902,6434,6536
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,340,0.0068,5117115,25584,25899,1275343,6376,6519
+200,32,340,0.0068,5141819,25708,25927,1301902,6509,6611
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,344,0.0068,5177115,25884,26199,1293343,6466,6609
+200,32,344,0.0069,5201819,26008,26227,1316902,6584,6686
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,348,0.0069,5237115,26184,26499,1305343,6526,6669
+200,32,348,0.0069,5261819,26308,26527,1331902,6659,6761
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,352,0.0071,5297115,26484,26799,1323343,6616,6759
+200,32,352,0.0070,5321819,26608,26827,1346902,6734,6836
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,356,0.0070,5357115,26784,27099,1335343,6676,6819
+200,32,356,0.0070,5381819,26908,27127,1361902,6809,6911
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,360,0.0070,5417115,27084,27399,1353343,6766,6909
+200,32,360,0.0071,5441819,27208,27427,1376902,6884,6986
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,364,0.0071,5477115,27384,27699,1365343,6826,6969
+200,32,364,0.0072,5501819,27508,27727,1391902,6959,7061
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,368,0.0072,5537115,27684,27999,1383343,6916,7059
+200,32,368,0.0072,5561819,27808,28027,1406902,7034,7136
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,372,0.0073,5597115,27984,28299,1395343,6976,7119
+200,32,372,0.0073,5621819,28108,28327,1421902,7109,7211
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,376,0.0073,5657115,28284,28599,1413343,7066,7209
+200,32,376,0.0074,5681819,28408,28627,1436902,7184,7286
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,380,0.0074,5717115,28584,28899,1425343,7126,7269
+200,32,380,0.0074,5741819,28708,28927,1451902,7259,7361
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,384,0.0074,5777115,28884,29199,1443343,7216,7359
+200,32,384,0.0075,5801819,29008,29227,1466902,7334,7436
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,388,0.0075,5837115,29184,29499,1455343,7276,7419
+200,32,388,0.0076,5861819,29308,29527,1481902,7409,7511
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,392,0.0076,5897115,29484,29799,1473343,7366,7509
+200,32,392,0.0076,5921819,29608,29827,1496902,7484,7586
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,396,0.0076,5957115,29784,30099,1485343,7426,7569
+200,32,396,0.0077,5981819,29908,30127,1511902,7559,7661
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,400,0.0078,6017115,30084,30399,1503343,7516,7659
+200,32,400,0.0078,6041819,30208,30427,1526902,7634,7736
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,404,0.0078,6077115,30384,30699,1515343,7576,7719
+200,32,404,0.0079,6101819,30508,30727,1541902,7709,7811
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,408,0.0078,6137115,30684,30999,1533343,7666,7809
+200,32,408,0.0079,6161819,30808,31027,1556902,7784,7886
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,412,0.0079,6197115,30984,31299,1545343,7726,7869
+200,32,412,0.0080,6221819,31108,31327,1571902,7859,7961
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,416,0.0080,6257115,31284,31599,1563343,7816,7959
+200,32,416,0.0081,6281819,31408,31627,1586902,7934,8036
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,420,0.0080,6317115,31584,31899,1575343,7876,8019
+200,32,420,0.0081,6341819,31708,31927,1601902,8009,8111
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,424,0.0081,6377115,31884,32199,1593343,7966,8109
+200,32,424,0.0082,6401819,32008,32227,1616902,8084,8186
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,428,0.0081,6437115,32184,32499,1605343,8026,8169
+200,32,428,0.0082,6461819,32308,32527,1631902,8159,8261
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,432,0.0082,6497115,32484,32799,1623343,8116,8259
+200,32,432,0.0085,6521819,32608,32827,1646902,8234,8336
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,436,0.0083,6557115,32784,33099,1635343,8176,8319
+200,32,436,0.0084,6581819,32908,33127,1661902,8309,8411
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,440,0.0083,6617115,33084,33399,1653343,8266,8409
+200,32,440,0.0084,6641819,33208,33427,1676902,8384,8486
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,444,0.0084,6677115,33384,33699,1665343,8326,8469
+200,32,444,0.0085,6701819,33508,33727,1691902,8459,8561
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,448,0.0085,6737115,33684,33999,1683343,8416,8559
+200,32,448,0.0087,6761819,33808,34027,1706902,8534,8636
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,452,0.0085,6797115,33984,34299,1695343,8476,8619
+200,32,452,0.0087,6821819,34108,34327,1721902,8609,8711
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,456,0.0086,6857115,34284,34599,1713343,8566,8709
+200,32,456,0.0087,6881819,34408,34627,1736902,8684,8786
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,460,0.0087,6917115,34584,34899,1725343,8626,8769
+200,32,460,0.0088,6941819,34708,34927,1751902,8759,8861
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,464,0.0088,6977115,34884,35199,1743343,8716,8859
+200,32,464,0.0088,7001819,35008,35227,1766902,8834,8936
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,468,0.0088,7037115,35184,35499,1755343,8776,8919
+200,32,468,0.0089,7061819,35308,35527,1781902,8909,9011
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,472,0.0089,7097115,35484,35799,1773343,8866,9009
+200,32,472,0.0090,7121819,35608,35827,1796902,8984,9086
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,476,0.0090,7157115,35784,36099,1785343,8926,9069
+200,32,476,0.0091,7181819,35908,36127,1811902,9059,9161
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,480,0.0090,7217115,36084,36399,1803343,9016,9159
+200,32,480,0.0091,7241819,36208,36427,1826902,9134,9236
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,484,0.0091,7277115,36384,36699,1815343,9076,9219
+200,32,484,0.0092,7301819,36508,36727,1841902,9209,9311
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,488,0.0091,7337115,36684,36999,1833343,9166,9309
+200,32,488,0.0093,7361819,36808,37027,1856902,9284,9386
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,492,0.0092,7397115,36984,37299,1845343,9226,9369
+200,32,492,0.0094,7421819,37108,37327,1871902,9359,9461
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,496,0.0093,7457115,37284,37599,1863343,9316,9459
+200,32,496,0.0095,7481819,37408,37627,1886902,9434,9536
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,500,0.0093,7517115,37584,37899,1875343,9376,9519
+200,32,500,0.0094,7541819,37708,37927,1901902,9509,9611
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,504,0.0094,7577115,37884,38199,1893343,9466,9609
+200,32,504,0.0095,7601819,38008,38227,1916902,9584,9686
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,508,0.0095,7637115,38184,38499,1905343,9526,9669
+200,32,508,0.0096,7661819,38308,38527,1931902,9659,9761
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,512,0.0095,7697115,38484,38799,1923343,9616,9759
+200,32,512,0.0097,7721819,38608,38827,1946902,9734,9836
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,516,0.0096,7757115,38784,39099,1938343,9691,9834
+200,32,516,0.0098,7781819,38908,39127,1961902,9809,9911
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,520,0.0097,7817115,39084,39399,1953343,9766,9909
+200,32,520,0.0098,7841819,39208,39427,1976902,9884,9986
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,524,0.0097,7877115,39384,39699,1968343,9841,9984
+200,32,524,0.0099,7901819,39508,39727,1991902,9959,10061
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,528,0.0098,7937115,39684,39999,1983343,9916,10059
+200,32,528,0.0099,7961819,39808,40027,2006902,10034,10136
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,532,0.0099,7997115,39984,40299,1998343,9991,10134
+200,32,532,0.0100,8021819,40108,40327,2021902,10109,10211
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,536,0.0100,8057115,40284,40599,2013343,10066,10209
+200,32,536,0.0101,8081819,40408,40627,2036902,10184,10286
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,540,0.0101,8117115,40584,40899,2028343,10141,10284
+200,32,540,0.0101,8141819,40708,40927,2051902,10259,10361
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,544,0.0101,8177115,40884,41199,2043343,10216,10359
+200,32,544,0.0103,8201819,41008,41227,2066902,10334,10436
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,548,0.0102,8237115,41184,41499,2058343,10291,10434
+200,32,548,0.0103,8261819,41308,41527,2081902,10409,10511
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,552,0.0103,8297115,41484,41799,2073343,10366,10509
+200,32,552,0.0104,8321819,41608,41827,2096902,10484,10586
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,556,0.0104,8357115,41784,42099,2088343,10441,10584
+200,32,556,0.0106,8381819,41908,42127,2111902,10559,10661
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,560,0.0104,8417115,42084,42399,2103343,10516,10659
+200,32,560,0.0106,8441819,42208,42427,2126902,10634,10736
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,564,0.0105,8477115,42384,42699,2118343,10591,10734
+200,32,564,0.0106,8501819,42508,42727,2141902,10709,10811
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,568,0.0106,8537115,42684,42999,2133343,10666,10809
+200,32,568,0.0107,8561819,42808,43027,2156902,10784,10886
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,572,0.0106,8597115,42984,43299,2148343,10741,10884
+200,32,572,0.0108,8621819,43108,43327,2171902,10859,10961
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,576,0.0107,8657115,43284,43599,2163343,10816,10959
+200,32,576,0.0109,8681819,43408,43627,2186902,10934,11036
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,580,0.0109,8717115,43584,43899,2178343,10891,11034
+200,32,580,0.0110,8741819,43708,43927,2201902,11009,11111
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,584,0.0108,8777115,43884,44199,2193343,10966,11109
+200,32,584,0.0110,8801819,44008,44227,2216902,11084,11186
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,588,0.0110,8837115,44184,44499,2208343,11041,11184
+200,32,588,0.0110,8861819,44308,44527,2231902,11159,11261
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,592,0.0110,8897115,44484,44799,2223343,11116,11259
+200,32,592,0.0111,8921819,44608,44827,2246902,11234,11336
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,596,0.0111,8957115,44784,45099,2238343,11191,11334
+200,32,596,0.0113,8981819,44908,45127,2261902,11309,11411
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,600,0.0111,9017115,45084,45399,2253343,11266,11409
+200,32,600,0.0113,9041819,45208,45427,2276902,11384,11486
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,604,0.0112,9077115,45384,45699,2268343,11341,11484
+200,32,604,0.0114,9101819,45508,45727,2291902,11459,11561
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,608,0.0113,9137115,45684,45999,2283343,11416,11559
+200,32,608,0.0115,9161819,45808,46027,2306902,11534,11636
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,612,0.0113,9197115,45984,46299,2298343,11491,11634
+200,32,612,0.0115,9221819,46108,46327,2321902,11609,11711
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,616,0.0114,9257115,46284,46599,2313343,11566,11709
+200,32,616,0.0115,9281819,46408,46627,2336902,11684,11786
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,620,0.0115,9317115,46584,46899,2328343,11641,11784
+200,32,620,0.0116,9341819,46708,46927,2351902,11759,11861
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,624,0.0115,9377115,46884,47199,2343343,11716,11859
+200,32,624,0.0117,9401819,47008,47227,2366902,11834,11936
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,628,0.0115,9437115,47184,47499,2358343,11791,11934
+200,32,628,0.0117,9461819,47308,47527,2381902,11909,12011
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,632,0.0117,9497115,47484,47799,2373343,11866,12009
+200,32,632,0.0118,9521819,47608,47827,2396902,11984,12086
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,636,0.0118,9557115,47784,48099,2388343,11941,12084
+200,32,636,0.0119,9581819,47908,48127,2411902,12059,12161
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,640,0.0119,9617115,48084,48399,2403343,12016,12159
+200,32,640,0.0119,9641819,48208,48427,2426902,12134,12236
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,644,0.0118,9677115,48384,48699,2418343,12091,12234
+200,32,644,0.0121,9701819,48508,48727,2441902,12209,12311
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,648,0.0119,9737115,48684,48999,2433343,12166,12309
+200,32,648,0.0121,9761819,48808,49027,2456902,12284,12386
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,652,0.0121,9797115,48984,49299,2448343,12241,12384
+200,32,652,0.0121,9821819,49108,49327,2471902,12359,12461
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,656,0.0121,9857115,49284,49599,2463343,12316,12459
+200,32,656,0.0122,9881819,49408,49627,2486902,12434,12536
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,660,0.0122,9917115,49584,49899,2478343,12391,12534
+200,32,660,0.0123,9941819,49708,49927,2501902,12509,12611
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,664,0.0122,9977115,49884,50199,2493343,12466,12609
+200,32,664,0.0123,10001819,50008,50227,2516902,12584,12686
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,668,0.0123,10037115,50184,50499,2508343,12541,12684
+200,32,668,0.0124,10061819,50308,50527,2531902,12659,12761
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,672,0.0123,10097115,50484,50799,2523343,12616,12759
+200,32,672,0.0124,10121819,50608,50827,2546902,12734,12836
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,676,0.0125,10157115,50784,51099,2538343,12691,12834
+200,32,676,0.0126,10181819,50908,51127,2561902,12809,12911
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,680,0.0124,10217115,51084,51399,2553343,12766,12909
+200,32,680,0.0126,10241819,51208,51427,2576902,12884,12986
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,684,0.0125,10277115,51384,51699,2568343,12841,12984
+200,32,684,0.0127,10301819,51508,51727,2591902,12959,13061
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,688,0.0126,10337115,51684,51999,2583343,12916,13059
+200,32,688,0.0128,10361819,51808,52027,2606902,13034,13136
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,692,0.0126,10397115,51984,52299,2598343,12991,13134
+200,32,692,0.0128,10421819,52108,52327,2621902,13109,13211
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,696,0.0127,10457115,52284,52599,2613343,13066,13209
+200,32,696,0.0129,10481819,52408,52627,2636902,13184,13286
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,700,0.0128,10517115,52584,52899,2628343,13141,13284
+200,32,700,0.0131,10541819,52708,52927,2651902,13259,13361
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,704,0.0129,10577115,52884,53199,2643343,13216,13359
+200,32,704,0.0131,10601819,53008,53227,2666902,13334,13436
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,708,0.0129,10637115,53184,53499,2658343,13291,13434
+200,32,708,0.0130,10661819,53308,53527,2681902,13409,13511
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,712,0.0129,10697115,53484,53799,2673343,13366,13509
+200,32,712,0.0131,10721819,53608,53827,2696902,13484,13586
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,716,0.0130,10757115,53784,54099,2688343,13441,13584
+200,32,716,0.0132,10781819,53908,54127,2711902,13559,13661
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,720,0.0130,10817115,54084,54399,2703343,13516,13659
+200,32,720,0.0132,10841819,54208,54427,2726902,13634,13736
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,724,0.0132,10877115,54384,54699,2718343,13591,13734
+200,32,724,0.0134,10901819,54508,54727,2741902,13709,13811
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,728,0.0131,10937115,54684,54999,2733343,13666,13809
+200,32,728,0.0134,10961819,54808,55027,2756902,13784,13886
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,732,0.0133,10997115,54984,55299,2748343,13741,13884
+200,32,732,0.0134,11021819,55108,55327,2771902,13859,13961
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,736,0.0135,11057115,55284,55599,2763343,13816,13959
+200,32,736,0.0135,11081819,55408,55627,2786902,13934,14036
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,740,0.0134,11117115,55584,55899,2778343,13891,14034
+200,32,740,0.0137,11141819,55708,55927,2801902,14009,14111
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,744,0.0134,11177115,55884,56199,2793343,13966,14109
+200,32,744,0.0138,11201819,56008,56227,2816902,14084,14186
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,748,0.0135,11237115,56184,56499,2808343,14041,14184
+200,32,748,0.0137,11261819,56308,56527,2831902,14159,14261
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,752,0.0136,11297115,56484,56799,2823343,14116,14259
+200,32,752,0.0138,11321819,56608,56827,2846902,14234,14336
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,756,0.0136,11357115,56784,57099,2838343,14191,14334
+200,32,756,0.0139,11381819,56908,57127,2861902,14309,14411
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,760,0.0138,11417115,57084,57399,2853343,14266,14409
+200,32,760,0.0140,11441819,57208,57427,2876902,14384,14486
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,764,0.0139,11477115,57384,57699,2868343,14341,14484
+200,32,764,0.0140,11501819,57508,57727,2891902,14459,14561
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,768,0.0138,11537115,57684,57999,2883343,14416,14559
+200,32,768,0.0141,11561819,57808,58027,2906902,14534,14636
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,772,0.0140,11597115,57984,58299,2898343,14491,14634
+200,32,772,0.0141,11621819,58108,58327,2921902,14609,14711
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,776,0.0140,11657115,58284,58599,2913343,14566,14709
+200,32,776,0.0142,11681819,58408,58627,2936902,14684,14786
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,780,0.0142,11717115,58584,58899,2928343,14641,14784
+200,32,780,0.0143,11741819,58708,58927,2951902,14759,14861
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,784,0.0141,11777115,58884,59199,2943343,14716,14859
+200,32,784,0.0144,11801819,59008,59227,2966902,14834,14936
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,788,0.0143,11837115,59184,59499,2958343,14791,14934
+200,32,788,0.0144,11861819,59308,59527,2981902,14909,15011
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,792,0.0143,11897115,59484,59799,2973343,14866,15009
+200,32,792,0.0145,11921819,59608,59827,2996902,14984,15086
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,796,0.0146,11957115,59784,60099,2988343,14941,15084
+200,32,796,0.0145,11981819,59908,60127,3011902,15059,15161
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,800,0.0144,12017115,60084,60399,3003343,15016,15159
+200,32,800,0.0147,12041819,60208,60427,3026902,15134,15236
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,804,0.0145,12077115,60384,60699,3018343,15091,15234
+200,32,804,0.0147,12101819,60508,60727,3041902,15209,15311
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,808,0.0146,12137115,60684,60999,3033343,15166,15309
+200,32,808,0.0148,12161819,60808,61027,3056902,15284,15386
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,812,0.0146,12197115,60984,61299,3048343,15241,15384
+200,32,812,0.0148,12221819,61108,61327,3071902,15359,15461
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,816,0.0146,12257115,61284,61599,3063343,15316,15459
+200,32,816,0.0150,12281819,61408,61627,3086902,15434,15536
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,820,0.0148,12317115,61584,61899,3078343,15391,15534
+200,32,820,0.0149,12341819,61708,61927,3101902,15509,15611
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,824,0.0149,12377115,61884,62199,3093343,15466,15609
+200,32,824,0.0150,12401819,62008,62227,3116902,15584,15686
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,828,0.0149,12437115,62184,62499,3108343,15541,15684
+200,32,828,0.0151,12461819,62308,62527,3131902,15659,15761
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,832,0.0149,12497115,62484,62799,3123343,15616,15759
+200,32,832,0.0152,12521819,62608,62827,3146902,15734,15836
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,836,0.0151,12557115,62784,63099,3138343,15691,15834
+200,32,836,0.0152,12581819,62908,63127,3161902,15809,15911
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,840,0.0150,12617115,63084,63399,3153343,15766,15909
+200,32,840,0.0153,12641819,63208,63427,3176902,15884,15986
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,844,0.0152,12677115,63384,63699,3168343,15841,15984
+200,32,844,0.0153,12701819,63508,63727,3191902,15959,16061
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,848,0.0152,12737115,63684,63999,3183343,15916,16059
+200,32,848,0.0154,12761819,63808,64027,3206902,16034,16136
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,852,0.0153,12797115,63984,64299,3198343,15991,16134
+200,32,852,0.0155,12821819,64108,64327,3221902,16109,16211
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,856,0.0153,12857115,64284,64599,3213343,16066,16209
+200,32,856,0.0156,12881819,64408,64627,3236902,16184,16286
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,860,0.0155,12917115,64584,64899,3228343,16141,16284
+200,32,860,0.0156,12941819,64708,64927,3251902,16259,16361
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,864,0.0156,12977115,64884,65199,3243343,16216,16359
+200,32,864,0.0157,13001819,65008,65227,3266902,16334,16436
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,868,0.0157,13037115,65184,65499,3258343,16291,16434
+200,32,868,0.0158,13061819,65308,65527,3281902,16409,16511
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,872,0.0156,13097115,65484,65799,3273343,16366,16509
+200,32,872,0.0159,13121819,65608,65827,3296902,16484,16586
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,876,0.0157,13157115,65784,66099,3288343,16441,16584
+200,32,876,0.0159,13181819,65908,66127,3311902,16559,16661
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,880,0.0158,13217115,66084,66399,3303343,16516,16659
+200,32,880,0.0160,13241819,66208,66427,3326902,16634,16736
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,884,0.0158,13277115,66384,66699,3318343,16591,16734
+200,32,884,0.0160,13301819,66508,66727,3341902,16709,16811
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,888,0.0159,13337115,66684,66999,3333343,16666,16809
+200,32,888,0.0161,13361819,66808,67027,3356902,16784,16886
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,892,0.0160,13397115,66984,67299,3348343,16741,16884
+200,32,892,0.0162,13421819,67108,67327,3371902,16859,16961
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,896,0.0161,13457115,67284,67599,3363343,16816,16959
+200,32,896,0.0163,13481819,67408,67627,3386902,16934,17036
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,900,0.0162,13517115,67584,67899,3378343,16891,17034
+200,32,900,0.0164,13541819,67708,67927,3401902,17009,17111
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,904,0.0163,13577115,67884,68199,3393343,16966,17109
+200,32,904,0.0165,13601819,68008,68227,3416902,17084,17186
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,908,0.0164,13637115,68184,68499,3408343,17041,17184
+200,32,908,0.0165,13661819,68308,68527,3431902,17159,17261
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,912,0.0165,13697115,68484,68799,3423343,17116,17259
+200,32,912,0.0166,13721819,68608,68827,3446902,17234,17336
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,916,0.0165,13757115,68784,69099,3438343,17191,17334
+200,32,916,0.0166,13781819,68908,69127,3461902,17309,17411
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,920,0.0165,13817115,69084,69399,3453343,17266,17409
+200,32,920,0.0167,13841819,69208,69427,3476902,17384,17486
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,924,0.0168,13877115,69384,69699,3468343,17341,17484
+200,32,924,0.0168,13901819,69508,69727,3491902,17459,17561
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,928,0.0167,13937115,69684,69999,3483343,17416,17559
+200,32,928,0.0169,13961819,69808,70027,3506902,17534,17636
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,932,0.0169,13997115,69984,70299,3498343,17491,17634
+200,32,932,0.0175,14021819,70108,70327,3521902,17609,17711
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,936,0.0168,14057115,70284,70599,3513343,17566,17709
+200,32,936,0.0170,14081819,70408,70627,3536902,17684,17786
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,940,0.0169,14117115,70584,70899,3528343,17641,17784
+200,32,940,0.0171,14141819,70708,70927,3551902,17759,17861
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,944,0.0169,14177115,70884,71199,3543343,17716,17859
+200,32,944,0.0171,14201819,71008,71227,3566902,17834,17936
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,948,0.0170,14237115,71184,71499,3558343,17791,17934
+200,32,948,0.0172,14261819,71308,71527,3581902,17909,18011
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,952,0.0171,14297115,71484,71799,3573343,17866,18009
+200,32,952,0.0172,14321819,71608,71827,3596902,17984,18086
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,956,0.0173,14357115,71784,72099,3588343,17941,18084
+200,32,956,0.0173,14381819,71908,72127,3611902,18059,18161
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,960,0.0172,14417115,72084,72399,3603343,18016,18159
+200,32,960,0.0174,14441819,72208,72427,3626902,18134,18236
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,964,0.0177,14477115,72384,72699,3618343,18091,18234
+200,32,964,0.0176,14501819,72508,72727,3641902,18209,18311
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,968,0.0177,14537115,72684,72999,3633343,18166,18309
+200,32,968,0.0178,14561819,72808,73027,3656902,18284,18386
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,972,0.0177,14597115,72984,73299,3648343,18241,18384
+200,32,972,0.0177,14621819,73108,73327,3671902,18359,18461
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,976,0.0179,14657115,73284,73599,3663343,18316,18459
+200,32,976,0.0178,14681819,73408,73627,3686902,18434,18536
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,980,0.0180,14717115,73584,73899,3678343,18391,18534
+200,32,980,0.0179,14741819,73708,73927,3701902,18509,18611
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,984,0.0180,14777115,73884,74199,3693343,18466,18609
+200,32,984,0.0179,14801819,74008,74227,3716902,18584,18686
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,988,0.0180,14837115,74184,74499,3708343,18541,18684
+200,32,988,0.0180,14861819,74308,74527,3731902,18659,18761
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,992,0.0181,14897115,74484,74799,3723343,18616,18759
+200,32,992,0.0181,14921819,74608,74827,3746902,18734,18836
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,996,0.0184,14957115,74784,75099,3738343,18691,18834
+200,32,996,0.0182,14981819,74908,75127,3761902,18809,18911
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,1000,0.0182,15017115,75084,75399,3753343,18766,18909
+200,32,1000,0.0182,15041819,75208,75427,3776902,18884,18986
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,1004,0.0183,15077115,75384,75699,3768343,18841,18984
+200,32,1004,0.0183,15101819,75508,75727,3791902,18959,19061
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,1008,0.0184,15137115,75684,75999,3783343,18916,19059
+200,32,1008,0.0183,15161819,75808,76027,3806902,19034,19136
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,1012,0.0185,15197115,75984,76299,3798343,18991,19134
+200,32,1012,0.0184,15221819,76108,76327,3821902,19109,19211
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,1016,0.0185,15257115,76284,76599,3813343,19066,19209
+200,32,1016,0.0185,15281819,76408,76627,3836902,19184,19286
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,1020,0.0186,15317115,76584,76899,3828343,19141,19284
+200,32,1020,0.0185,15341819,76708,76927,3851902,19259,19361
iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)
-200,32,1024,0.0183,15377115,76884,77199,3843343,19216,19359
-mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.ld_st.bin.csv .
+200,32,1024,0.0186,15401819,77008,77227,3866902,19334,19436
+mv /gpfs/wolf/trn003/scratch/aherten//poisson2d.ld_st.bin.csv .
</pre>
</div>
</div>
@@ -14606,19 +14830,18 @@ mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.ld_st.bin.csv .
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
-<p>Once the run finished, let's plot it again with the following cell (non-interactive: <code>make graph_task2a</code>).</p>
+<p>Once the run finished, let's plot it again in the course of the following cells (non-interactive: <code>make graph_task2a</code>).</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
-<div class="prompt input_prompt">In [6]:</div>
+<div class="prompt input_prompt">In [8]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_ldst</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">"poisson2d.ld_st.bin.csv"</span><span class="p">,</span> <span class="n">skiprows</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">50000</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
-<span class="n">common</span><span class="o">.</span><span class="n">normalize</span><span class="p">(</span><span class="n">df_ldst</span><span class="p">,</span> <span class="s2">"PM_LD_CMPL (min)"</span><span class="p">,</span> <span class="s2">"Loads / Loop Iteration"</span><span class="p">)</span>
-<span class="n">common</span><span class="o">.</span><span class="n">normalize</span><span class="p">(</span><span class="n">df_ldst</span><span class="p">,</span> <span class="s2">"PM_ST_CMPL (min)"</span><span class="p">,</span> <span class="s2">"Stores / Loop Iteration"</span><span class="p">)</span>
+<span class="n">df_ldst</span><span class="p">[</span><span class="s2">"Grid Points"</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_ldst</span><span class="p">[</span><span class="s2">"nx"</span><span class="p">]</span> <span class="o">*</span> <span class="n">df_ldst</span><span class="p">[</span><span class="s2">"ny"</span><span class="p">]</span>
<span class="n">df_ldst</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>
@@ -14632,7 +14855,7 @@ mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.ld_st.bin.csv .
<div class="output_area">
- <div class="prompt output_prompt">Out[6]:</div>
+ <div class="prompt output_prompt">Out[8]:</div>
@@ -14665,8 +14888,7 @@ mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.ld_st.bin.csv .
<th>PM_ST_CMPL (total)</th>
<th>PM_ST_CMPL (min)</th>
<th>PM_ST_CMPL (max)</th>
- <th>Loads / Loop Iteration</th>
- <th>Stores / Loop Iteration</th>
+ <th>Grid Points</th>
</tr>
</thead>
<tbody>
@@ -14676,29 +14898,27 @@ mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.ld_st.bin.csv .
<td>32</td>
<td>4</td>
<td>0.0012</td>
- <td>95115</td>
- <td>474</td>
- <td>789</td>
- <td>21343</td>
- <td>106</td>
- <td>249</td>
- <td>3.703125</td>
- <td>0.828125</td>
+ <td>119819</td>
+ <td>598</td>
+ <td>817</td>
+ <td>32902</td>
+ <td>164</td>
+ <td>266</td>
+ <td>128</td>
</tr>
<tr>
<th>1</th>
<td>200</td>
<td>32</td>
<td>8</td>
- <td>0.0014</td>
- <td>137115</td>
- <td>684</td>
- <td>999</td>
- <td>33343</td>
- <td>166</td>
- <td>309</td>
- <td>2.671875</td>
- <td>0.648438</td>
+ <td>0.0013</td>
+ <td>161819</td>
+ <td>808</td>
+ <td>1027</td>
+ <td>56902</td>
+ <td>284</td>
+ <td>386</td>
+ <td>256</td>
</tr>
<tr>
<th>2</th>
@@ -14706,14 +14926,13 @@ mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.ld_st.bin.csv .
<td>32</td>
<td>12</td>
<td>0.0014</td>
- <td>197115</td>
- <td>984</td>
- <td>1299</td>
- <td>45343</td>
- <td>226</td>
- <td>369</td>
- <td>2.562500</td>
- <td>0.588542</td>
+ <td>221819</td>
+ <td>1108</td>
+ <td>1327</td>
+ <td>71902</td>
+ <td>359</td>
+ <td>461</td>
+ <td>384</td>
</tr>
<tr>
<th>3</th>
@@ -14721,29 +14940,27 @@ mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.ld_st.bin.csv .
<td>32</td>
<td>16</td>
<td>0.0015</td>
- <td>257115</td>
- <td>1284</td>
- <td>1599</td>
- <td>63343</td>
- <td>316</td>
- <td>459</td>
- <td>2.507812</td>
- <td>0.617188</td>
+ <td>281819</td>
+ <td>1408</td>
+ <td>1627</td>
+ <td>86902</td>
+ <td>434</td>
+ <td>536</td>
+ <td>512</td>
</tr>
<tr>
<th>4</th>
<td>200</td>
<td>32</td>
<td>20</td>
- <td>0.0016</td>
- <td>317115</td>
- <td>1584</td>
- <td>1899</td>
- <td>75343</td>
- <td>376</td>
- <td>519</td>
- <td>2.475000</td>
- <td>0.587500</td>
+ <td>0.0015</td>
+ <td>341819</td>
+ <td>1708</td>
+ <td>1927</td>
+ <td>101902</td>
+ <td>509</td>
+ <td>611</td>
+ <td>640</td>
</tr>
</tbody>
</table>
@@ -14758,12 +14975,111 @@ mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.ld_st.bin.csv .
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
-<div class="prompt input_prompt">In [79]:</div>
+<div class="prompt input_prompt">In [9]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="p">(</span><span class="n">ax1</span><span class="p">,</span> <span class="n">ax2</span><span class="p">)</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">df_ldst</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"Grid Points"</span><span class="p">)[</span><span class="s2">"PM_LD_CMPL (min)"</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">True</span><span class="p">);</span>
+<span class="n">df_ldst</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"Grid Points"</span><span class="p">)[</span><span class="s2">"PM_ST_CMPL (min)"</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax2</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">True</span><span class="p">);</span>
+</pre></div>
+
+ </div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+ <div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img 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v9whszUfD1SvK2JLV3NXLmnuxySYgYtRrCGsxZRcBIRERGRNZKfLXJ4JLsoJB1OZchXDp73+zz0RAz2nBWhr7KL1Bs1aArqR1RZf/RdKSIiIiKnLTM16+4ezR0kOzSWZe5IxKagj0TM5Pl7ut3zkbojYfw+1SNJfVBwEhEREZGqOY7D2OQ0A8Nzh8hmGEilGZuccce0mUESMYOLdkbpixsk4ibRlka1/pa6VlVwsiyrEfgkcA0wDfzQtu23WJa1E7gX6ADGgNfbtv1U5XNW/ZqIiIiInDnFUomhsdyCkJRmIJUhO10AwAPE20Ns72nh6otMEnGDZMykOazD42XjqXbH6aOUA9NO27Ydy7LilcfvAj5n2/aXLMu6CbgbuHoNr4mIiIjIGpjJFzk8kqkcIlveRTo8kmXWrUfy0hsNc7EVc3eRElGDYMBX45mLnBlLBifLsgzg9UCvbdsOgG3bw5ZlxYCLgGsrQ78M3GlZVpTyP0Cs6jXbtkdO652KiIiICACTuTwDz2r9ffRYzq1HCgX9JOMGV13YQzJePh+pqyOEz6t6JNm8qtlx2k75lrk/tyzrKiAD3AFMAYO2bRcBbNsuWpZ1BEhQDkCrfU3BSURERGQZHMdhdGJ6fhepEpTG0/P1SO3NQZIxk31nx9zzkTqaVY8k8mzVBCc/sA143Lbtd1mWdSnwTeB313Rmp6Gjw6jp60ejZk1fX5ZPa1Z/tGb1R2tWf7Rm9aWtPczAcJrfDk7w2yMT/HZwgqcHJ9x6JK8HemImF5wVZWt3C9t7Wtja06J6pBrS77H64nHm9mRPwbKsCDAEBOZu1bMs65fALcCDQEdlZ8hHeWfqLMo7R0+u5rUqb9XbAjw9NpahVHru97VWolGTkZF0TV5bVkZrVn+0ZvVHa1Z/tGbr23S+wEAqU279PZzmyLEch4YmKRTLP/8E/F56YwbJmFHZRTLpiYYJNqgeab3Q77Ha8Xo9cxstW4Fnqv28JXecbNsetSzrPyjXHT1Y6XoXoxxwngBuBL5U+fXxuYBjWdaqXxMRERHZbCay+cr5SGk3KKXGp5j7J2KjqYEdva1cc3GCZKVpQ2d7k+qRRFZZtV31DgJ/a1nWJ4BZ4Gbbto9blnUQuNeyrPcC45SbSCz8nNW+JiIiIrIhlRyHkeNTbjiaO0h2IpN3x0RaGknEDA6c2+nWI7WZQWKxZu1eiKyxJW/VqzNb0K16skxas/qjNas/WrP6ozVbW4ViicGRLP2p+V2kgVSG6XwRAK/HQ3ckRCJmuq2/k3GDcGPDSZ9P61V/tGa1s2a36omIiIjIyk3NlOuR3ANkhzMMjmYpVv6RN9DgLe8inddJX9wkETPojYZp8KseSWQ9UXASERERWQWO43A8k2cgVW79PReSUsen3DFmqIFk3ORF29pJxsq7SPG2EF6vWn+LrHcKTiIiIiLLVHIcUuNTlfOR0u5hspO5WXdMtLWRZNzksvO73O52rUZA5yOJ1CkFJxEREZHnMFsoMjiaXdS0YSCVYWa2XI/k83rojoTZvb3D3UVKxExCjfoxS2Qj0e9oERERkYrc9Ox8QEqVfx0ay7n1SMGAj2TM4PLdXSTj5V2k7kiYBr9af4tsdApOIiIisuk4jsN4euaEkDQ6Me2OaQkHSMZNLtgRcVt/R1ub8OpWO5FNScFJRERENrRSyeHosdyigNQ/nCEzNV+PFG9rYktXM1fu6S6HpJhBixGs4axFZL1RcBIREZENIz9b5PCzzkc6nMqQL5QA8Ps89EQM9pwVcVt/J2IGTUH9SCQiz01/SoiIiEhdykzNurtHc0FpaCyLUy5HoinoIxEzef6ebjckdUfC+H2qRxKR5VNwEhERkXXNcRzGJqcZGJ47RDbDQCrN2OSMO6bNDJKIGVy0M0pf3CARN4m2NKr1t4isGgUnERERWTeKpRJDYzk3JA1UapKy0wUAPEC8PcT2nhauvsgkETdIxkyaw4HaTlxENjwFJxEREamJmXyRwyOZyiGy5V2kwyNZZt16JC+90TAXWzF3FykRNQgGfDWeuYhsRgpOIiIisuYmc3kGntX6++ixnFuPFAr6ScYNrrqwxz0fqasjhM+reiQRWR8UnERERGTVOI7D6MT0/C5SJSiNp+frkdqbgyRjJvvOjrnnI3U0qx5JRNY3BScRERFZkUKxXI/kdrarhKSpmUo9kge6OsJYyVaSMdPdSTKaGmo8cxGR5VNwEhERkSVN5wuVRg3zB8gOjmYoFMv32gX8XnpjBpeeM7eLZNITDRNsUD2SiGwMCk4iIiKyyEQ2T//YMP/1ZMoNSqnxKSrlSBhNDSTjBtdcnCBZadrQ2d6keiQR2dAUnERERDapkuMwcnxq0flI/ak0E5m8OybS0kgiZnDg3E63HqnNDKoeSUQ2HQUnERGRTaBQLDE4kqU/NV+PNJDKMJ0vAuD1eOiKhNjV105f3GC3FaM56CPcqHokERFQcBIREdlwpmbK9UjlXaQ0A8MZBkezFEuVeqQGb3kX6bxO+uImiZhBbzRMg3++HikaNRkZSdfqLYiIrDsKTiIiInXKcRyOZ/IMpBa0/h7OkDo+5Y4xQw0k4yYv2tbudraLt4XwenWrnYjIcig4iYiI1IGS45Aan6qcj5R2D5OdzM26Y6KtjSTjJped30UyVm793WoEVI8kIrIKFJxERETWmdlCkcHR7KLW3wOpDDOz5Xokn9dDdyTM7u0d7i5SImYSatRf6yIia0V/woqIiNRQbnp20eGx/cNphsZybj1SMOAjGTO4fHeXe4BsdyRMg1+tv0VEziQFJxERkTPAcRzG0zNuy++5sDQ6Me2OaQkHSMZNLtgRcVt/R1ub8OpWOxGRmlNwEhERWWWlksPRY7lFu0j9wxkyU/P1SPG2JrZ0NXPlnu5ySIoZtBjBGs5aRESey7KCk2VZfw68D9ht2/YvLMvaD9wNNAHPADfZtp2qjF31ayIiIutNfrbI4Wedj3Q4lSFfKAHg93noiRjsOSvitv5OxAyagvq3SxGRelL1n9qWZV0E7Af6Kx97gC8Bt9i2/YhlWXcAHwZuXYtrq/WGRUREViozNevuHs0FpaGxLE65HImmoI9EzOT5e7rdkNQdCeP3qR5JRKTeVRWcLMsKAp8Dfh/4j8rDe4Fp27YfqXx8F+UdolvX6JqIiMgZ4TgOY5PTDAzPHSKbYSCVZmxyxh3TZgZJxAwu2hmlL26QiJtEWxrV+ltEZIOqdsfpL4Ev2bb9tGVZc48lgUNzH9i2PWpZlteyrPa1uGbb9rEVvkcREZFTKpZKDI3l3JA0UKlJyk4XAPAA8fYQ23tauPoik0TcIBkzaQ4HajtxERE5o5YMTpZlHQD2AX+y9tNZHR0dRk1fPxo1a/r6snxas/qjNas/62HNpmcKPHN0kt8OTrj/HRqadOuRGvxe+rqauXxPD1u7W9je08KWrmYaN2k90npYM6me1qv+aM3qSzV/E1wJnA3M7Tb1Av8GfAbomxtkWVYEcGzbPmZZVv9qX1vOmxoby1CqnH9xpkWjJiMj6Zq8tqyM1qz+aM3qTy3WLJ3Ln3A+0tFjObceKRT0k4wbvODCHvd8pK6OED7v4nqk9OQUm/G7Tb/P6ovWq/5ozWrH6/WsaKNlyeBk2/aHKTdoAMCyrGeAlwO/BN5iWdbllZqkg8BXKsN+CjSt8jUREZETOI7D6MQ0/cNpDg1nGKgEpfH0fD1Se3OQZMxk39kx93ykjmbVI4mISPVWfO+Bbdsly7JuBu62LKuRSuvwtbomIiJSKJbrkdzOdpWQNDVTqUfyQFdHGCvZSjJWDkiJmIEZUj2SiIicHo/j1OaWtjWyBXhat+rJcmjN6o/WrP6sZM2m84VKo4b5A2QHRzMUiuU/3wN+L70xg2TMqOwimfREwwQbfGvxFjYd/T6rL1qv+qM1q50Ft+ptpbxRU5XNWe0qIiLrykQ2XwlHc2ckZUgdyzH3T2BGUwPJuME1FyfKu0hxk872phPqkURERNaKgpOIiJwxJcdh5PjUovOR+lNpJjJ5d0ykpZFEzODArrhbj9RmBlWPJCIiNaXgJCIia6JQLDE4kqU/lWZkcoYnnzlGfyrDdL4IgNfjoSsSYldfu3uAbDJuEG5sqPHMRURETqTgJCIip21qplyPdKhyu93AcIbB0SzFSr1pMOCjNxrmwHmd9MVNEjGD3miYBr/qkUREpD4oOImISNUcx+F4Js9AakHr7+EMqeNT7hgz1EAybvKibe1uZ7tzd8Y5Npap4cxFREROj4KTiIicVMlxSI1PVc5HKu8i9Q+nmczNumOirY0k4yaX7e50O9u1GoET6pF8XtUniYhIfVNwEhERZgtFBkezi1p/D6QyzMyW65F8Xg/dkTC7t3csOB/JJNSov0ZERGRz0N94IiKbTG56dtHhsf3DaYbGcovqkZIxg8t3d5GMl89I6o6EafCr9beIiGxeCk4iIhuU4ziMp2fclt9zYWl0Ytod0xIOkIybXLAjUr7VLmYQbWvCq9bfIiIiiyg4iYhsAKWSw9FjuUW7SP3DGTJT8/VI8bYmtnQ1c+WebjcktRjBGs5aRESkfig4iYjUmfxskcOV85HmdpEOpzLkCyUA/D4PPRGDPWdF3NbfiZhBU1B/5IuIiKyU/hYVEVnHMlOz7u5Rf6rc2W5oLEfJKdcjNQV9JGImz9/T7Yak7kgYv0/1SCIiIqtJwUlEZB1wHIexyWkGhucOkc0wkEozNjnjjmkzgyRiBhfujNIXN0jETaItjSe0/hYREZHVp+AkInKGFUsljo7l6K+EpIFKTVJ2ugCAB4i3h9je08LVF5kk4gbJmElzOFDbiYuIiGxiCk4iImtoJl/k8Mji1t+HR7LMuvVIXnqjYS62Ym7r70TUIBjw1XjmIiIispCCk4jIKknn8iecj3T0WI5KORKhoJ9k3OCqC3vckNTVEcLnVT2SiIjIeqfgJCKyTI7jMDoxPd+0oRKUxtPz9UjtzUGSMZN9Z8fKrb/jBh3NqkcSERGpVwpOIiLPoVAsMTSWOyEkTc1U6pE80NURxkq2koyVA1IiZmCGVI8kIiKykSg4iYhUTOcLlUYN8wfIDo5mKBTL99oF/F56YwaXnjO3i2TSEw0TbFA9koiIyEan4CQim9JENl8JR3NnJGVIHctRKUci3OgnGTe55uJEeRcpbtLZ3qR6JBERkU1KwUlENrSS4zByfGrR+Uj9qTQTmbw7JtLSSCJmcGBX3K1HajODqkcSERERl4KTiGwYhWKJwZEs/anKAbKVeqTpfBEAr8dDVyTErr529wDZZNwg3NhQ45mLiIjIeqfgJCJ1aWqmXI90qHK73cBwhsHRLMVSpR6pwVveRTqvk2Ss3Pq7Nxqmwa96JBEREVk+BScRWdccx+F4Js9AKs2hyi7S4GiOobGsO8YMNZCMm7xoW7vb2S7eFsLr1a12IiIisjoUnERk3Sg5DqnxKfqH0xyq7CL1D6eZzM26Y6KtjZyVbGP/rvnOdq1GQPVIIiIisqYUnESkJmYLRQZHs4tafw+kMszMluuRfF4P3ZEwu7d3LDgfySTU6CcaNRkZSdf4HYiIiMhmsmRwsiyrA7gP2A7MAP8N/IFt2yOWZe0H7gaagGeAm2zbTlU+b9WviUh9yk3PLjo8tn84zdBYzq1HCgZ8JGMGl+/uIhkv1yN1R8I0+NX6W0RERNaHanacHOCjtm1/F8CyrI8BH7Ys603Al4BbbNt+xLKsO4APA7daluVZ7Wur+aZFZG04jsN4esZt+T0XlkYnpt0xLeEAybjJBTsi5VvtYgbRtia8utVORERE1rElg5Nt28eA7y546D+BtwJ7gWnbth+pPH4X5R2iW9fomoisI6WSw9FjuUUBqX84Q2Zqvh4p3tbElq5mrtzT7YakFiNYw1mLiIiIrMyyapwsy/JSDk3fAJLAoblrtm2PWpbltSyrfS2uVQKciNRAfrbI4QXnI/UPpzmcypAvlADw+zz0RAz2nBWhL26SiBkkYgZNQZVRioiIyMaw3J9qPgtkgDuBG1Z/Oqujo8Oo6etHo2ZNX1+WT2s2L53L89vBifn/jkxwOJWhVKlHCjX62drdwksObGFbTwvbelrojZlnvB5Ja1Z/tGb1R2tWX7Re9UdrVl+qDk6WZX0cOAtpU9euAAAgAElEQVS43rbtkmVZ/UDfgusRwLFt+9haXFvOmxobm/8h70xTt6/6s1nXzHEcxianGRguHyI7UGnaMDY5445pM4MkYgbn7+8rHyLbaRJtaTyh9ffx8eyzn35NbdY1q2das/qjNasvWq/6ozWrHa/Xs6KNlqqCk2VZHwQuBl5m2/bcT1U/BZosy7q8UpN0EPjKGl4TkRUqlkocHcvR/6yQlJ0uAOAB4u0htve0cPVFJom4QTJm0hwO1HbiIiIiIutENe3IzwXeAzwJ/MCyLICnbdu+wbKsm4G7LctqpNI6HKCyI7Wq10SkOjP5IodHFrf+PjySZdatR/LSGw1zsRVzW38nogbBgK/GMxcRERFZvzyOU5tb2tbIFuBp3aony1HPa5bO5U84H+nosRxzv61DQb8bjuZ+7WwP4ffV9/lI9bxmm5XWrP5ozeqL1qv+aM1qZ8Gtelspb9RURS2vROqA4ziMTky7Lb/ngtJ4er4eqb05SDJmsu/smBuUOppPrEcSERERkeVTcBJZZwrFEkNjuRNC0tRMpR7JA10dYaxkK8lYOSAlYgZmSPVIIiIiImtFwUmkhqbzhUqjhvkDZAdHMxSK5XvtAn4vvTGDS8+Z20Uy6YmGCTaoHklERETkTFJwEjlDJrL5Sjiq7CSlMqSO5Zirxgs3+knGTa65OFHeRYqbdLY34fPWdz2SiIiIyEag4CSyykqOw8jxKfd8pHJISjORybtjIi2NJGIGB3bF3XqkNjOoeiQRERGRdUrBSeQ0FIolBkey9KfKAWmgUo80nS8C4PV46IqE2NXXTl9lFykZNwg3NtR45iIiIiKyHApOIlWaminXIx2q3G43MJxhcDRLsdL6PtDgLe8inddJMlZu/d0bDdPgVz2SiIiISL1TcBJ5Fsdx3HqkQ3O7SMMZUsen3DFmqIFk3ORF29rdznbxthBer261ExEREdmIFJxkUys5DoMjGZ741TCHKrtI/cNpJnOz7phoayPJuMlluzvdznatRkD1SCIiIiKbiIKTbBqzhRKDo4tbfw+kMszMluuRfF4P3ZEwu7d3LDgfySTUqN8mIiIiIpudfiKUDSk3Peu2/J5rAT40lnPrkYIBH8mYweW7uzh3R4S2UAPdkTANfrX+FhEREZETKThJXXMch/H0jNvye243aXRi2h3TEg6QjJtcsCNSvtUuZhBta8JbudUuGjUZGUnX6i2IiIiISB1QcJK6USo5HD2WWxSQ+oczZKbm65HibU1s6Wrmyj3dbkhqMYI1nLWIiIiIbAQKTrIu5WeLHF5wPlL/cJrDqQz5QgkAv89DT8Rgz1kR+uImiZhBImbQFNS3tIiIiIisPv2UKTWXmZploNL6uz9V7mw3NJaj5JTrkZqCPhIxk+fv6XZDUnckjN+neiQREREROTMUnOSMcRyHsclpBobLh8gOVBo3jE3OuGPazCCJmMGFO6PlQ2Q7TaItjWr9LSIiIiI1peAka6JYKnF0LEf/s0JSdroAgAeIt4fY3tPC1ReZJOIGyZhJczhQ24mLiIiIiJyEgpOctpnZIofn2n5Xfj08kmXWrUfy0hsNc7EVIxk3SMZNElGDYMBX45mLiIiIiFRHwUmWJZ3Lz3e0q4Sko8dyVMqRCAX9JOMGV13Y44akzvaQ6pFEREREpK4pOMlJOY7D6MS02/J7LiiNp+frkdqbgyRjJvvOjpVbf8cNOppVjyQiIiIiG4+Ck1Aolhgay50QkqZmKvVIHujqCGMlWt2AlIgZmCHVI4mIiIjI5qDgtMlM5wuVRg3zB8gOjmYoFMv32gX8XnpjBpeeU95FSsQNeqMGwQbVI4mIiIjI5qXgtIFNZPOVcFTZSUplSB3LUSlHItzoJxk3uebiRHkXKW7S2d6Ez6t6JBERERGRhRScNoCS4zByfMo9H6m/cpDsRCbvjulobiQZNziwK04ibtAXN2kzg6pHEhERERGpgoJTnSkUSxwZzboBaaBSjzSdLwLg9XjoioTY1ddOX2UXKRk3CDc21HjmIiIiIiL1S8FpHZuaKdcjHarcbjcwnGFwNEuxVKlHavCSiBkcOK+TZKzc+rs3GqbBr3okEREREZHVpOC0DjiO49YjHZrbRRrOkDo+5Y4xQw0k4yYv2tZOMlbeRYq3hfB6daudiIiIiMhaW5fBybKsncC9QAcwBrzetu2najur1VFyHFLjU5WQVN5F6h9OM5mbdcdEWxtJxk0u291Zaf9t0moEVI8kIiIiIlIj6zI4AXcBn7Nt+0uWZd0E3A1cXeM5LYtTadiQOj7FyPgUg6PZck1SKsPMbLkeyef10B0Js3t7h7uLlIiZhBrX67KIiIiIiGxO6+4ndMuyYsBFwLWVh74M3GlZVtS27ZHazWx5fvCLo9zz7V+5HwcDPpIxg8t3d5GMl+uRuiNhGvxq/S0iIiIist6tu+AEJIBB27aLALZtFy3LOlJ5vG6C0/nbO3jLK3bRbjYSbW2ixQjg1a12IiIiIiJ1aT0Gp9PW0WHU9PWjUZMosK2vo6bzkOpFo2atpyDLpDWrP1qz+qM1qy9ar/qjNasv6zE4DQA9lmX5KrtNPqC78nhVxsYylCotu8+0aNRkZCRdk9eWldGa1R+tWf3RmtUfrVl90XrVH61Z7Xi9nhVttKy7AhvbtlPAE8CNlYduBB6vp/omERERERHZWNbjjhPAQeBey7LeC4wDr6/xfEREREREZBNbl8HJtu1fA5eu4FN9QM0Pha3168vyac3qj9as/mjN6o/WrL5oveqP1qw2Fnzdfcv5PI/j1KYWaI1cDjxc60mIiIiIiMi6dwXwSLWDN1pwCgL7gCGgWOO5iIiIiIjI+uMDuoCfADPVftJGC04iIiIiIiKrbt111RMREREREVlvFJxERERERESWoOAkIiIiIiKyBAUnERERERGRJSg4iYiIiIiILEHBSUREREREZAkKTiIiIiIiIktQcBIREREREVmCgpOIiIiIiMgSFJxERERERESWoOAkIiIiIiKyBAUnERERERGRJSg4iYiIiIiILEHBSUREREREZAkKTiIiIiIiIktQcBIREREREVmCgpOIiIiIiMgSFJxERERERESWoOAkIiIiIiKyBAUnERERERGRJSg4iYiIiIiILEHBSUREREREZAkKTiIiIiIiIktQcBIREREREVmCgpOIiIiIiMgS/EsNsCyrA7gP2A7MAP8N/IFt2yOWZe0H7gaagGeAm2zbTlU+b9WviYiIiIiI1ILHcZznHGBZVjtwvm3b3618/DGgHXgT8BRwi23bj1iWdQewzbbtWy3L8qz2tSrfTxDYBwwBxWV9JUREREREZDPwAV3ATyhvDFVlyR0n27aPAd9d8NB/Am8F9gLTtm0/Unn8Lso7RLeu0bVq7AMernKsiIiIiIhsXlcAjyw5qmLJ4LSQZVleyqHpG0ASODR3zbbtUcuyvJUdqlW/VglwSxkCGB/PUio9907aWunoMBgby9TktWVltGb1R2tWf7Rm9UdrVl+0XvVHa1Y7Xq+HtrYwVLJDtZYVnIDPAhngTuCGZX7umVAE5r4QNdPRYdT09WX5tGb1R2tWf7Rm9UdrVl+0XvVHa1ZzyyrtqTo4WZb1ceAs4HrbtkuWZfUDfQuuRwDHtu1ja3FtOW9qbCxTsx2naNRkZCRdk9eWldGa1R+tWf3RmtUfrVl90XrVH61Z7Xi9nhWF1qrakVuW9UHgYuBVtm3PFVD9FGiyLOvyyscHga+s4TUREREREZGaqKYd+bnAe4AngR9YlgXwtG3bN1iWdTNwt2VZjVRahwNUdqRW9ZqIiIiIiEitLNmOvM5sAZ5+9q16xWKB8fERCoX8mk/A6/VSKpXW/HVk9ZxqzbxeH01NBobRgsfjqcHM5FR0e0P90ZrVH61ZfdF61Z+NvGa56VkGUhn6hzP0D6fpT2XIF0q8/7ZL8PuquuFtTS24VW8r5Y2aqiy3OURdGh8fobExRDjcueY/APv9XgoFBad6crI1cxyHYrFAOn2c8fER2ttjNZqdiIiIyPrkOA7j6ZlyQEql3aA0OjHtjmkJB0jEDXb1teP11vc/RG+K4FQo5M9IaJKNw+Px4Pc30NrawfDw4VpPR0RERKSmSiWHoWM5BobTi4JSZmoWAA8Qaw+xtauZK/d0k4ybJGMGLUawthNfRZsiOAEKTbIiHo8X2FC3s4qIiIg8p5nZIodTGfpTGQaG0xwazjA4Ur7dDsDv89ATNbhoZ4REzKQvbtITDdMU3NjRYmO/OxEREREROaXJXJ6BBbVI/cNpjh7LMdcGIRT0k4wbvODCHpJxg2TMpLMjtC5qlc40BacaeM1rricQCNDQEKBUKvKGN9zGNde8mMcee5Q//MOD3HjjzbztbX/kjr/99rfwxBOP8eCD3yMUCp30OWdnZ/n0pz/BE088hs9XbnZw88230tLSwhe+8FkAjh0bo1QqEYlEAXjjG9/MlVdeddLny+Wy3H335/jP//wBgUAAgGuvfQmvf/2t3H//N/nQh/6Cd7zj3bz61a8Fyve4vva1ryKXy/Dtbz+05Pv83Oc+zT333Lfk1+orX/kyhUKB3//9m6v86pb9zd/cxdat23jhC1/0nOO+9rWvMjWV5XWvu2VZzy8iIiJSTxzHYWRimv6j8wFpIJVhPD3jjuloDpKMm+w7O0Zf3CQRN+hobtSdWxUKTjXygQ98hG3bdvDkk7/m4MHb2Lv3UgCSyT4efvi7HDx4Oz6fjyNHBpmZmV7i2eCrX/0yk5MT3Hvvl/H5fORyOcbGRkkkklx66QEA7rnnbqamprj99rc/53M5jsO73vV2tm/fwZe+9FUaGhqYmZnmm9/8F3fMzp0W//qv33aD0+OP/5Tm5mZyuUxV77Ma09PT/PM//yP33fdPVX/OnDe96WBV417xiht43etew6te9RrCYZ3eLSIiIvWvUCxxZDTLoeF0eTcplWEglWZqpgiA1+OhKxLi7GSrW4uUiJsYTQ01nvn6timD0/f/a4hHfj60Js995YXd7N/VWfX4nTvPJhQKMTQ0CEBTU4gtW7by4x//kAMHLueBB77FS17yMn71q18+5/OkUina2zvw+XwAhEIhQqHkit7Do4/+mKGhI3z601/A7y9/iwSDjbzmNb/njunu7uH48eM8/fRv2bp1G/ff/02uu+7l/O3f/lVV77Ma3/3uQ+zZcxHBYCMA99//Tb7znX/FMEx+85uniEZjvP3t7+Lzn/80AwMDnHPOLt773vfj8Xj44Affx9lnn8OrX/3/cM89d9Pff4hsNsORI4P09PTy/vd/hMbGRvx+P5dcsp+HHvoOr3jFDSv6eomIiIjUSm66wEBqwS7ScIbB0SzFytE8gQYviZjB/nM7ScYMknGTnkiYQIOvxjOvP5syOK0njz32KPl8nt7eJE89ZQNw3XXX8/Wvf439+y/joYce5AtfuIdPfvJjz/k811//Kt75ztt57LGfsHv3BVx66fN4/vNfsKI5Pfnkr7Gss93QdCovecnLeOCBb3HLLW/iv/7rZ9xyy5tOGZxO9j6X8vjjP2XXrnMXPfarX/2SL37xH4nF4rz73W/nL/7iDu68869obGzktttu4tFHf8y+fSfuatn2r/jrv/4ihmHwznfezoMPPuAGpd27z+f7339YwUlERETWLcdxOJ7Jl2uRFtQjjRyfvzOpOdRAMm5y7rb28q12MYN4W6ju24CvF5syOF22u4vLdnetyXNXe47THXf8MYFAkHA4zAc/+BFM03SvXXTRXj7xiQ/zve99l23bttPS0rrk823fvoOvfOXr/Oxnj/Pznz/Bpz71MX70ox/wrne9Z9nvodpDka+++lpuvfUmEokkV1zxAne3a6Hnep9LGRlJ8bznXbHosfPPv4BYLA7AWWdZdHZ2YRjlW+x27DiLwcGBkwanSy7Z7772rl3nMTg432K8o6ODVCpV9bxERERE1lKp5DA8npu/1a4SlNK5WXdMrK2Jvs5mrji/0vo7btC6gVp/r0ebMjitB3O1Pyfj8Xi4+upr+ehHP8B73vO+qp8zGAxyySX7ueSS/Rw4cDnveMfbVhScLOscvva1r1IoFJ5z1ykUCnHuuedx112f5bOfvfukY57rfS4lGAySz88semyuUQWA1+slEAgu+NhHsVg86XMtHuddNG5mJk8wqD9oRERE5MybmS0yOJJdtIt0OPWs1t8Rgwt2RNxdpETM2PCtv9cjfcXXqVe+8ndoampyGzss5Wc/e5xEIkl7ewdQvt2uq6t7Ra+9d+8lxONx7rzzU7ztbX9UaQ4xw1e/+mVuuumWRWNvuukWdu06j23bdjA0dGRFr3cq27btoL//0Ko+58k888zT7Nixc81fR0RERDa3zNSsu4s0fHyapwbGGRrLuq2/m4J+kjGDK/dUWn/HTbo2aevv9UjBaZ2KRmO87nVvqHr80NARPvWpjzE7W8Dn89La2s573/v+Fb22x+Ph4x//DHfd9Tle97rX0NhYbs5w7bUvPWHs1q3b2Lp127Jf4ze/eYobbrjO/Xjv3kv40z9936IxV155FZ/4xEe47bY/WPbzL8ePfvQD3vzm/7GmryEiIiKbh+M4jE5MV+qRMgykMhwaTi9q/R1pbaI3EmavFa0cImvQ0aLW3+uZp9p6ljqxBXh6bCxDqTT/vo4ePURnZ98ZmUC1NU5SnXe+83YOHrydnTvPXpPnP3ToGT7+8Q/x2c+evKkFnNnvH6lONGoyMpKu9TRkGbRm9UdrVl+0XrUz1/p7LhzNtf+emikA4PFAV0fYPTw2GS/faretr0NrViNer4eODgNgK/BMtZ+nHSdZ197xjndz+HD/mj1/KnWUd797+XVgIiIisvlMzRQYSGUW1SMdGc1SKC5o/R012L8rTqISlHqjav29USg41Znbbrv5hAYI55573oqaQHzsYx/i//7fXyx6zOfzcc89953WHFdTIpEkkVjZeVTV2Ldvv3YJRUREZJG51t8DqfKtdnNBKTU+5Y4xK62/r93b7na1U+vvjU3Bqc6sZqhZSdgSERER2UjmWn/3D2forwSlgeE0kwtbf7c2kYgbXLa7yz1EttUIqB5pk9k0wclxHH1zy7I5TgnQ942IiMhGkJ8tMjiadZs29KfSHE5lmZkt383j83roiYQ5f3uERNygL27SGzUINW6aH5nlOWyK7wK/P0A2O0k43KzwJFVxHIdisUA6PU4g0Fjr6YiIiMgyZaZmGRhOc2g4495yNzSWo1RpjNYU9JGImVxxQZfbtKE7ElbrbzmlTRGc2tqijI+PkMkcX/PX8nq9lEqql6knp1ozr9dHU5OBYbTUYFYiIiJSDcdxGJuYdps19FeC0tjkfOvvNjNIMmZw4c4ofXGDRNwkqtbfskybIjj5fH4ika4z8lpqB1p/tGYiIiL1oVAscXQsV277vSAo5Ra0/u5sD7Gjt5WrK13tEnGD5lCgxjOXjWBTBCcRERERqS9TMwUOj2QWdbUbHMlSKJbvEgn4vfTGDC45J0YyXg5IvVGDoFp/yxpRcBIRERGRmprIzLi1SIcqXe1S41M4letGUwPJuME1e3vdrnbx9iZ8XtUjyZmj4CQiIiIiZ0TJcUiNTy3qatc/nGEym3fHRFsbScZMDpzXWT4fKWbQZgZVjyQ1p+AkIiIiIqtutjDX+nth04bMotbf3ZEwu7e1u13tEjGDUGNDjWcucnIKTiIiIiJyWrLTs4sCUn8qzdDofOvvxoCPZMzg8vO7SFaaNnRHwjT4daud1A8FJxERERGpiuM4HJuccZs1zAWlsclpd0yrESAZN7nwrIi7kxRpbcKrW+2kzlUVnCzL+jjwamALsNu27V9UHn8GmK78B/DHtm3/W+XafuBuoAl4BrjJtu3U6VwTERERkTOjWCoxNJZjYDizqP13drrS+hvo7AixvaeZqy/qIVHZSWoOq/W3bEzV7jj9C/Bp4OGTXHvNXJCaY1mWB/gScItt249YlnUH8GHg1pVeW8mbExEREZGlTecLHE5l3WYN/cNpDi9o/d3g99IbDbP37Jjb1a43ahAMqPW3bB5VBSfbth8BsCyr2ufdC0zPfR5wF+Xdo1tP45qIiIiInKaJbJ6B4bS7i3RoOEPqWM5t/R1u9JOMm7zw4h63q11nR0itv2XTW40ap7+v7BQ9ArzHtu3jQBI4NDfAtu1Ry7K8lmW1r/SabdvHVmGuIiIiIptCyXEYOT51QtOGicx86+9ISyPJuMmBXfFySIqr9bfIqZxucLrCtu0By7KCwKeAO4GbTn9ap6ejw6jp60ejZk1fX5ZPa1Z/tGb1R2tWf7Rm9WO2UGRipshvByd4enCC3wxO8MzQBFMz862/E3GTi8+Os62nhW3dLWztacFoUuvvWtLvsfpyWsHJtu2Byq8zlmV9HvhG5VI/0Dc3zrKsCODYtn3MsqwVXVvOvMbGMpRKztID10A0ajIykq7Ja8vKaM3qj9as/mjN6o/WbP3KzbX+XtDVbmgsS7Hys08w4CMRM3jeuV0k4gZ9cZPuSIgG/+J6pKnMNFOZ6ZO9hJwB+j1WO16vZ0UbLSsOTpZlhQG/bdsTlVv1fg94onL5p0CTZVmXV+qVDgJfOc1rIiIiIpuG4ziMp2fmb7WrBKXRifmw02IESMZMDpzfRcQMkowbRNX6W2RNVNuO/DPA7wCdwP+xLGsMuB74fy3L8gE+4JfA/wCwbbtkWdbNwN2WZTVSaSt+OtdERERENqpiqcTRsdyiXaSBVIbM1CxQbv0daw+xrbuZK/d00xc3ScRNWiqtv7V7IbL2PI5Tm1va1sgW4GndqifLoTWrP1qz+qM1qz9as7Uzky9yeGTxLtLhkSyzhXLrb7+v3Po7GTcqXe1MemNhGgOn/vdurVf90ZrVzoJb9bZS3qipymp01RMRERGRk5jM5ekfTi86RPbosRxz/2491/r7qgt73KDU2R7C71Prb5H1RsFJRERE5DSVHIfRudbfCw6RPb6g9XdHcyPJuMEl58TdQ2Tbm9X6W6ReKDiJiIiILEOhWGJwJOsGpIHhNAMjGbf1t9fjoTsS4py+dncXKREz1PpbpM4pOImIiIicQm66wEAqvWgn6cjogtbfDeXW3wfO7XQPkO2JhE9o/S0i9U/BSURERDY9t/V3pVnDXE3SwtbfzeEAybjB7m0d7k5SrE2tv0U2CwUnERER2VRKJYejx3KLutr1D8+3/gaItzWxpavc+jsRM+mLG7QYwRrOWkRqTcFJRERENqyZ2XLr74EFh8geTmXIu62/PfREDS48K+LeatcbNWgK6kckEVlMfyqIiIjIhpDO5d1apLlb7Ra2/g4F/STjBi+4sIdEzKAvbtLZodbfIlIdBScRERGpK47jMDIxzcBwmkOVrnb9qQzj6Rl3THtzkGTMZN/ZscohsgYdLY1q/S0iK6bgJCIiIutWoVjiyGh2UVe7gVR6Uevvro4QZydbScRMt2mDWn+LyGpTcBIREZF1YWqmwMCCZg39qTRHRrMUiuV77QINXhIxg/27Ot2A1BMJE2hQ628RWXsKTiIiInJGOY7D8Ux+UVe7geEMqeNT7hgz1EAybnLtvnaSlZ2keFsIr1e32olIbSg4iYiIyJoplRyGx3PlHaQFQSmdm2/9HWtrIhk3uPz8LncnqSUcUD2SiKwrCk4iIiKyKvKzRQ6PZOdrkYbTDIxkyM+WW3/7vB56omEu2BEhGSsHpERMrb9FpD7oTyoRERFZtszU7KJapP7hDENjWbf1d1PQTzJm8PwLuumrBKTuSFitv0Wkbik4iYiIyCk5jsPYxHS57XdqPigdm5xv/d1mBumLm1y8M+reahdR628R2WAUnERERAQot/4eGsvRP5xmJP0MTz5zjP7hDLmZAgAeD3R1hNnZ21q+zS5ukIwZmKFAjWcuIrL2FJxEREQ2obnW3wOpDIcqXe0GRzMLWn/76I2GuWRX3K1H6omGCar1t4hsUgpOIiIiG9zxzMwJXe1S4/Otv42mBvriBtfuTVR2kUzOs+IcG8vUcNYiIuuLgpOIiMgGUXIcUuNT800bKkFpMpt3x0RbG0nGTS47r5Nk3CQZN2k1Tmz97dN5SSIiiyg4iYiI1KHZQqX1dyUcDQyXb7ubmS0CldbfkTC7t7WXA1LMIBEzCTXqr34RkZXQn54iIiLrXGZqloEFt9n1pzIMjeYoVXp/NwV9JGImV5zfVdlFUutvEZHVpuAkIiKyTjiOw9jktHub3UAlKI09q/V3ImZw4VnRctOGznLrb69af4uIrCkFJxERkRooFEscHcu5h8fOBaXsdKX1N9DZEWJHbytXV7raJWIGzWG1/hYRqQUFJxERkTU2nS9wOJUtt/1OpTk0nGFwJEuhWAKgwe+lN2qw9+yYW4/UGzUIBtT6W0RkvVBwEhERWUUT2Xylq11lJymVIXUsh1O5bjQ1kIwbXHNxL8m4QSJu0tnehM+reiQRkfVsyeBkWdbHgVcDW4Ddtm3/ovL4TuBeoAMYA15v2/ZTa3VNRERkPSk5DiPjU5VdpPlDZCcWtP6OtJRbfx84N04yVm7a0GYGT2j9LSIi6181O07/AnwaePhZj98FfM627S9ZlnUTcDdw9RpeExERqYnZQonB0cyis5EGUhlm8vOtv7sjYc7b2u52tUvEDEKNDTWeuYiIrJYlg5Nt248AWJblPmZZVgy4CLi28tCXgTsty4pSrmdd1Wu2bY+s9A2KiIgsR3Z6loEFAal/OM3QWI5iqXyzXWPARyJmcPnurnJXu7hJdyRMg1+32omIbGQrrXFKAIO2bRcBbNsuWpZ1pPK4Zw2uKTiJiMiqchyHY5Mz9KfKt9jN3XI3OjHtjmkxAvTFTS7YEaEvbpKIG0Rbm9T6W0RkE9qQzSE6Ooyavn40atb09WX5tGb1R2tWf2q5ZsViicMjGX47OOH+9/SRCdK5WQA8HuiOGJyztYNtPb/crd4AABgHSURBVC1s627h/2/v3mMjvc77jn95X5Izu9wlOcNdLofW9ci6rLSyZEuyFCWChTRBDbexa1u1rDhBgMjNBW3i1kDgpBfAreG4SOLYroSkSZQ4MSDAqB0UqF0UqeuqToHUlWK7aY/t2FpybxySe+PwtiRn+se8HA5Xu0suOdyZIb8fYLHknHdmzu6jV8Nnz3l/7y3D+zmY3le3OTcCz7PmYr2ajzVrLlttnMaB4RBCW7Iy1AYcSR5v2YGxGzI9XaBYLG184A4YHEwzOTlTl/fW1liz5mPNms/NrNni5RXGJwuMT5Rjv8fzM5ycnGVpuTr6u5fjdwwymqTajVwl+nt5YYnJhaWbMudG5HnWXKxX87Fm9dPa2rKlhZYtNU4xxnwI4TXgGeDzye+vrl6LtBNjkiRd6dJq9HdyLdLYRIGJqujv3n3t5LJpnnpwuJJqN9TfY/S3JOmGbSaO/NPATwFDwH8JIUzHGO8BngdeCiH8BnAeeK7qaTsxJknao4qlEpMX5tddi3RiYoaLhbXo7/79+8hlU7zt7iy5bIpcJs2h/UZ/S5Jqo6VUqs+Wth3yJuCHbtXTjbBmzceaNZ8bqdnScpHTU7PrVpLG8wUWkujv1pYWjgz0lGO/k1S7kWyKXqO/a8rzrLlYr+ZjzeqnaqveLcDrm33ergyHkCQ1h7mFpWT1qHxN0li+wOmp2Ur0d1cS/f3YvUOV+yMND/TS0d62wStLklRbNk6SpB1Xjv5eKN9ANj9TuZHsuujv3k5y2TTHbutnJJNiNJtm8KDR35KkxmDjJEmqqWKxxJlzc+UVpKRROjk5y6XZ8vVILUDmUA+3HN7Pkw8cqWy5O5Dqqu/EJUm6DhsnSdKWLS6tcHKywNjEWvz3qckCl5Po7/a2VoYHe3nk3sMM7u9iNJtmeLCX7i4/fiRJzcVPLknSplyau8x4ssVuNbTh7Lk5VjOGerrayWVT/Ojx4Uqq3VB/D+1trV4ELUlqejZOkqR1SqUSkxcXGDu7PtXu/Mxi5Zj+/V3ksmkevivDaJJq179/n9HfkqRdy8ZJkvaw5ZVy9PeJiZnyalK+wHh+hvnFtejvwwM93JXrq1yLNJJNk+o2+luStLfYOEnSHjG3sMx4vmoVaaLAqero745y9Pcj9wxV7o80PNBLZ4fR35Ik2ThJ0i5TKpW4ULhcvhap6nqkyQtr0d/7ezrIZdPce2t/+XqkbJpMXzetrW61kyTpamycJKmJFYslJs7PrW21SxqlmbmlyjGZg92MDu3niWNHKjeR7TP6W5KkG2LjJElNYnFphVOTs+tWkU7mq6O/WxgeSHH/7QPlwIZMipFMyuhvSZJqwE9TSWpAhfmlN6winZmerUR/d3e1M5pN8eQDw5WtdoeT6G9JklR7Nk6SVEelUompiwuMTazFfp+YmFkX/X1ofxe5TJqHwiAjmTSj2RT9B4z+liTpZrJxkqSbZDX6e7U5Wo3/nl9cBqClBQ739xJyfeQy5WuRRjIp0j2ddZ65JEmycZKkHTC/uMx4vrDueqTTU7Msr5T32nV2tDIymOKRu7OMZFPkMmmODhr9LUlSo7JxkqRtWI3+Hs/PVLbbjeUL5M/PV45JJ9HfTz90qJJqlz3YY/S3JElNxMZJkjZpNfp7bKLAWNIojU/McKk6+ruvm1w2xdvvO1y5iWxfqtPrkSRJanI2TpJ0FZeXVjg1lUR/J43Syfwsi0srALS1tjA82Mux2wYqqXZGf0uStHv5CS9pzyvMLzE+McOJiUJly92Z6TmKSfZ3d1cbI5k0T9x/uBLacGSg1+hvSZL2EBsnSXtGqVRi+uJCJaxhLGmUpi+tRX8fTHeRy6Q4fucgo9kUI9k0g0Z/S5K059k4SdqVlleKnJ2eK8d+VzVKc1XR30OHerjjaB9PVW2122/0tyRJugobJ0lNb+HyavT3WqrdqclZlleKAHS2t3I0k+Ktb86UG6RsiqODKbqM/pYkSZtk4ySpqVwsLDI2Pce3v5uvNEr58/OUkvFUdwe5bIp3PHS0kmqXPdRNW6vXI0mSpK2zcZLUkIqlEvnz8+tS7cYmClyavVw5ZrBvH7lMmsfuHWIkmyaXSXEw3eX1SJIkqeZsnCTV3dLyavR3dWhDYV3095GBXu679RC5TJpjIUO6s42eff4vTJIk3Rz+1CHppppdWFrXII3lZzgztRb9va+zjVwmxePHDpfvj5RJc2Sgl472ta12g4NpJidn6vVHkCRJe5CNk6QdUSqVOHdpsRLWsNooTV9aqBzTl+okl01z/I6Byv2RBvq6aXWrnSRJajDbbpxCCK8DC8kvgI/GGL8aQngEeBHoBl4Hno0x5pPnbGlMUmNaKRY5Mz3H+ERhXfz37EIS/Q0M9fdw2/B+nnpwmJFkJWl/r9HfkiSpOdRqxek9McbvrH4TQmgBPg98KMb4SgjhY8AngJ/d6liN5ilpmxYuL3MyP1sJaxibmOFkVfR3R3srRwd7eeiuTCXV7uhgiq5Oo78lSVLz2qmteg8BCzHGV5LvX6C8evSz2xiTdJNdnL3M+MRMZRXpxESB/Lm5SvR37752ctk073jL0WQVKcVQf4/R35IkadepVeP0p8lq0SvArwE54MTqYIxxKoTQGkI4tNWxGOO5Gs1V0hWKpRKTF+bfENpwsbAW/T1wYB+5bJpH786Sy5avRzL6W5Ik7RW1aJyeiDGOhxC6gN8GPgP8hxq87pb196fq+fYMDqbr+v66cXupZkvLK5w4O8MPT13kB6cu8oPTF/nh6UvML5avR2prbWEkm+Ytd2W5dfgAtx45wC3DB0h1d9R55uvtpZrtFtas+Viz5mK9mo81ay7bbpxijOPJ74shhM8Bfw78DjC6ekwIYQAoxRjPhRDGtjJ2I3Oani5QLJY2PnAHGJPcfHZzzeZWo7+rUu3OTM+ykpwfXUn092P3DJWjv7Npjgz00NG+/nqk+cIC84WFq71FXezmmu1W1qz5WLPmYr2ajzWrn9bWli0ttGyrcQoh9ALtMcaLyVa99wOvAd8EukMIjyfXKz0PvJw8batjkq6hVCpxfmZxbatd0ihNXVxrdg6kOsll0tx/e39lq92g0d+SJEmbst0VpyzwxRBCG9AG/A3wj2KMxRDCB4EXQwj7SGLFAbY6JqlspVjk7PTculWk8XyBwvwSUI7+zhzq4dYj+3nygSOMZtOMZNMcMPpbkiRpy7bVOMUYfwAcv8bYN4D7ajkm7TWLl1c4Obl+Fenk5CxLy+Xo7/a2cvT3g3cOVrbaHR3sZV+n97aWJEmqJX+6khrEpbnLjE3MrLuJ7Nlzc5SSy/VWo79/7PhwsoqU4rDR35IkSTeFjZN0kxVLJaZWo7+rbiJ7oSr6u3//PnLZFG99c7ZyE9lD+43+liRJqhcbJ2kHLa8UOTU5W2mQxidmGJ8sML+4AkBrSwtHBnp48+ihyla7kUyq4aK/JUmS9jobJ6lG5haWGc/PrFtJOj1VFf3d0cZIJsWj9wxVUu2GB3rfEP0tSZKkxmPjJN2gUqnEhcLl8nVIE2uN0uSFtejv/b2d5LIp7ru1v7KSlDlo9LckSVKzsnGSrqNYLHH23Ny6VLuxibXob4DswW7eNLSfH7n/SHklKZPiQKqrjrOWJElSrdk4SYnFpXL093jVTWRP5gtcrkR/tzA8mOL4HQOVrXZHB1N0d3kaSZIk7Xb+xKc9aWbucmWLXf7CIt8dO7cu+runq51cNsWPHh9mJJNiNJtmqL+H9jajvyVJkvYiGyftaqVSicmLC4xPzHAiSbUbyxc4P7NYOWbwYDfD/b08fFemstWu/8A+o78lSZJUYeOkXWN5pcjpqdl1qXbj+Zl10d+H+3u4K9fHSCbNaDbFSDbNLblDTE7O1Hn2kiRJamQ2TmpK84vLjFeFNYzlZzg9NcvySnmvXWdHKyOZFI/cPVRJtRse6KWzw+hvSZIk3TgbJzW01ejv6lS78YkC+QvzlWPSPR3ksmmefvgQuUw5tCF7sIfWVrfaSZIkqTZsnNQwisUSE+fnyitIVY3SzNxa9HfmYDe5bIrHjx2urCQd6O30eiRJkiTtKBsn1cXlpRVOTc0mN5FNVpImC1xeKkd/t7W2MDzYy/23D5DLlBukkYzR35IkSaoPfwrVjivML627Fml8osCZ6TmKSfZ3d1c7uUyKJ+8fJpdNMZJJcWSg1+hvSZIkNQwbJ9VMqVRi+uJCOfY7v9Yonbu0Fv19MN3FaDbNg3cOVm4iO2D0tyRJkhqcjZO2ZHmlyJnpucpK0mqjNLe4DEBLCxzu7+XOo33lbXbZFLlMinRPZ51nLkmSJN04GydtaDX6ezxfqFyTdGqqsBb93d7K0UyKt96drVyPNDzYS5fR35IkSdolbJy0zoXC4htS7fLn16K/U90djGZTPP3QCCPZFKPZtNHfkiRJ2vVsnPaoYqlE/vz8WmhD0ihdmr1cOWawbx+5bJq33zuUXI+Upi9l9LckSZL2HhunPWBpeYWTk7OV5mh8orztbnFpBUiivwd6OXZrf+VapJFMmp59/uchSZIkgY3TrlOYX2K8apvdWL7Amanq6O82RjJpnjh2uJJqZ/S3JEmSdH02Tk2qVCoxfWmB8YkksCFplKaviP4eyaQ4fsdgObRhKM3AgX20utVOkiRJuiE2Tk1geaXI2ek5xvJr1yON5wvMLqxFfw8d6uH2o308laTajWRT7Df6W5IkSaoJG6cGs3B5mZP52WQVaYYTEwVOTc6yvFIEoKO9laODKR6+K8NIstXu6ECKrk6jvyVJkqSdYuNURxdnLyepdslKUr5A/twcpWQ81d1BLpviHW85Si6bYiSbZuhQN22tXo8kSZIk3UwN2TiFEO4EXgL6gWnguRjj9+o7q60rlkpMnp9fC2xItttdrIr+HjhQjv5+9J4suUx5Jelgusvob0mSJKkBNGTjBLwAfDbG+PkQwrPAi8BTdZ7TDVkpFvnr70/z2vem+NbfTnFpbgkoR38fGejl3lsOVVLtRjIpevZ11HnGkiRJkq6l4RqnEEIGeBB4OnnoC8BnQgiDMcbJ+s3sxrzyrTO89JVIT1c7993Wz5tHDzKaTXNkoJeOdrfaSZIkSc2k4RonYAQ4FWNcAYgxroQQTiePN03j9La7s+V0u0zKeyRJkiRJTa4RG6dt6+9P1fX9BwfTAIwMH6zrPLR5qzVT87BmzceaNR9r1lysV/OxZs2lERuncWA4hNCWrDa1AUeSxzdlerpAsVja+MAdMDiYZnJypi7vra2xZs3HmjUfa9Z8rFlzsV7Nx5rVT2try5YWWhpuD1mMMQ+8BjyTPPQM8GozXd8kSZIkaXdpxBUngOeBl0IIvwGcB56r83wkSZIk7WEN2TjFGP8f8LYtPLUNystv9VTv99eNs2bNx5o1H2vWfKxZc7Fezcea1UfV33vbjTyvpVSqz7VAO+Rx4L/XexKSJEmSGt4TwCubPXi3NU5dwMPAGWClznORJEmS1HjagMPAXwGLm33SbmucJEmSJKnmGi5VT5IkSZIajY2TJEmSJG3AxkmSJEmSNmDjJEmSJEkbsHGSJEmSpA3YOEmSJEnSBmycJEmSJGkD7fWewG4SQrgTeAnoB6aB52KM36vvrPaeEMLrwELyC+CjMcavhhAeAV4EuoHXgWdjjPnkOVsa09aEED4FvBt4E3BfjPE7yePXPId2Ykybd52avc5VzrdkzHOuTkII/cCfALdRvrnj94GfjzFO7kRdrNn2bVCzEvBtoJgc/sEY47eT570T+E3KP9N9E/iZGOPcdsa0eSGELwG3UK5NAfilGONrfp7tTq441dYLwGdjjHcCn6X8IaL6eE+M8YHk11dDCC3A54FfSOrzdeATAFsd07Z8CfgR4MQVj1/vHNqJMW3etWoGV5xvsPXzynOuZkrAJ2OMIcZ4DPhb4BM7URdrVjNXrVnV+GNV59lq05QCfg94Z4zxdmAG+Mh2xnTDfjrGeH+M8TjwKeAPksf9PNuFbJxqJISQAR4EvpA89AXgwRDCYP1mpSoPAQsxxleS718A3rvNMW1RjPGVGON49WPXO4d2Ymyn/my71dVqtgHPuTqKMZ6LMX6t6qH/CYyyM3WxZjVwnZpdz08A/6tq1eEF4H3bHNMNiDFerPr2AFD082z3snGqnRHgVIxxBSD5/XTyuG6+Pw0hfCuE8LkQQh+Qo+pfymOMU0BrCOHQNsZUW9c7h3ZiTLVz5fkGnnMNI4TQCnwY+HN2pi7WrMauqNmqr4UQXgsh/JsQQlfy2Lq/e2CMtf+/bXVMNyiE8PshhDHg48BP4+fZrmXjpN3oiRjj/cDDQAvwmTrPR9rNPN8a3+9SvvbC2jSPK2uWizE+RHm77N3Ar9drYnqjGOPPxRhzwK9Rvm5Mu5SNU+2MA8MhhDaA5PcjyeO6iVa3E8UYF4HPAW+n/K9plS0PIYQBoBRjPLeNMdXW9c6hnRhTDVzjfAPPuYaQhHrcAbwvxlhkZ+pizWroKjWrPs8uAb/PNc4zyitJ49sc0xbFGP8E+DHgJH6e7Uo2TjWSpAe9BjyTPPQM8GqMcbJ+s9p7Qgi9IYQDydctwPsp1+WbQHcI4fHk0OeBl5OvtzqmGrreObQTYzv/J9r9rnO+gedc3YUQPg68Bfh7SWMLO1MXa1YjV6tZCOFgCKE7+bodeA9r59lXgIdDCHck31f/3W91TJsUQkiFEEaqvn8ncA7w82yXaimVSvWew64RQriLckzkQeA85ZjIWN9Z7S0hhFuBLwJtya+/AX45xngmhPAY5QSafazF5U4kz9vSmLYmhPBp4KeAIWAKmI4x3nO9c2gnxrR5V6sZ8E6ucb4lz/Gcq5MQwj3Ad4DvAvPJwz+MMf79naiLNdu+a9UM+CTlv9sS0AF8A/jHMcZC8rx3Jce0Aa8CH4oxzm5nTJsTQsgCXwZ6gRXKTdNHYoz/28+z3cnGSZIkSZI24FY9SZIkSdqAjZMkSZIkbcDGSZIkSZI2YOMkSZIkSRuwcZIkSZKkDdg4SZIaUgjhhRDCr19nvBRCuL3G7/mBEMJ/ruVrSpJ2B+PIJUk7LoTwfuCfAPcCs5TvT/MS8O9ijFv6IAohlIA7Yozfv8rY14BHgGVgAfg68Aur95iqhRDCh4CfizE+vtGxkqTm54qTJGlHhRB+Ffgd4Dcp30A3CzwPvB3ovMZz2mrw1r8YY0wBdwJ9wG/V4DUlSXtUe70nIEnavUIIB4B/RfkO91+sGnoV+EDVcX8EzAOjwJPAu0IIzwInY4wfS475p8CvACXgY5udQ4zxXAjhi8CHq+b0u8BPAHPA7wH/OsZYvHIVKVnV+jDwq8AA8GfALwJ3AS8AHSGEArAcY+wLIfwk8ClgBLgE/FaM8VObnaskqXG54iRJ2kmPAl3Alzdx7D8EPg6kgVeqB0IIfwf4CPA0cAfwjs1OIIQwALybcrMG5abpAHAr5SbtOeBnrvMSfxd4GLgfeC/w4zHG/0t51ewvY4ypGGNfcuy/B34+xpimvC3xLzY7T0lSY3PFSZK0kwaAqRjj8uoDIYRvAHdTbqh+PMb49WToyzHG/5F8vRBCqH6d9wJ/GGP8TvIa/wJ4ZoP3/nQI4VOUr6n6GvAryRbA9wHHY4wzwEwI4d8CH6Tc9FzNJ2KMF4ALIYT/CjwAfOUaxy4Bd4cQ/jrGeB44v8EcJUlNwhUnSdJOmgYGQgiVf6iLMT6WrNBMs/5zaPw6r3PkivETm3jvX44x9sUYh2OMH4gxTlJu5DqveP4JYPg6r3O26us5IHWdY98N/CRwIoTw30IIj25inpKkJmDjJEnaSX8JLALv2sSx10vXO0P5uqFVuS3OZ4ryqtDoFa91aguv9Yb5xhj/Ksb4LiADfAl4eSuTlCQ1HrfqSZJ2TIzxQgjhXwKfCyG0UN7iNgccA3pv4KVeBv4whPDHwOvAP9/ifFZCCC8DHw8hPAccohw4sZUAhwngaAihM8Z4OYTQCfwD4D/GGC+GEC4BK1uZpySp8bjiJEnaUTHGT1JuTv4ZkKfccLwIfBT4xiZf4z8Bv005bOH7bC904ZcoX/f0A8ohFH8G/MEWXucvgP8DnA0hTCWPfRB4PWmangee3cY8JUkNxBvgSpIkSdIGXHGSJEmSpA3YOEmSJEnSBmycJEmSJGkDNk6SJEmStAEbJ0mSJEnagI2TJEmSJG3AxkmSJEmSNmDjJEmSJEkbsHGSJEmSpA38fxuQeZJ2rtAMAAAAAElFTkSuQmCC
+"
+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div><div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Also this behaviour looks – at a first glance – linear. We can again fit a first-order polynom (and re-use our previously defined function <code>curve_fit</code>)!</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [29]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">_fit</span><span class="p">,</span> <span class="n">_cov</span> <span class="o">=</span> <span class="n">common</span><span class="o">.</span><span class="n">print_and_return_fit</span><span class="p">(</span>
+ <span class="p">[</span><span class="s2">"PM_LD_CMPL (min)"</span><span class="p">,</span> <span class="s2">"PM_ST_CMPL (min)"</span><span class="p">],</span>
+ <span class="n">df_ldst</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"Grid Points"</span><span class="p">),</span>
+ <span class="n">linear_function</span><span class="p">,</span>
+ <span class="n">format_value</span><span class="o">=</span><span class="s2">".4f"</span>
+<span class="p">)</span>
+<span class="n">fit_parameters</span> <span class="o">=</span> <span class="p">{</span><span class="o">**</span><span class="n">fit_parameters</span><span class="p">,</span> <span class="o">**</span><span class="n">_fit</span><span class="p">}</span>
+<span class="n">fit_covariance</span> <span class="o">=</span> <span class="p">{</span><span class="o">**</span><span class="n">fit_covariance</span><span class="p">,</span> <span class="o">**</span><span class="n">_cov</span><span class="p">}</span>
+</pre></div>
+
+ </div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+ <div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Counter PM_LD_CMPL (min) is proportional to the grid points (nx*ny) by a factor of 2.3437 (± 0.000037)
+Counter PM_ST_CMPL (min) is proportional to the grid points (nx*ny) by a factor of 0.5860 (± 0.000019)
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div><div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Let's overlay this in one common plot:</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [28]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="p">(</span><span class="n">ax1</span><span class="p">,</span> <span class="n">ax2</span><span class="p">)</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="n">df_ldst</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"nx"</span><span class="p">)[</span><span class="s2">"Loads / Loop Iteration"</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">True</span><span class="p">);</span>
-<span class="n">df_ldst</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"nx"</span><span class="p">)[</span><span class="s2">"Stores / Loop Iteration"</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax2</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">True</span><span class="p">);</span>
+<span class="k">for</span> <span class="n">ax</span><span class="p">,</span> <span class="n">pmu_counter</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">([</span><span class="n">ax1</span><span class="p">,</span> <span class="n">ax2</span><span class="p">],</span> <span class="p">[</span><span class="s2">"PM_LD_CMPL (min)"</span><span class="p">,</span> <span class="s2">"PM_ST_CMPL (min)"</span><span class="p">]):</span>
+ <span class="n">df_ldst</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"Grid Points"</span><span class="p">)[</span><span class="n">pmu_counter</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">True</span><span class="p">);</span>
+ <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
+ <span class="n">df_ldst</span><span class="p">[</span><span class="s2">"Grid Points"</span><span class="p">],</span>
+ <span class="n">linear_function</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s2">"Grid Points"</span><span class="p">],</span> <span class="o">*</span><span class="n">fit_parameters</span><span class="p">[</span><span class="n">pmu_counter</span><span class="p">]),</span>
+ <span class="n">linestyle</span><span class="o">=</span><span class="s2">"--"</span><span class="p">,</span>
+ <span class="n">label</span><span class="o">=</span><span class="s2">"Fit: </span><span class="si">{:.2f}</span><span class="s2"> * x + </span><span class="si">{:.2f}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="o">*</span><span class="n">fit_parameters</span><span class="p">[</span><span class="n">pmu_counter</span><span class="p">])</span>
+ <span class="p">)</span>
+ <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">();</span>
</pre></div>
</div>
@@ -14782,7 +15098,7 @@ mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.ld_st.bin.csv .
<div class="output_png output_subarea ">
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8u127dvX/D1pk2btGfPHnXo0EGS1L9/f33wwQeSpB07dmj9+vXq06dP2J2tC1w8dBUAAACoF6ocGTpy5IgefvhhHTlyRKZpqmnTppo+fboMw9CQIUM0fPhwdenSRc8//7w2bNgg0zTlcrk0ZcqU4GjRH/7wB40ZM0bXX3+9TNPUE088ofj46E97i4RtmTxnCAAAAKgHqgxDLVq00KxZsyrdd/J9Q5mZmac9R2xsrF5++eUIulf32JYhnz8gfyAgkzX1AQAAAMeqcpocynPZpV+Zj6lyAAAAgKMRhsJkW6VfWYk3eos5AAAAAKg+wlCYysIQD14FAAAAnI0wFKayaXKEIQAAAMDZCENhsq3SRRMIQwAAAICzEYbCFLxnyMc9QwAAAICTEYbC5Cq7Z4hnDQEAAACORhgKk809QwAAAEC9QBgKE6vJAQAAAPUDYShMruA9Q4QhAAAAwMkIQ2Gy7eOryfHQVQAAAMDRCENhYpocAAAAUD8QhsLENDkAAACgfiAMhclmaW0AAACgXiAMhYmltQEAAID6gTAUJpdVuoBCiY8FFAAAAAAns0NpNGzYMO3evVumaSo2NlaPP/64UlJSyrWZNm2aFi9eLMuyZNu2RowYoT59+kiSxowZoy+//FJJSUmSpP79++v++++v4Y9ydrCAAgAAAFA/hBSGMjMzlZCQIElaunSpxo4dq7lz55Zr07VrV91zzz1q3Lixvv/+e911111asWKFGjVqJEkaOnSo7rrrrhru/tkXnCbHPUMAAACAo4U0Ta4sCElSYWGhDMOo0KZPnz5q3LixJKlTp04KBALKz8+voW7WHaZhyDINVpMDAAAAHC6kkSFJGjdunFauXKlAIKDXXnvtjG3nzZunc889V+ecc05w25tvvqkPPvhA7dq10x//+Eedf/75kfc6yizLYJocAAAA4HBGIBAIayWAefPmadGiRZoxY0al+//5z39q1KhReuONN9SxY0dJ0r59++TxeGSapubNm6eXXnpJS5culWVZ1f8EUXDH+MX6z25t9X9+0zXaXQEAAAAQobDDkFR6f9A//vGP4IIIZdatW6dHHnlEr7zyijp37nza43v16qWPPvpIbdq0CfmaubmF8vujt4Kbx5OgnJwCSdKIqSt06QUtdPevLo5af1B3nVwrwJlQKwgH9YJQUSsIVX2qFdM01Lx5fPjHVdWgqKhIWVlZwffLli1T06ZNlZiYWK7dt99+qxEjRujll1+uEIT27dsXfL18+XKZpqlWrVqF3dm6wrZMpskBAAAADlflPUNHjhzRww8/rCNHjsg0TTVt2lTTp0+XYRgaMmSIhg8fri5dumjSpEk6evSoJkyYEDx2ypQp6tSpk0aPHq3c3FwZhqH4+Hi9+uqrsu2Qb1eqc2ybMAQAAAA4XZWJpEWLFpo1a1al+06+b+jDDz887TlmzpwZfs/qMJdlqISltQEAAABHC2lpbZRXOk0uevcvAQAAAKg+wlAEmCYHAAAAOB9hKAIuy+ShqwAAAIDDEYYiYFumvNwzBAAAADgaYSgCtmUwTQ4AAABwOMJQBFy2qRIWUAAAAAAcjTAUAabJAQAAAM5HGIpA6dLahCEAAADAyQhDEXARhgAAAADHIwxFwLYNHroKAAAAOBxhKAJMkwMAAACcjzAUAZdlyucPyB9gdAgAAABwKsJQBGy79GtjRTkAAADAuQhDEbCt42GIqXIAAACAYxGGIuCyDEniwasAAACAgxGGIhAcGWKaHAAAAOBYdiiNhg0bpt27d8s0TcXGxurxxx9XSkpKuTY+n0+TJ0/W8uXLZRiGhg4dqoEDB1a5z4mC9wwxTQ4AAABwrJDCUGZmphISEiRJS5cu1dixYzV37txybRYsWKBdu3bps88+U35+vtLT03XVVVepbdu2Z9znRK7jI0MlhCEAAADAsUKaJlcWhCSpsLBQhmFUaLN48WINHDhQpmmqWbNm6tu3r5YsWVLlPidiAQUAAADA+UIaGZKkcePGaeXKlQoEAnrttdcq7M/KylLr1q2D75OTk5WdnV3lPieyjy+g4PWygAIAAADgVCGHoaeeekqSNG/ePE2ZMkUzZsyotU5Vpnnz+LN6vcp4PKUjZC0OHZMkxSXEBLcBJ6MuECpqBeGgXhAqagWhaui1EnIYKpOenq4JEyYoLy9PSUlJwe3Jycnau3evunbtKqn8aNCZ9oUqN7dQfn/0RmI8ngTl5BRIkgoLj0qSDuQWKadpo6j1CXXTybUCnAm1gnBQLwgVtYJQ1adaMU0josGTKu8ZKioqUlZWVvD9smXL1LRpUyUmJpZr179/f82ePVt+v18HDx7U0qVL1a9fvyr3OZGLpbUBAAAAx6tyZOjIkSN6+OGHdeTIEZmmqaZNm2r69OkyDENDhgzR8OHD1aVLF6Wlpembb77RDTfcIEl64IEH1K5dO0k64z4nsoMPXSUMAQAAAE5VZRhq0aKFZs2aVem+k+8bsixLkyZNqrTdmfY5Ec8ZAgAAAJwvpKW1UV5wmpyP1eQAAAAApyIMRaDsOUMl3DMEAAAAOBZhKAI8dBUAAABwPsJQBFz28YeuEoYAAAAAxyIMRcBimhwAAADgeIShCJiGIcs0WEABAAAAcDDCUIRs22SaHAAAAOBghKEIuSyTh64CAAAADkYYipBtGfJyzxAAAADgWIShCNkW0+QAAAAAJyMMRchlmyphAQUAAADAsQhDEbItk2lyAAAAgIMRhiLENDkAAADA2QhDEXJZBmEIAAAAcDDCUIRKnzPEPUMAAACAUxGGImTznCEAAADA0eyqGuTl5WnUqFHatWuX3G63zjvvPD3xxBNq1qxZuXZ333238vLyJEk+n09btmzR/PnzdfHFF2vMmDH68ssvlZSUJEnq37+/7r///lr4OGePi3uGAAAAAEerMgwZhqF7771XvXr1kiRlZmbqz3/+s55++uly7WbOnBl8vXTpUr344ou6+OKLg9uGDh2qu+66q4a6HX22zWpyAAAAgJNVOU0uMTExGIQk6bLLLtPevXvPeMycOXN0yy23VL93dZjNAgoAAACAo4V1z5Df79d7772nX/7yl6dtc+DAAa1atUppaWnltr/55psaMGCAhg0bpm3btkXW2zqk9J4hFlAAAAAAnKrKaXIne/LJJxUbG3vG6W5z585Vnz59yt1TNGLECHk8HpmmqXnz5unee+/V0qVLZVlWyNdu3jw+nK7WCo8nIfg6IT5GPn+g3DagDHWBUFErCAf1glBRKwhVQ6+VkMNQZmamdu7cqenTp8s0Tz+g9NFHH2nUqFHltrVq1Sr4Oj09Xc8884yys7PVpk2bkDuam1sovz96IzEeT4JycgqC773FPpWU+MptA6SKtQKcDrWCcFAvCBW1glDVp1oxTSOiwZOQpsm98MIL+u677zRt2jS53e7Ttlu7dq0KCgp0zTXXlNu+b9++4Ovly5fLNM1yAcmJbNtgaW0AAADAwaocGdqyZYumT5+u9u3b6/bbb5cktW3bVtOmTVNaWpr+9re/BYPNRx99pPT09ArT30aPHq3c3FwZhqH4+Hi9+uqrsu2wZujVObZlKhCQfH6/rDOMlAEAAACom6pMJBdeeKE2b95c6b758+eXez958uRK25287HZ94bJKA5DXG5B1+sEyAAAAAHUUQxoRso+HIabKAQAAAM5EGIqQbR8fGSIMAQAAAI5EGIpQXKPSGYaHCouj3BMAAAAAkSAMRejCtomSpO935UW5JwAAAAAiQRiKUFJCjFo1i9WmnYQhAAAAwIkIQ9WQcl6SfvgpXz4/9w0BAAAATkMYqoaLz03U0WKfdmTXjyf3AgAAAA0JYagaLj43SZL0PVPlAAAAAMchDFVDkzi32njiCEMAAACAAxGGqinl3CRt2X1IJV7uGwIAAACchDBUTRefl6Rir1/bs36OdlcAAAAAhIEwVE2dzk2UIbHENgAAAOAwhKFqimvk0rmtErRpx8FodwUAAABAGAhDNeDSC5pry+5D2p1TGO2uAAAAAAgRYagG9O3eTjFuS/NXbI92VwAAAACEyK6qQV5enkaNGqVdu3bJ7XbrvPPO0xNPPKFmzZqVazdmzBh9+eWXSkoqffZO//79df/990uSDhw4oFGjRmnPnj2KiYnRk08+qUsvvbQWPk50xDd26YYe7fTxyh3amV2g885JiHaXAAAAAFShypEhwzB077336tNPP9WCBQvUrl07/fnPf6607dChQzV//nzNnz8/GIQk6bnnnlP37t316aefasKECRo5cqQCgUDNfYo64IYe7RTXyNa85T9GuysAAAAAQlBlGEpMTFSvXr2C7y+77DLt3bs3rIssWbJEt99+uySpe/fuiomJ0fr168Psat0W28ilfj3P1TfbcrVt76FodwcAAABAFcK6Z8jv9+u9997TL3/5y0r3v/nmmxowYICGDRumbdu2SSqdZhcIBMpNq0tOTlZ2dnY1ul039e3eVvGNXXrn0x90rMQX7e4AAAAAOIMq7xk62ZNPPqnY2FjdddddFfaNGDFCHo9Hpmlq3rx5uvfee7V06dIa62jz5vE1dq5IeTxV3wv0yO2X66mZ/9Tbn/2gMf+7h0zTOAs9Q10TSq0AErWC8FAvCBW1glA19FoJOQxlZmZq586dmj59ukyz4oBSq1atgq/T09P1zDPPKDs7W23atJEkHTx4MDg6lJWVpXPOOSesjubmFsrvj959Rh5PgnJyCqps17FVvH577QV6f9lWvTrn37rt2gvOQu9Ql4RaKwC1gnBQLwgVtYJQ1adaMU0josGTkKbJvfDCC/ruu+80bdo0ud3uStvs27cv+Hr58uUyTTMYkPr376/3339fkrRmzRodPXpUl1xySdiddYrre7TTtZe30ZLVu/Txyu3y17PFIgAAAID6oMqRoS1btmj69Olq3759cBGEtm3batq0aUpLS9Pf/vY3tWrVSqNHj1Zubq4Mw1B8fLxeffVV2Xbp6f/4xz/q0Ucf1bx58xQTE6MpU6ZUOrpUXxiGoUHXX6gjx7yat3y7dmQV6N7UFMU2ckW7awAAAACOMwIOWePaKdPkThYIBLT0692atWyrmjdtpN/166TO7ZtVfSAcrT4NOaN2USsIB/WCUFErCFV9qpVanSaHyBiGoeu7t9Ojd1wuvz+g597/t16c/Y32HCiKdtcAAACABo8wdBZc1C5RTw3ppYHXnq8tuw/p8ddW6/lZ/9a/tx6I6mgXAAAA0JCFtbQ2IueyLf2q13m6ukuylq3do//37z16ec63SkqIUbeLPOreyaML2yayFDcAAABwlhCGzrKEWLfSru6gX191ntZtOaCvNmTrH//eq//79W41jrF18bmJSjkvSee3aaq2nji5bCvaXQYAAADqJcJQlNiWqR4Xt1SPi1vqaLFX327L1cYdB7VxR57WbTkgSTINQ61bxOq8Vgk695wEndsyXi2TYpUY75ZhMIIEAAAAVAdhqA5o5LbVM6WVeqaUPpfpwKEj2pldoJ37CrQzu1Drf8zVyu+yg+3dtilPUmO1TGyslkmN1SyhkZrGu5UYH1P6Oy5GMW5GlAAAAIAzIQzVQS2aNlaLpo11RaeWkkqX6M4vLNaenELtzz+i/XmlP/vyjmj9jwfl9fkrnKNxjKWmcTFKjHerSZxbcY1cimtsKzam9Hd8I5diG9mKa+xSXCOXGrktxbgtmYw4AQAAoIEgDDmAYRhKSohRUkJMhX3+QEBFR0p0qLBY+UXHlF9QrENFx5RfWKxDhceUX1SsHdkFOnzUq6KjJarqqVJul6lGbluNXFYwIDVy28d/W2rksuRymXLblly2KZdlyuU6/tsu/QnuO/nHMuV2WcF2LBQBAACAaCMMOZxpGEqIdSsh1q22OvODpvyBgI4e8+nw0RIVHQ9HZb+PHvPpWIlPR4u9Olrs07Fin44Wl74vOFysA4dOvC/x+uX1VW9JcEOSZRmyLFO2acgyS1+X/S63zTJO38YyZJnm8d+lP6ZhyDQNGUbZ+9IHcZnGSduObzeOby87zjBLv9OybUbwfDqxzTCC5yvbXnY905COBaT8vMMyjNIgaxgKXrv0z+zE9rJjTuwzKjlO3CMGAABQCwhDDYhpGIptZCu2ka0W1TyXPxBQidd/0o+v9LfPr+KS0t+oSiT7AAAgAElEQVQV9p304/UH5PP75fMFSn/8x7f5/PL5y7YF5A2+P36cz1t63PE23rJzlJ3PH5DfXzq10FfPnuFk6DQhyjgesHS6fce3q2LACv4+fgHj+JWMstfHjyvbZxhV7FdpoxNtTwS5E8cY5Y4Ntjulbdmx5fpWybkj73v5Y09uq5OOP3Gtss0ndVjl25+8v7L8apzSOC7OrcNFxRXaBr+HU85dflv5NuGdwzil7ZnPfepnrSyc18g5KrSt/Lus8GdRyfmrPscp/avk/JXtrfTPtZJzVLbx1O/99O0q71PTA4d16NDhM7aMWv8qvcYZ/oyrbnqadqE1rPx7iPzzhfp9hf45QjtfqMeeqrDEr7y8wxW2h/7ZQrvwmf/7ObMz/m9ACDtC7WPI14ikXQiNavLPNVTh/AOq6baVV3DsDOeq4njTUJNYd8jXq4sIQ4iIaRiKcVmKcdXthRr8gYD8/oACgdKQ5D8ekvyBgAL+gPwBye8PyBd8X9q+bPuJ92faXnreQCCgQECKT2ikn38+EnzvP/677H1AOsO+E+/9J28/ZX+F40567dfJ+0rPo5PPWck1dFK/JAWnUwYCAZVFyrLrl7XV8X0ntz3RTgr4j+8PHnPydVS6J3iu0+wP7qt4/rLrlx17oh8njq3wOULs+/Gt5fp3Uo90yq+Tpp8GTnl/QqCycxiVtwUAwCmGDviFrux8TrS7ETHCEOo10zBkWjX4zy0h8HgSlJNTcFavCWeqrFaCwSy44aR9pwlbpwtjp54jcHLUq+ocp213clw89fzlDwrlHKde/7SB8zTnrrTtKeev+H2Ef47TCTXMBnT6zxzqNZKSYoP/2l/Z+ULcdJp21elf9b6ryvtYSX9CPrayTZF/X5X3O7TvK1r9a9Kk9B/lqmoXYveq9WdcrVo9bdvI6zUUoZyr0s9VsVFo1wutWWjnCqHzJ7dIiG+kgsKjVTc8DdM01PX85qF1ro4iDAFAHXKm6W2hT/BAfeTxJCinMf+3jarxj3IIFbUimdHuAAAAAABEA2EIAAAAQINEGAIAAADQIDlm8nFdeEhnXegDnIFaQaioFYSDekGoqBWEqr7USqSfwwiEukQIAAAAANQjTJMDAAAA0CARhgAAAAA0SIQhAAAAAA0SYQgAAABAg0QYAgAAANAgEYYAAAAANEiEIQAAAAANEmEIAAAAQINEGAIAAADQIBGGAAAAADRIhCEAAAAADRJhCAAAAECDRBgCAAAA0CARhgAAAAA0SIQhAAAAAA0SYQgAAABAg0QYAgAAANAgEYYAAAAANEiEIQAAAAANEmEIAAAAQINEGAIAAADQIBGGAAAAADRIhCEAAAAADRJhCAAAAECDRBgCAAAA0CARhgAAAAA0SIQhAAAAAA0SYQgAAABAg0QYAgAAANAgEYYAAAAANEh2tDsQqry8Ivn9gahdv3nzeOXmFkbt+nAOagWholYQDuoFoaJWEKr6VCumaSgpKS7s4xwThvz+QFTDUFkfgFBQKwgVtYJwUC8IFbWCUDX0WmGaHAAAAIAGiTAEAAAAoEEiDAEAAABokBxzz1BdcPioV8+887X+T1pntfXER7s7AAAAZ5XP51VeXo683uJodwU1YP9+U36/P9rdCJttu5WU5JFlVT/KEIbC8PPhYu05UKTd+wsJQwAAoMHJy8tRo0axios7R4ZhRLs7qCbbNuX1OisMBQIBFRX9rLy8HLVokVzt8zFNLgy2VfoffYnPWUUDAABQE7zeYsXFNSEIIWoMw1BcXJMaG50kDIXBZZV+XV5fw16CEAAANFwEIURbTdYgYSgMtl36dZU4bDgRAACgPlq2bKl+//tBuvvuQRo06BZNnDguuO/11/+qkpKSKPZOmjnzNX344awK2x98cKhWrlx+1vvz1FMT9eGHH0iS1q5do9WrV9XKdWbNeld5eQeD7+fNm6MPPvh7rVyrurhnKAwnRoYIQwAAANF04MABPf/8n/T66++oVatzFAgEtHXrD8H9b745Q3fc8Tu5XK6wzuv1emXbNfNX5BUrvtBTT02pkXPVtHXrvtaxY0d1xRW9wj7W5/PJsqzT7p816z11795TSUnNJEnp6bdG3M/aRhgKQ9nIkNNuNAMAAKhvDh48IMuy1bRpoqTSqVMXXthJkvTcc5mSpPvvv0eGYWrq1L+qpKRYzz77jPbu3a1AIKA77vidfvWrVEnSrbcOUGpqmr7++l9q3bqNHntsgj75ZKE++mi2fD6f4uPjNXLkGJ17bnutX/+NXnhhivz+gLxerwYPvkfXX9+/Qv/279+nQCCgVq3OCfkz7d79k5599mnl5+fJsiwNHfqArrzyPyRJX331pf7617/I7/crMTFJjz46Vm3bttPatWv00kvPqVOni7V16w+yLEtjx05Uhw4dT3udbdu2av78jxQI+PXPf67WddfdoN/97m6tWrVCb7/9ho4dK5bL5dJDD/2XLrmki9auXaOXX35el156mTZt2qjBg/+goqIizZ79nrze0tG3Bx54RN2799Rbb72uAwdyNH78aLndMcrImKxlyz7XkSNH9OCDj8jn8+nVV6dq9eovJUm9ev2H7r//IVmWpaeemii3262fftql/fv3qXPnLho/flKtTs0kDIXBNAxZpsECCgAAoMFbuT5LK77NqpVzX901Wb27nHmlsAsuuEi/+EVn3XLLr3X55Veoa9fL1K/fjWraNFF//ONozZ07W6+++oZiY2MlSRMmPKaOHc/XM8/8WQcOHNAf/nCnOnW6WB07XiCpdKRp6tS/SpK++Wadli37XNOmzZDb7daqVSv1zDNP6NVX39Df//6WbrttkPr3/7UCgYAKCwsr7d/y5f/Q1VdfE9bnnjRpvNLSblZqarq2b/9RDz44RO+8M0dSQJMnT9DUqX9Thw4dtXDhPE2aNF4zZrwlSdq2bYseeWSkLr/8Cn3yyUJNnpyh11//79Ne5/zzL1Ba2m907NhRDRv2sCRpz57dmjnzdT3//FTFxcXrxx+3aeTI4froo0WSpB9/3KqRI8doxIhRkqRDh/J1/fX9ZBiGdu3aoYcfHqa5cxdr8OA/aMGCeZo8OTP43Z7s44/nasuWH/TGG6XT5kaOHK6PP56rm2++9fh1tunFF1+RaZr6/e/v1Jo1q9Wjx5VhfY/hIAyFybZMpskBAABEmWmaeuaZ5/Tjj1u1bt1aLV/+//Tuu/+tt99+X02aNK3Qfs2af+rBBx+RJLVo0UJXXXW11q5dE/wLe//+vw62XbnyC23dukVDh94tqXQ554KCnyVJ3bp11zvvzFR2dpZ69LhSnTtfUmn/Vqz4RzBohOLw4SJt3fqDbrzxJklShw4ddcEFnbRhw3pJ0vnnXxQc7bnxxpv03HOZOny4SJLUtm07XX75FZKkfv1u1JQpT6moqFBxcaE/Cmb16lXas2e3HnhgaHCbz+fTwYO5wWtccknX4L49e3Zr4sRxysnJkW3bOngwV7m5B9S8eYszXmfNmtW68cbU4PTFG28coC+++J9gGOrT5z8VExMjSerUqZP27NmtHj1C/hhhIwyFybYMeb2sJgcAABq23l2qHr05Gzp2vEAdO16gW265TXfdNVDr1n2t//W/fllp21OnW538Pja2cfB1ICD9+tc36d5776twjttuG6Teva/Rv/61Wi++OEU9elypoUOHlWtTWFiorKwsXXjhRSF/jkCg8r9fGoahQMCv2l7ELxAIqFevq/T4409U2Ldjx3Y1bhxbbtvEieP04IMjdM01/ym/36++fa9WcXHVy10HAmf+c4iJcQdfm6Yln88X7kcJC6vJhcm2TabJAQAARFlOzn599923wff79+9Tfn6ekpNbS5JiY+NUVHRiClv37j318cdzJUm5uQe0atVKXX5590rP3bt3Hy1Zskj79++TVDpC8v33myRJu3btVJs2bZWefosGDrxDmzZtqHD8qlUrgvf6hCouLl4XXHCRPvlkoSRp584d2rbtB/3iF5eoc+eu2rr1B+3cuUOS9MknC3XhhZ0UGxsnqfReo2++WSdJ+vzzJerY8YIqR4Xi4uLKTfHr2fNKrV69Sj/+uC24rbLPVqawsDD4XS9cOL9cEDr13Cfr0aOXFi9eIK/XK6/Xq08+Waju3Xuesa+1iZGhMLmYJgcAABB1Pp9Pr7/+V2VnZykmppECAb/uvfd+XXTRxZKk22+/U8OH36eYmEaaOvWveuSRkXr22ac1ePDtCgQCuu++B9Wx4/mVnvuyy7pp6NBhGjPmv+Tz+eX1lujaa/vq4otTNGfO+1q79mu5XLZcLrdGjHi0wvHLl/9DaWm/OWP/n356otzumOD7Z599SRkZk/Xss09r1qx3ZVmWxo9/QklJSZKk8eOf0KRJ4+Tz+ZSYmKQJE54MHnvhhRfp888/1UsvPSfLMjV+/KQqv79rrrlW48eP0t13DwouoDBhwpP605+e1LFjx+T1lqhLl0uVktK50uOHD/8vjR07Ui1aeHTZZd3UtOmJqYm33nq7nn76CTVq1EgZGZPLHXfTTTdr9+6f9PvfD5Ik9ex5lQYMuLnK/tYWI3C6MbmTbN++XWPGjFF+fr4SExOVmZmp9u3bl2uTm5urxx57TFlZWSopKdGVV16p8ePHy7Zt+Xw+TZ48WcuXL5dhGBo6dKgGDhwYVkdzcwvl90dveprHk6CcnAKN/dtXatcyXvenVz4/FCirFaAq1ArCQb0gVLVZK9nZO3XOOefVyrnri5KSEt1xx2/0/vtza2yJ7jNZu3aNpk176YwLJpyObZuOXSX51Fo0TUPNm4d+j1TwuFAaZWRkaNCgQfr00081aNAgTZgwoUKb6dOn6/zzz9eCBQu0YMECbdiwQZ999pkkacGCBdq1a5c+++wzffDBB5o6dap2794ddmfrApfNyBAAAAAq53K5NGfOgrMShFB9VYah3Nxcbdy4Uamppeuwp6amauPGjTp48GC5doZhqKioSH6/X8XFxSopKVGrVq0kSYsXL9bAgQNlmqaaNWumvn37asmSJbXwcWqfbXHPEAAAAOqGbt26RzQqhFJVhqGsrCy1atUq+JRZy7LUsmVLZWWVX1d+2LBh2r59u66++urgzxVXXBE8R+vWrYNtk5OTlZ2dXZOf46xxWYZjhxMBAAAAnFBj43dLlixRp06d9NZbb6moqEhDhgzRkiVL1L9/xSfyRiKSOYA1zeNJUGxjt46V+OTxJES7O6jDqA+EilpBOKgXhKq2amX/flOWZVRYGhnOZdvOW1w6EAjINM0aqfMqw1BycrL27dsnn88nyypd63v//v1KTi6/rvw777yjp59+WqZpKiEhQb/85S+1evVq9e/fX8nJydq7d6+6di19UNOpI0WhqCsLKPj9fh0+WsJNrDgtbnJGqKgVhIN6Qahqs1ZM09ahQ/mKi2tCIKoHnLiAQiAQUFHRzzJNu1ydR7qAQpVhqHnz5kpJSdHChQuVlpamhQsXKiUlRc2aNSvXrm3btvriiy/UtWtXFRcXa9WqVbr++uslSf3799fs2bN1ww03KD8/X0uXLtXf//73sDtbF7C0NgAAaKiSkjzKy8tRYWF+tLuCGmCapvx+5/291rbdSkry1My5Qmk0ceJEjRkzRq+88oqaNGmizMxMSdKQIUM0fPhwdenSRWPHjlVGRoYGDBggn8+nXr166bbbbpMkpaWl6ZtvvtENN9wgSXrggQfUrl27GvkAZ5ttmypxWIIGAACoCZZlq0WL5KobwhEYcQ7xOUN1QV2ZJvf6oo3atDNPfx7WO2p9Qd3G/7AgVNQKwkG9IFTUCkJVn2qlVp8zhBNctuW4uZUAAAAAKiIMhcm2DJX4HDGYBgAAAOAMCENhYgEFAAAAoH4gDIXJtkqXIHTIrVYAAAAAToMwFCbbNhWQ5IviYg4AAAAAqo8wFCaXVfqVMVUOAAAAcDbCUJhsq/RpyzxrCAAAAHA2wlCYbLtsZIhpcgAAAICTEYbCVDZNroRpcgAAAICjEYbC5CobGWKaHAAAAOBohKEw2SygAAAAANQLhKEw2UyTAwAAAOoFwlCYXMdXk2OaHAAAAOBshKEwsZocAAAAUD8QhsIUnCbHyBAAAADgaIShMLlYQAEAAACoFwhDYSpbWpsFFAAAAABnIwyFKbi0NtPkAAAAAEcjDIXpxAIKhCEAAADAyexQGm3fvl1jxoxRfn6+EhMTlZmZqfbt25drM2rUKG3evDn4fvPmzZo2bZquu+46TZ06Ve+++65atmwpSerWrZsyMjJq7lOcRWVLa5ewmhwAAADgaCGFoYyMDA0aNEhpaWmaP3++JkyYoLfffrtcmylTpgRff//99xo8eLD69OkT3Jaenq7Ro0fXULejx2YBBQAAAKBeqHKaXG5urjZu3KjU1FRJUmpqqjZu3KiDBw+e9pg5c+ZowIABcrvdNdfTOqJsmhxLawMAAADOVmUYysrKUqtWrWRZliTJsiy1bNlSWVlZlbYvLi7WggULdMstt5TbvmjRIg0YMED33HOP1q1bVwNdjw7TMGSZBiNDAAAAgMOFNE0uHEuXLlXr1q2VkpIS3Hb77bfrvvvuk8vl0sqVKzVs2DAtXrxYSUlJIZ+3efP4mu5q2DyeBEmly2u73HbwPXAqagOholYQDuoFoaJWEKqGXitVhqHk5GTt27dPPp9PlmXJ5/Np//79Sk5OrrT9hx9+WGFUyOPxBF/37t1bycnJ2rJli3r27BlyR3NzC+X3R2/RAo8nQTk5BZJK7xv6ueBo8D1wspNrBTgTagXhoF4QKmoFoapPtWKaRkSDJ1VOk2vevLlSUlK0cOFCSdLChQuVkpKiZs2aVWibnZ2tr7/+Onh/UZl9+/YFX2/atEl79uxRhw4dwu5sXWFbTJMDAAAAnC6kaXITJ07UmDFj9Morr6hJkybKzMyUJA0ZMkTDhw9Xly5dJElz587Vtddeq8TExHLHP//889qwYYNM05TL5dKUKVPKjRY5jW2ZKvGytDYAAADgZEYgEHDE3+rr0jS5cTO+UltPvO5PvyRq/UHdVZ+GnFG7qBWEg3pBqKgVhKo+1UqtTZNDRbZlMk0OAAAAcDjCUARKp8kRhgAAAAAnIwxFwMUCCgAAAIDjEYYiYNumSghDAAAAgKMRhiLgskx5WU0OAAAAcDTCUARsmwUUAAAAAKcjDEXAZTFNDgAAAHA6wlAEWFobAAAAcD7CUARs25SXpbUBAAAARyMMRcC2DKbJAQAAAA5HGIqAyzJVwmpyAAAAgKMRhiLgOr6aXCBAIAIAAACcijAUAdsq/dp8fsIQAAAA4FSEoQiUhaESFlEAAAAAHIswFAGXXfq1sbw2AAAA4FyEoQjYliFJ8vqYJgcAAAA4FWEoAiemyfmi3BMAAAAAkSIMRaBsmlwJI0MAAACAYxGGIlA2MuRlAQUAAADAsexQGm3fvl1jxoxRfn6+EhMTlZmZqfbt25drM2rUKG3evDn4fvPmzZo2bZquu+46+Xw+TZ48WcuXL5dhGBo6dKgGDhxYox/kbGIBBQAAAMD5QgpDGRkZGjRokNLS0jR//nxNmDBBb7/9drk2U6ZMCb7+/vvvNXjwYPXp00eStGDBAu3atUufffaZ8vPzlZ6erquuukpt27atwY9y9gRHhghDAAAAgGNVOU0uNzdXGzduVGpqqiQpNTVVGzdu1MGDB097zJw5czRgwAC53W5J0uLFizVw4ECZpqlmzZqpb9++WrJkSQ19hLPPVbaAAmEIAAAAcKwqw1BWVpZatWoly7IkSZZlqWXLlsrKyqq0fXFxsRYsWKBbbrml3Dlat24dfJ+cnKzs7Ozq9j1qbPv40tpeFlAAAAAAnCqkaXLhWLp0qVq3bq2UlJQaPW/z5vE1er5IeDwJkqTDx0NQbFxMcBtwMuoCoaJWEA7qBaGiVhCqhl4rVYah5ORk7du3Tz6fT5Zlyefzaf/+/UpOTq60/YcfflhuVKjsHHv37lXXrl0lVRwpCkVubqH8/uiNxHg8CcrJKZAkFfx8pLRPB4uC24AyJ9cKcCbUCsJBvSBU1ApCVZ9qxTSNiAZPqpwm17x5c6WkpGjhwoWSpIULFyolJUXNmjWr0DY7O1tff/118P6iMv3799fs2bPl9/t18OBBLV26VP369Qu7s3WFzT1DAAAAgOOF9JyhiRMn6p133lG/fv30zjvvaNKkSZKkIUOGaP369cF2c+fO1bXXXqvExMRyx6elpalt27a64YYbdNttt+mBBx5Qu3btavBjnF02S2sDAAAAjhfSPUPnn3++Zs+eXWH7jBkzyr2///77Kz3esqxggKoPXDx0FQAAAHC8kEaGUJ7r+GpyTJMDAAAAnIswFAEr+NBVltYGAAAAnIowFAHTMGSZBvcMAQAAAA5GGIqQbZsq4Z4hAAAAwLEIQxFyWSb3DAEAAAAORhiKkG0ZrCYHAAAAOBhhKEK2ZXLPEAAAAOBghKEIuWxTJawmBwAAADgWYShCLstkmhwAAADgYIShCNk20+QAAAAAJyMMRYh7hgAAAABnIwxFyGUZLK0NAAAAOBhhKEK2xUNXAQAAACcjDEWo9J4hVpMDAAAAnIowFCFWkwMAAACcjTAUIds2uWcIAAAAcDDCUIRcrCYHAAAAOBphKEIsrQ0AAAA4G2EoQrZtqMTLAgoAAACAU9mhNNq+fbvGjBmj/Px8JSYmKjMzU+3bt6/QbvHixXr11VcVCARkGIbefPNNtWjRQlOnTtW7776rli1bSpK6deumjIyMGv0gZ1vZNLmyzwoAAADAWUIKQxkZGRo0aJDS0tI0f/58TZgwQW+//Xa5NuvXr9df/vIXvfXWW/J4PCooKJDb7Q7uT09P1+jRo2u291FkW6WDal5fQC6bMAQAAAA4TZXT5HJzc7Vx40alpqZKklJTU7Vx40YdPHiwXLuZM2fqnnvukcfjkSQlJCQoJiamFrpcN5wIQ9w3BAAAADhRlWEoKytLrVq1kmVZkiTLstSyZUtlZWWVa7dt2zb99NNPuvPOO3XzzTfrlVdeUSBw4p6aRYsWacCAAbrnnnu0bt26Gv4YZ5/LLv3qWF4bAAAAcKaQpsmFwufzafPmzXrzzTdVXFyse++9V61bt1Z6erpuv/123XfffXK5XFq5cqWGDRumxYsXKykpKeTzN28eX1NdjZjHkxB8nZQYK0lq0iRWnqTG0eoS6qiTawU4E2oF4aBeECpqBaFq6LVSZRhKTk7Wvn375PP5ZFmWfD6f9u/fr+Tk5HLtWrdurf79+8vtdsvtduu6667Tt99+q/T09ODUOUnq3bu3kpOTtWXLFvXs2TPkjubmFsrvj97qbR5PgnJyCoLv/SVeSdJPe/Mlrzda3UIddGqtAKdDrSAc1AtCRa0gVPWpVkzTiGjwpMppcs2bN1dKSooWLlwoSVq4cKFSUlLUrFmzcu1SU1O1YsUKBQIBlZSU6KuvvtLFF18sSdq3b1+w3aZNm7Rnzx516NAh7M7WJQmxLklSweHiKPcEAAAAQCRCmiY3ceJEjRkzRq+88oqaNGmizMxMSdKQIUM0fPhwdenSRb/+9a/13Xff6cYbb5Rpmrr66qt16623SpKef/55bdiwQaZpyuVyacqUKeVGi5woIbZ0pbyCwyVR7gkAAACASBiBk1c5qMPq2jS5gsPFevjlFbqj74W6vnu7qPULdU99GnJG7aJWEA7qBaGiVhCq+lQrtTZNDpWLa+ySYTAyBAAAADgVYShCpmEoobGLe4YAAAAAhyIMVUNCrJuRIQAAAMChCEPVkBDLyBAAAADgVIShakiIdetnRoYAAAAARyIMVUNCrEuFjAwBAAAAjkQYqoYmsW4VHfXK6/NHuysAAAAAwkQYqoaEWJckqfAIU+UAAAAApyEMVUNCrFsSzxoCAAAAnIgwVA1lI0M/c98QAAAA4DiEoWo4MTJEGAIAAACchjBUDU3ijoehIqbJAQAAAE5DGKqG2Ea2TMNQwZETI0Mbth/Uj3t/jmKvAAAAAISCMFQNpmEovrFdbgGFmZ9s0uz/2RrFXgEAAAAIhR3tDjhdQpxbPxeVjgwdPupV7s/HdKyE5w4BAAAAdR0jQ9WU0NilguPPGdp7oEhS6XOHygISAAAAgLqJMFRNTeLcKjgefHbnFAa37znpNQAAAIC6hzBUTQmN3cF7hnbnFMoyDUnSnuOjRAAAAADqJsJQNSXEunT4mFden197corU/pwExcbYwSlzUum0ub99vIGpcwAAAEAdElIY2r59u37729+qX79++u1vf6sdO3ZU2m7x4sUaMGCAUlNTNWDAAB04cECS5PP5NGnSJPXt21fXX3+9Zs+eXWMfINoSyp41dLhEu3MK1cYTr9aeuHIjQ2s279dXG/fp6837o9VNAAAAAKcIaTW5jIwMDRo0SGlpaZo/f74mTJigt99+u1yb9evX6y9/+YveeusteTweFRQUyO0uDQoLFizQrl279Nlnnyk/P1/p6em66qqr1LZt25r/RGdZQmOXJOmn/YUqOupVW0+cTEP61/f7FQgEZBiGvt+ZJ0natCtf13Zz/mcGAAAA6oMqR4Zyc3O1ceNGpaamSpJSU1O1ceNGHTx4sFy7mTNn6p577pHH45EkJSQkKCYmRlLpiNHAgQNlmqaaNWumvn37asmSJTX9WaIiIbY0DJUFnraeeLVuEaeio14dKiqWPxDQpuP7Nu/KUyAQCB77+Zqf9P7/3XL2Ow0AAACg6jCUlZWlVq1aybIsSZJlWWrZsqWysrLKtdu2bZt++ukn3Xnnnbr55pv1yiuvBP/in5WVpdatWwfbJicnKzs7uyY/R9Q0OT5NbtOu0sDTxhOnNi3iJJUuorA3p0gFh0t0UbtEFRwuCU6f8/n9WvTlDn3+/9u79/CoynvR49+5XzIzyUwySSYkIdwJKGKDou3uVoENtIKye9Fqy+7ZFfv09KrnafeR+rTaY30q9bDQ0OQAABo6SURBVNnbdler1vr0nL1p9dTqsRar2yoUiwJyiRBiCCEh98wkmWQmM5n7zHv+GBgMYBLwEmB+n38ga61Z77ve9Zt55zfvWu/a20XwlHuJ4sn0R3gEQgghhBBC5KcP7KGr6XSa5uZmfvOb35BIJNiwYQMVFRWsW7fuA9l/cbHtA9nP++F2209bZrJmR786fSGcdhMzpxfjdGWToZFoimA0BcA/r13Ixl++Qbc/yuULPNQ39zNyfBa65p4Rrv/EDAD6hyLc8W/b+do/LmLFldUfxWGJD8GZYkWIM5FYEWdD4kVMlsSKmKx8j5UJkyGPx4PP5yOdTqPT6Uin0/T39+PxeMZsV1FRwerVqzEajRiNRpYvX87BgwdZt24dHo+H3t5eFi1aBJw+UjQZfn+YTEZNvOGHxO22MzAQOm15Rim0Gg0ZpagotjIwEEIphc1ioLl9iGA4TqnTQpnDRLHDzL53vFw1380ru9oxG3UU2kxs3dPJlXNLAHhm21HiiTR/eO0Ii2qK0GiyU3Vv29/NG4e8/M9bP4ZBf3JAL5lKo9dpc9uJqfdesSLEqSRWxNmQeBGTJbEiJutiihWtVnNOgycTXiZXXFxMbW0tW7ZsAWDLli3U1tbicrnGbLdmzRp27NiBUopkMsmuXbuYP38+AKtXr+aZZ54hk8kwNDTEq6++yqpVq866sucjrUaD7fh9Q9Pc2ROg0WioKCmgqz9Mc1eABdOdAMyfXsThzmESyTT7mgf42Fw3Vy8oo6UrwHAoTjyR5vW3e7FbDfQMjnK4MwBAJJbk2e1ttPWO8PqB3lzZI5EE//LYTv7v1qNj6hSOJnnzUB8ZNTZ5VGrqkkkhhBBCCCHON5OaWvvee+9l8+bNrFq1is2bN/OjH/0IgNtvv52GhgYArr/+eoqLi/n0pz/NunXrmD17Np/73OcAuPHGG6msrGTlypXcdNNNfOMb36CqqupDOqSPnj2XDBXklk0rKeBY3wixRJr5J5KhaiejsRQvv9VJNJ7iytoyrqgtRQF7D/ez8x0vkXiKr65diM1i4LV93QC8sqeLSDxFmcvKlp3tJI7fU/SHv7YSDCf4y54uOrzZrF4pxeMvNPLrLU28urc7V59EMs0Dv93P4y80jkmSgqMJnt3eetozkILhOP2B6GnHemqCJYQQQgghxIVqUvcMzZo164zPBnriiSdy/9dqtWzcuJGNGzeetp1Op8slUBcjh9VID6NUuk8Ozb07MTqRDNUe//fFnR3YLAYW1DjR67RUum281eQjlkhTXWZjQY2TT17m4eXdnXR4Q7yyp4sl89ws+1glP32qnr++3cvMCgc7DvZx7eIK9h8ZYPNfmtn4pTq21/fQeGyIYoeJZ7e3culMF57iAjb/5Qgt3UEgSHWpjU9dNZ14Ms2//+Egx/pGONwxzPduuRyjQUd/IMpPNu8jlkjzvS9czswKBwCHjvl57PlGViyp5Ma/m4FGo0Epxat7uzncOcx/+9R87NbshBLJVJoXd3Ywp6qIhTUnRxGHQ3Eajw1x1cIy9LqTuXjP4CgWow6Xw5xbppTCPxKj2GEecxlgJqPIKDXm9UIIIYQQQpwt3b333nvvVFdiMqLRBFM5KFFQYCISSZxxXUObn97BUb6wYk7uC3oimeaNBi+Vbhurl2YnQrCY9Ow85CUUSfLxS8q5fG52GvLRWJI3jy//7DWzmF5up8xp4dW93ew7MkA0luK//+OlzKxwcKQrwP7mfo50BdBqNXz7c4sospnZur+HjIIX3jhG7XQn3/n8Zbz+di/NXUGUUvzpzXbWfLwGR4GRrft6mFtVxLPbW2lqH2bFkkr2Nw/QH4gys8LBT39XTyKZpsBs4G8He1k0q5i2vhEefq4BrVZL47EhkukMtdOd/H7rUf644xjeoQj1LYMsnlVMMq146JkD7Gr0savRS4FZzwyPg8OdAf716Xp2vePjcOcwl84sxqjX8eLODh57vpG/HuilxGGmstTGyGiCJ/70Dv/5X0fo9Ueone7EaNDR0h3god8f4OXdnZS5rJS7rGQyitf2dfOLZxsYCEaZM60Qg17HyGiCp7e28MpbnbjsJtxFFgBae4I89VoLPQOj1JTbMei1pDMZdhzs49W9XTgKjLmkLDia4KVdHXT6QlS5bbnz29ob5K/1PVhNBops2Uk0UukMu5t8dPrCuGxGdMe3DYbj7Gz0YdBpc7MPnthHW88I7iIzOm1222QqQ0ObH6VULrGEbBLZ2hvEaTej02YTQ6UUXf1h4qkMBWZDbttkKk2HN4zdakCrPZlEBsJxIrEUFtPJ30CUUvQHopiNujHbxpNpIrEUJqNuTKyHo0m0Ws2YbTNKEY2nx9zLduJYIHtP3bul0pkxrz9RDwV5d+/beJ8rQpxK4kVMlsSKmKyLKVY0Gg3Wd313mvTr1AVyI8n5OoECwBsNfRzuGOa2NQtyy0KRBN/59x2svKKKLyyfk1v+v19q4vUDffzLLZfnRox8wxE2Pr4Lm8XAv37j4xj02S+gv3j2IPUtg1y9sIzb1y4EoKU7wE827wfgazcu5MraMjJK8ZP/3Edr7whWk577NizFaTfxVpOPx/7YCMDCGid33rSYeDLN//o/exkMRElnFDcvm82qK6t5aXcHz2xrxWLSo5Tie7dcjs1i4Ceb95HOKCKxFFWlNv7HzYt57vU2/lrfg6fYSp8/woolldTNdfOLZxsw6LXodRpCkST/tHoe+5oHqG8ZZEGNk6aOYcpdVq65rILn/taGxain3GWluSvAkvmlBMJxjnYHqZvr5kh3gGg8xRXzy3iryYfNYuCSGS7ePOTF5TBjNuroGRzlytpSfMNROrwhqkttdA2EsVuNXL2wjNcP9JFIprFbDQTCCZbMc6PRaNhzuB+rSU8knsJRYOTaxRXsburHNxRBr9OSSme4fE4JpU4L2/b3kExlUIDDamB5XSVHugI0tg/nzumiWcXMqSxkW30PQyNxIHvp5PKPVTI4EmNXo5dUOhu7C2e4uGxWMTsbfRzrG8lte83iaSileP1AL6HjswzWTndyRW0ph9qGeLtlkMzxiTn+bpGHogIjf2voo2cgO1X7/OoirlpYToc3xO53fETiKWwWA1ctLMuNPDa1D6OAuZWFXLWwnKFQnN3veBkIxLBZDFwxv5TZ0wppaPNT3zJIPJlm9rRCli4oI51R7Dnso7VnhAKznrp5bhbUuGjpDrL/yADDoTizKhzUzSulwKynvmWQxvYhjHoti2eXcOmsYnoHR6lvGaS7P0yNx87iOW5KiywcavNzsM1PJqO4dFYxi2YWExxNcLDVT0t3kEp3AYtmFVNVaudIV4CGNj/haJIFNU4unVlMOqM41ObncGcAl8PEpTOLmVnhoMMborF9iIFAjLmVhSyc4cJs1PNO+xCHO4exGPUsqHExp7IQ33CUw53D9AyEqS6zs6DGhctuorkrQHNnAKUUc6uLmFflJBRJ0NwVoN0bwuOyMq+6CE+xlbbeEY50BYjEUsyuLGRuVRHptKKlO0Bb7whFdhNzq4qoKrXRMzBKS3eAkWiSqpIC5lQWYTToaO0J0tobxGTUMWdaITUeB4PBGEe7g/iGI1S6C5hVUUihzcixvhCtvUFQMLPCwQyPg3A0SVvvCD0DYdxOC7MqCnEXWejqD9PWGySaSFNTbmemx0EqozjWN0KnL0RhgYkZHjuekgK8/gjt3hGC4QRVpTZmeBzo9Vo6vCE6vCHMRh015XamldoYGonR4Q0xGIxR7rJSU27HZjXQ1R+m0xdGKcX0cjvVpXbC0SSdvhDeoQjFhWaml9lx2U30+iN0+kLEk2kq3Taqy2yk0tlEv2dwFIfVQHWZndIiC/2BKF2+MP6R2Gn3QE6USE+UZ5/6+tM214z752kFnLr+gyjfbDIQiyfPvP6UJaeWN97xaCY4uImP5WzLfn9tfbb1PX39+PU97eUTvP6jLv+09j7D9lariUgkPv6Oc9tP8N6ZcAfva/WEDTCZn8je7+9oH34bvL8KThgj72MHNpuJ0fAEsTJOATqthitry7BZDO+90UfkXCdQkGRoks5lto36IwPMmlY4ZjSgwxtiR0MftyyfM+bX8d/8uYkZHgfXXj4tt6y1N8ivtzRx5+cXUeq05pY/8adGUmnF125cmHsDd3hDPPT7t/niynlcMb80t+2TW96hpTvI9/+pDsfxbLm7P8xPfruPjy/0cOs/zMld7vYf/9XMzkYvd37+MuZVZxO1nsFRNv12P2UuC3d+fjFWczZZeurVFl7d183nr53F6qXVaDQaegbC/NvvDwDwrc9eSk25g4xSPLu9lZd2dbJknpt//nQtFpOe7v4wD/+/BgKhOLf+w1w+uchDRin+uKOdF99sp6rMxoY1C6h02+j0hXhiyzv0DoyyrK6Sz14zE71Oy5Y327OXHFoN3LJ8DlfML6XDF+I/Xm6m3Rvikhkublkxh2KHmZff6uTPOzsAWL20mtVLq+kdjPD01haOdgepKCngs38/k9oaJ3/Z283LuzuIxdMsXVjGDZ+YQSiS4Pm/HaOpYxiH1cCqpdVctaCcHQ19/GVPF+Fo9llSn76qmlK3nadePkxDmx+jXssnFnn45CIPjceGeHVvN8HRBGVOC8vrKikvtrJ1Xw8Hjg6CBi6bVcLfL66gZyCcS65OJECzKgrZ1eil/nhiNMPj4JOLPISjSf52sJeBQAyDXkvdPDcLa1wcaPXzdssAqbSipNDMxy8pR6fTsvOQF+9QBI0mm3BdNruE1p4gb7cMkkhlKDDrWTK/FKfdxN7D/XQfT7iqS21cPteNbzg7ChhPZEeDLpnhYprbRkOrnw5f9j1S7DCxeI6bSCzFgaODROIpNBqYPa2QWRWFNHcFcslggVnPpTOL0Wo1HGzNJjqQve9ublURXf1hWnuDKAV6nZb51UU4Cow0HhvKPaPLaTcxv9qJPxjlaM8IGaXQaGCGx0Gp00JzZ3aSEsiO0M6vLiIaT3G0J5hLVMtdVipLbbT3jTAYjAHZ8mZPc6DRaDjaEzw+2gWFNiMzyh30+kfpH87eW6cBqspsFJgNtPWO5J4XZjHpmOFxMByK0+eP5N6bpU4L5cUFtHQNE42nj5enYXq5nVg8nXsmGWSfZ1bustIzEGY0lp2uX6OBaSW24+/TcG7k3GzUMa2kAN9wNNeWACWFZiwmPT0Do7l7//S67GQvgXBizH2Ddmt2xLN3cJT08c9dDVBebCWWSOfaEsBk1OEuNOMbjuba50QMAPhHTm6r02oodVrwj8RIJE9ua7MYMBt1uXY/UZ67yMJIJEEscfLZayaDjlKnZcyIY3Zc8V3G//MMVxmocf6azP7G75tOXX36/idXvk6nJZ3OnHa8E/XiZ3u84+3v1GM927aa6FgnPpb32dYTtN3Ex/MBx9pZnruzLV+Ij8KJH+enmiRDH7LzaepBpdQZf8U4Mc33qdtmlMpdhnVCMpXOjUC9e9tEMnPapVHReAqTQXfapU2hSGLMpVwAsUT2i5rZOPZ2tOFQnCKbcUy948k08UR6TLII4A/GKLQZx9wTlEpnCITilBy/1O2EoZEYVrN+THmZjGI4FMflMI0p78QXw3f/enHiMjF3oWXM8UViSeLJDE67aUx5vuEIRTYTJsPJNoolUgTDCcpc2YT1RKz0B6JYTfox5SVTGbxDEaa5C8acK38whlarGVNeOpOhu3+UipKCMZegBcJxYok05a6TCXJGKbp8YdxFFqzmk20RjiYZGolRVWrLtYVSip6BUWzWk5f4QfY89/pHmV5mH9P2ff7R419iT5aXSKbp7A9T6S4Y0/YDgSixRJpKd0GuvFQ6Q7s3RKnTkkvIIRsTQ6EYNeX2XHxmMooOXwi7xTDmXIciCfr8EaaX23Ntn1GK7v4wOm32C/2J8qLxFJ2+ENPctlzbK6Xo80eIJ7P35Z0oL55M0+ENUVJoHnO/2kAgSiAcP34Zpe74uUvT7g1htxopc1py5Q2H4viGIlSX2XNtn0pn6OoPo9VoqCq15WJrZDRBz0CYipICCm0m3G47Pt8I3QNhEqkM08tsufJGY0k6fWFKCs2UFGbvm8sohW8owshoguoye+6SxxPHbLMY8BQXoNVmf+AYCMYYDESpdNty77N4Mk2XL4xOp6HSbcOg16JU9j3TNxSh3GnNvXeSqQzdA2GSqQzVZbbcuQ6G43QPjuKymyhzWdFqNKTSGfr8EcKRBFVl9lzbh6NJunwhCiwGKkoK0Ou0ZDIK71CE4VCcipKC3GdDJJaie+B43UpsmIw6MkoxGIhm36dFFtxFltM+5/LF+dQPifPbZGNlwuRywh1MtPrsktdzMfE+3l8dzvc2eL/HX1xsY9AfPuf9azVgNU/9qBBIMvShk05ITJbEipgsiRVxNiRexGRJrIjJuphi5UN7zpAQQgghhBBCXIwkGRJCCCGEEELkJUmGhBBCCCGEEHlpUg9dPR+cevN+vtZBXBgkVsRkSayIsyHxIiZLYkVM1sUSK+d6HBfMBApCCCGEEEII8UGSy+SEEEIIIYQQeUmSISGEEEIIIURekmRICCGEEEIIkZckGRJCCCGEEELkJUmGhBBCCCGEEHlJkiEhhBBCCCFEXpJkSAghhBBCCJGXJBkSQgghhBBC5CVJhoQQQgghhBB5SZKhCRw7doybb76ZVatWcfPNN9Pe3j7VVRJTZHh4mNtvv51Vq1axdu1avvnNbzI0NATA22+/zQ033MCqVav4yle+gt/vz71uvHXi4vfwww8zb948jhw5AkisiDOLx+Pcc889rFy5krVr1/KDH/wAGL8Pkv4pP23bto1169Zx4403snbtWl555RVAYkXApk2bWLZs2Zg+B849NvImbpQY1/r169Xzzz+vlFLq+eefV+vXr5/iGompMjw8rHbt2pX7+4EHHlAbN25UmUxGrVixQu3Zs0cppdQjjzyi7rrrLqWUGneduPgdOnRI3Xbbberaa69Vzc3NEiviPd13333q/vvvV5lMRiml1MDAgFJq/D5I+qf8k8lk1JIlS1Rzc7NSSqmmpia1ePFilU6nJVaE2rNnj+rt7VXXXXddLkaUOvfPkXyJG0mGxjE4OKjq6upUKpVSSimVSqVUXV2d8vv9U1wzcT54+eWX1Ze//GV14MABdf311+eW+/1+tXjxYqWUGneduLjF43F10003qc7OzlzHJLEiziQcDqu6ujoVDofHLB+vD5L+KT9lMhl15ZVXqr179yqllHrrrbfUypUrJVbEGO9Ohs41NvIpbvRTPTJ1Puvr66OsrAydTgeATqejtLSUvr4+XC7XFNdOTKVMJsNTTz3FsmXL6Ovro6KiIrfO5XKRyWQIBALjrisqKpqKqouPyM9//nNuuOEGqqqqcsskVsSZdHV1UVRUxMMPP8zu3bspKCjgO9/5Dmaz+T37IKWU9E95SKPR8LOf/Yyvf/3rWK1WRkdHefzxx8f9viKxkt/ONTbyKW7kniEhzsF9992H1WrlS1/60lRXRZyH6uvraWho4NZbb53qqogLQCqVoquriwULFvDcc8/x3e9+l29961tEIpGprpo4z6RSKR5//HF++ctfsm3bNh599FHuvPNOiRUh3gcZGRqHx+PB5/ORTqfR6XSk02n6+/vxeDxTXTUxhTZt2kRHRwePPfYYWq0Wj8dDb29vbv3Q0BAajYaioqJx14mL1549e2hra2P58uUAeL1ebrvtNtavXy+xIk5TUVGBXq9nzZo1AFx22WU4nU7MZvN79kFKKemf8lBTUxP9/f3U1dUBUFdXh8ViwWQySayIMxrvu+x4sZFPcSMjQ+MoLi6mtraWLVu2ALBlyxZqa2svuuFBMXkPPfQQhw4d4pFHHsFoNAJwySWXEIvF2Lt3LwBPP/00n/rUpyZcJy5eX/3qV9mxYwdbt25l69atlJeX8+STT7JhwwaJFXEal8vF0qVLeeONN4DsDE5+v5+ampr37IOkf8pP5eXleL1e2traAGhtbWVwcJDp06dLrIgzGu/8n+u6i41GKaWmuhLns9bWVu666y5GRkZwOBxs2rSJmTNnTnW1xBRoaWlhzZo11NTUYDabAaisrOSRRx5h//793HPPPcTjcaZNm8aDDz5ISUkJwLjrRH5YtmwZjz32GHPnzpVYEWfU1dXF97//fQKBAHq9njvuuINrrrlm3D5I+qf89MILL/DEE0+g0WgA+Pa3v82KFSskVgQ//vGPeeWVVxgcHMTpdFJUVMSLL754zrGRL3EjyZAQQgghhBAiL8llckIIIYQQQoi8JMmQEEIIIYQQIi9JMiSEEEIIIYTIS5IMCSGEEEIIIfKSJENCCCGEEEKIvCTJkBBCCCGEECIvSTIkhBBCCCGEyEuSDAkhhBBCCCHykiRDQgghzlvLli3jySefZO3atdTV1XHHHXcQj8f51a9+xU033UQqlQLgd7/7Hddffz3xeHyKayyEEOJCIsmQEEKI89pLL73Er3/9a1577TWam5t57rnn2LBhAwaDgUcffZT29nYeeughHnzwQUwm01RXVwghxAVEP9UVEEIIIcazfv16ysrKALjuuutoampCq9WyadMmPvOZz/DnP/+ZDRs2sGDBgimuqRBCiAuNjAwJIYQ4r7nd7tz/LRYLkUgEgMrKSpYuXUpPTw9f/OIXp6p6QgghLmCSDAkhhLggbd++nfr6eq6++mp++tOfTnV1hBBCXIAkGRJCCHHBGRoa4u677+b+++/ngQceYOvWrWzfvn2qqyWEEOICI8mQEEKIC84Pf/hDli1bxjXXXIPT6eT+++/n7rvvZnh4eKqrJoQQ4gKiUUqpqa6EEEIIIYQQQnzUZGRICCGEEEIIkZckGRJCCCGEEELkJUmGhBBCCCGEEHlJkiEhhBBCCCFEXpJkSAghhBBCCJGXJBkSQgghhBBC5CVJhoQQQgghhBB5SZIhIYQQQgghRF6SZEgIIYQQQgiRl/4/PkImeef1mHAAAAAASUVORK5CYII=
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
"
>
</div>
@@ -14797,8 +15113,9 @@ mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.ld_st.bin.csv .
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>Did you expect more?</p>
-<p>The reason is simple: Among the load and store instructions counted by <code>PM_LD_CMPL</code> and <code>PM_ST_CMPL</code> are vector instructions which can load and store multiple (two) values at a time. To see how many <em>bytes</em> are loaded and stored, we need to measure counters for vectorized loads and stores as well.</p>
-<p><a name="task2-b"></a><strong>TASK B</strong>: Please measure counters for <em>vectorized</em> loads and <em>vectorized</em> stores. See the TODOs in <a href="/edit/Tasks/poisson2d.vld.c"><code>poisson2d.vld.c</code></a> and <a href="/edit/Tasks/poisson2d.vst.c"><code>poisson2d.vst.c</code></a> (<em>Note: These vector counters can not be measured together and need separate files and runs</em>). Can you find out the name of the counters yourself, using <code>papi_native_avail | grep VECTOR_</code>?</p>
+<p>The reason is simple: Among the load and store instructions counted by <code>PM_LD_CMPL</code> and <code>PM_ST_CMPL</code> are vector instructions which can load and store multiple (in this case: two) values at a time. To see how many <em>bytes</em> are loaded and stored, we need to measure counters for vectorized loads and stores as well.</p>
+<h3 id="TASK-B">TASK B<a class="anchor-link" href="#TASK-B">¶</a></h3><p><a name="task2-b"></a></p>
+<p>Please measure counters for <em>vectorized</em> loads and <em>vectorized</em> stores. See the TODOs in <a href="poisson2d.vld.c"><code>poisson2d.vld.c</code></a> and <a href="poisson2d.vst.c"><code>poisson2d.vst.c</code></a> (<em>Note: These vector counters can not be measured together and need separate files and runs</em>). Can you find out the name of the counters yourself, using <code>papi_native_avail | grep VECTOR_</code>?</p>
<p>Compile, test, and bench-run your program again.</p>
<p><a href="#toc">Back to top</a></p>
@@ -14807,7 +15124,7 @@ mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.ld_st.bin.csv .
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
-<div class="prompt input_prompt">In [2]:</div>
+<div class="prompt input_prompt">In [9]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">!</span>papi_native_avail <span class="p">|</span> grep VECTOR_
@@ -14827,9 +15144,9 @@ mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.ld_st.bin.csv .
<div class="output_subarea output_stream output_stdout output_text">
-<pre>| PM_VECTOR_FLOP_CMPL |
-| PM_VECTOR_LD_CMPL |
-| PM_VECTOR_ST_CMPL |
+<pre>| PM_VECTOR_FLOP_CMPL |
+| PM_VECTOR_LD_CMPL |
+| PM_VECTOR_ST_CMPL |
</pre>
</div>
</div>
@@ -14848,7 +15165,7 @@ mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.ld_st.bin.csv .
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
-<div class="prompt input_prompt">In [3]:</div>
+<div class="prompt input_prompt">In [1]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">!</span>make bench_task3
@@ -14868,8 +15185,8 @@ mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.ld_st.bin.csv .
<div class="output_subarea output_stream output_stdout output_text">
-<pre>bsub -W 60 -nnodes 1 -Is jsrun -n 1 -c 1 -g ALL_GPUS ./bench.sh poisson2d.vld.bin /gpfs/wolf/gen110/scratch/aherten//poisson2d.vld.bin.csv
-Job <4097> is submitted to default queue <batch>.
+<pre>bsub -W 60 -nnodes 1 -Is -P TRN003 jsrun -n 1 -c 1 -g ALL_GPUS ./bench.sh poisson2d.vld.bin /gpfs/wolf/trn003/scratch/aherten//poisson2d.vld.bin.csv
+Job <24641> is submitted to default queue <batch>.
<<Waiting for dispatch ...>>
<<Starting on login1>>
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
@@ -14879,9 +15196,9 @@ iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
200,32,12,0.0012,174000,870,870
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,16,0.0013,234000,1170,1170
+200,32,16,0.0012,234000,1170,1170
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,20,0.0014,294000,1470,1470
+200,32,20,0.0013,294000,1470,1470
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
200,32,24,0.0014,354000,1770,1770
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
@@ -14895,11 +15212,11 @@ iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
200,32,44,0.0017,654000,3270,3270
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,48,0.0017,714000,3570,3570
+200,32,48,0.0018,714000,3570,3570
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
200,32,52,0.0018,774000,3870,3870
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,56,0.0020,834000,4170,4170
+200,32,56,0.0019,834000,4170,4170
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
200,32,60,0.0020,894000,4470,4470
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
@@ -14909,117 +15226,117 @@ iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
200,32,72,0.0022,1074000,5370,5370
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,76,0.0023,1134000,5670,5670
+200,32,76,0.0022,1134000,5670,5670
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
200,32,80,0.0023,1194000,5970,5970
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,84,0.0023,1254000,6270,6270
+200,32,84,0.0024,1254000,6270,6270
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
200,32,88,0.0024,1314000,6570,6570
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
200,32,92,0.0025,1374000,6870,6870
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,96,0.0025,1434000,7170,7170
+200,32,96,0.0027,1434000,7170,7170
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
200,32,100,0.0026,1494000,7470,7470
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,104,0.0027,1554000,7770,7770
+200,32,104,0.0029,1554000,7770,7770
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
200,32,108,0.0027,1614000,8070,8070
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
200,32,112,0.0028,1674000,8370,8370
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,116,0.0028,1734000,8670,8670
+200,32,116,0.0029,1734000,8670,8670
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
200,32,120,0.0029,1794000,8970,8970
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
200,32,124,0.0030,1854000,9270,9270
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,128,0.0030,1914000,9570,9570
+200,32,128,0.0032,1914000,9570,9570
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
200,32,132,0.0031,1974000,9870,9870
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
200,32,136,0.0032,2034000,10170,10170
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,140,0.0032,2094000,10470,10470
+200,32,140,0.0033,2094000,10470,10470
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
200,32,144,0.0033,2154000,10770,10770
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
200,32,148,0.0034,2214000,11070,11070
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,152,0.0035,2274000,11370,11370
+200,32,152,0.0036,2274000,11370,11370
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
200,32,156,0.0035,2334000,11670,11670
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
200,32,160,0.0036,2394000,11970,11970
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,164,0.0036,2454000,12270,12270
+200,32,164,0.0037,2454000,12270,12270
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
200,32,168,0.0037,2514000,12570,12570
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,172,0.0037,2574000,12870,12870
+200,32,172,0.0038,2574000,12870,12870
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,176,0.0038,2634000,13170,13170
+200,32,176,0.0039,2634000,13170,13170
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
200,32,180,0.0039,2694000,13470,13470
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,184,0.0041,2754000,13770,13770
+200,32,184,0.0040,2754000,13770,13770
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,188,0.0040,2814000,14070,14070
+200,32,188,0.0041,2814000,14070,14070
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
200,32,192,0.0041,2874000,14370,14370
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,196,0.0041,2934000,14670,14670
+200,32,196,0.0042,2934000,14670,14670
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
200,32,200,0.0042,2994000,14970,14970
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
200,32,204,0.0043,3054000,15270,15270
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,208,0.0044,3114000,15570,15570
+200,32,208,0.0045,3114000,15570,15570
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,212,0.0044,3174000,15870,15870
+200,32,212,0.0045,3174000,15870,15870
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,216,0.0044,3234000,16170,16170
+200,32,216,0.0045,3234000,16170,16170
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,220,0.0045,3294000,16470,16470
+200,32,220,0.0046,3294000,16470,16470
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,224,0.0046,3354000,16770,16770
+200,32,224,0.0048,3354000,16770,16770
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
200,32,228,0.0047,3414000,17070,17070
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,232,0.0047,3474000,17370,17370
+200,32,232,0.0048,3474000,17370,17370
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
200,32,236,0.0048,3534000,17670,17670
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,240,0.0048,3594000,17970,17970
+200,32,240,0.0049,3594000,17970,17970
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,244,0.0049,3654000,18270,18270
+200,32,244,0.0050,3654000,18270,18270
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,248,0.0049,3714000,18570,18570
+200,32,248,0.0052,3714000,18570,18570
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,252,0.0050,3774000,18870,18870
+200,32,252,0.0051,3774000,18870,18870
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,256,0.0051,3834000,19170,19170
+200,32,256,0.0052,3834000,19170,19170
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
200,32,260,0.0052,3894000,19470,19470
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,264,0.0052,3954000,19770,19770
+200,32,264,0.0053,3954000,19770,19770
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,268,0.0053,4014000,20070,20070
+200,32,268,0.0054,4014000,20070,20070
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,272,0.0053,4074000,20370,20370
+200,32,272,0.0054,4074000,20370,20370
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
200,32,276,0.0055,4134000,20670,20670
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,280,0.0055,4194000,20970,20970
+200,32,280,0.0056,4194000,20970,20970
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,284,0.0055,4254000,21270,21270
+200,32,284,0.0056,4254000,21270,21270
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
200,32,288,0.0057,4314000,21570,21570
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,292,0.0056,4374000,21870,21870
+200,32,292,0.0058,4374000,21870,21870
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,296,0.0057,4434000,22170,22170
+200,32,296,0.0058,4434000,22170,22170
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
200,32,300,0.0059,4494000,22470,22470
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
@@ -15027,366 +15344,366 @@ iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
200,32,308,0.0060,4614000,23070,23070
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,312,0.0060,4674000,23370,23370
+200,32,312,0.0061,4674000,23370,23370
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,316,0.0061,4734000,23670,23670
+200,32,316,0.0062,4734000,23670,23670
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,320,0.0061,4794000,23970,23970
+200,32,320,0.0062,4794000,23970,23970
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,324,0.0062,4854000,24270,24270
+200,32,324,0.0063,4854000,24270,24270
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,328,0.0062,4914000,24570,24570
+200,32,328,0.0063,4914000,24570,24570
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,332,0.0063,4974000,24870,24870
+200,32,332,0.0064,4974000,24870,24870
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,336,0.0063,5034000,25170,25170
+200,32,336,0.0065,5034000,25170,25170
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,340,0.0066,5094000,25470,25470
+200,32,340,0.0065,5094000,25470,25470
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,344,0.0065,5154000,25770,25770
+200,32,344,0.0066,5154000,25770,25770
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,348,0.0067,5214000,26070,26070
+200,32,348,0.0069,5214000,26070,26070
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
200,32,352,0.0068,5274000,26370,26370
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,356,0.0067,5334000,26670,26670
+200,32,356,0.0070,5334000,26670,26670
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,360,0.0067,5394000,26970,26970
+200,32,360,0.0069,5394000,26970,26970
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,364,0.0068,5454000,27270,27270
+200,32,364,0.0070,5454000,27270,27270
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,368,0.0069,5514000,27570,27570
+200,32,368,0.0070,5514000,27570,27570
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,372,0.0069,5574000,27870,27870
+200,32,372,0.0071,5574000,27870,27870
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,376,0.0070,5634000,28170,28170
+200,32,376,0.0073,5634000,28170,28170
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,380,0.0071,5694000,28470,28470
+200,32,380,0.0073,5694000,28470,28470
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,384,0.0071,5754000,28770,28770
+200,32,384,0.0073,5754000,28770,28770
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,388,0.0073,5814000,29070,29070
+200,32,388,0.0074,5814000,29070,29070
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
200,32,392,0.0074,5874000,29370,29370
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,396,0.0073,5934000,29670,29670
+200,32,396,0.0076,5934000,29670,29670
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,400,0.0074,5994000,29970,29970
+200,32,400,0.0075,5994000,29970,29970
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,404,0.0074,6054000,30270,30270
+200,32,404,0.0076,6054000,30270,30270
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,408,0.0075,6114000,30570,30570
+200,32,408,0.0077,6114000,30570,30570
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,412,0.0076,6174000,30870,30870
+200,32,412,0.0078,6174000,30870,30870
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,416,0.0076,6234000,31170,31170
+200,32,416,0.0079,6234000,31170,31170
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,420,0.0080,6294000,31470,31470
+200,32,420,0.0079,6294000,31470,31470
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
200,32,424,0.0079,6354000,31770,31770
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,428,0.0078,6414000,32070,32070
+200,32,428,0.0080,6414000,32070,32070
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,432,0.0079,6474000,32370,32370
+200,32,432,0.0080,6474000,32370,32370
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,436,0.0080,6534000,32670,32670
+200,32,436,0.0081,6534000,32670,32670
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,440,0.0080,6594000,32970,32970
+200,32,440,0.0082,6594000,32970,32970
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
200,32,444,0.0083,6654000,33270,33270
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,448,0.0082,6714000,33570,33570
+200,32,448,0.0084,6714000,33570,33570
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,452,0.0082,6774000,33870,33870
+200,32,452,0.0084,6774000,33870,33870
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,456,0.0083,6834000,34170,34170
+200,32,456,0.0084,6834000,34170,34170
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,460,0.0086,6894000,34470,34470
+200,32,460,0.0085,6894000,34470,34470
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,464,0.0084,6954000,34770,34770
+200,32,464,0.0086,6954000,34770,34770
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,468,0.0085,7014000,35070,35070
+200,32,468,0.0087,7014000,35070,35070
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,472,0.0086,7074000,35370,35370
+200,32,472,0.0088,7074000,35370,35370
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,476,0.0086,7134000,35670,35670
+200,32,476,0.0088,7134000,35670,35670
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,480,0.0087,7194000,35970,35970
+200,32,480,0.0089,7194000,35970,35970
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,484,0.0088,7254000,36270,36270
+200,32,484,0.0090,7254000,36270,36270
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,488,0.0088,7314000,36570,36570
+200,32,488,0.0091,7314000,36570,36570
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,492,0.0089,7374000,36870,36870
+200,32,492,0.0091,7374000,36870,36870
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
200,32,496,0.0091,7434000,37170,37170
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,500,0.0092,7494000,37470,37470
+200,32,500,0.0094,7494000,37470,37470
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,504,0.0091,7554000,37770,37770
+200,32,504,0.0093,7554000,37770,37770
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,508,0.0092,7614000,38070,38070
+200,32,508,0.0095,7614000,38070,38070
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,512,0.0092,7674000,38370,38370
+200,32,512,0.0096,7674000,38370,38370
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,516,0.0093,7734000,38670,38670
+200,32,516,0.0095,7734000,38670,38670
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,520,0.0093,7794000,38970,38970
+200,32,520,0.0095,7794000,38970,38970
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,524,0.0094,7854000,39270,39270
+200,32,524,0.0097,7854000,39270,39270
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
200,32,528,0.0097,7914000,39570,39570
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,532,0.0095,7974000,39870,39870
+200,32,532,0.0098,7974000,39870,39870
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,536,0.0096,8034000,40170,40170
+200,32,536,0.0098,8034000,40170,40170
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,540,0.0097,8094000,40470,40470
+200,32,540,0.0099,8094000,40470,40470
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,544,0.0097,8154000,40770,40770
+200,32,544,0.0100,8154000,40770,40770
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,548,0.0099,8214000,41070,41070
+200,32,548,0.0101,8214000,41070,41070
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,552,0.0099,8274000,41370,41370
+200,32,552,0.0101,8274000,41370,41370
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,556,0.0100,8334000,41670,41670
+200,32,556,0.0104,8334000,41670,41670
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,560,0.0100,8394000,41970,41970
+200,32,560,0.0103,8394000,41970,41970
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,564,0.0101,8454000,42270,42270
+200,32,564,0.0103,8454000,42270,42270
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,568,0.0102,8514000,42570,42570
+200,32,568,0.0106,8514000,42570,42570
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,572,0.0103,8574000,42870,42870
+200,32,572,0.0105,8574000,42870,42870
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,576,0.0103,8634000,43170,43170
+200,32,576,0.0106,8634000,43170,43170
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,580,0.0104,8694000,43470,43470
+200,32,580,0.0108,8694000,43470,43470
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,584,0.0104,8754000,43770,43770
+200,32,584,0.0109,8754000,43770,43770
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,588,0.0106,8814000,44070,44070
+200,32,588,0.0108,8814000,44070,44070
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,592,0.0106,8874000,44370,44370
+200,32,592,0.0109,8874000,44370,44370
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,596,0.0107,8934000,44670,44670
+200,32,596,0.0109,8934000,44670,44670
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,600,0.0107,8994000,44970,44970
+200,32,600,0.0110,8994000,44970,44970
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,604,0.0109,9054000,45270,45270
+200,32,604,0.0111,9054000,45270,45270
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,608,0.0109,9114000,45570,45570
+200,32,608,0.0112,9114000,45570,45570
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,612,0.0110,9174000,45870,45870
+200,32,612,0.0112,9174000,45870,45870
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,616,0.0110,9234000,46170,46170
+200,32,616,0.0114,9234000,46170,46170
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,620,0.0111,9294000,46470,46470
+200,32,620,0.0113,9294000,46470,46470
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,624,0.0112,9354000,46770,46770
+200,32,624,0.0114,9354000,46770,46770
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,628,0.0112,9414000,47070,47070
+200,32,628,0.0117,9414000,47070,47070
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,632,0.0113,9474000,47370,47370
+200,32,632,0.0116,9474000,47370,47370
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,636,0.0114,9534000,47670,47670
+200,32,636,0.0116,9534000,47670,47670
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,640,0.0115,9594000,47970,47970
+200,32,640,0.0117,9594000,47970,47970
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,644,0.0115,9654000,48270,48270
+200,32,644,0.0119,9654000,48270,48270
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,648,0.0115,9714000,48570,48570
+200,32,648,0.0118,9714000,48570,48570
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,652,0.0116,9774000,48870,48870
+200,32,652,0.0119,9774000,48870,48870
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,656,0.0118,9834000,49170,49170
+200,32,656,0.0119,9834000,49170,49170
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,660,0.0117,9894000,49470,49470
+200,32,660,0.0121,9894000,49470,49470
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,664,0.0118,9954000,49770,49770
+200,32,664,0.0122,9954000,49770,49770
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,668,0.0118,10014000,50070,50070
+200,32,668,0.0123,10014000,50070,50070
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,672,0.0120,10074000,50370,50370
+200,32,672,0.0122,10074000,50370,50370
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,676,0.0121,10134000,50670,50670
+200,32,676,0.0123,10134000,50670,50670
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,680,0.0120,10194000,50970,50970
+200,32,680,0.0123,10194000,50970,50970
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,684,0.0121,10254000,51270,51270
+200,32,684,0.0125,10254000,51270,51270
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,688,0.0123,10314000,51570,51570
+200,32,688,0.0125,10314000,51570,51570
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,692,0.0122,10374000,51870,51870
+200,32,692,0.0127,10374000,51870,51870
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,696,0.0123,10434000,52170,52170
+200,32,696,0.0126,10434000,52170,52170
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,700,0.0124,10494000,52470,52470
+200,32,700,0.0127,10494000,52470,52470
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,704,0.0124,10554000,52770,52770
+200,32,704,0.0128,10554000,52770,52770
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,708,0.0125,10614000,53070,53070
+200,32,708,0.0129,10614000,53070,53070
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,712,0.0126,10674000,53370,53370
+200,32,712,0.0128,10674000,53370,53370
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,716,0.0126,10734000,53670,53670
+200,32,716,0.0131,10734000,53670,53670
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,720,0.0126,10794000,53970,53970
+200,32,720,0.0130,10794000,53970,53970
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,724,0.0128,10854000,54270,54270
+200,32,724,0.0130,10854000,54270,54270
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,728,0.0128,10914000,54570,54570
+200,32,728,0.0132,10914000,54570,54570
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,732,0.0129,10974000,54870,54870
+200,32,732,0.0133,10974000,54870,54870
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,736,0.0130,11034000,55170,55170
+200,32,736,0.0135,11034000,55170,55170
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,740,0.0130,11094000,55470,55470
+200,32,740,0.0135,11094000,55470,55470
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,744,0.0130,11154000,55770,55770
+200,32,744,0.0135,11154000,55770,55770
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,748,0.0131,11214000,56070,56070
+200,32,748,0.0134,11214000,56070,56070
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,752,0.0132,11274000,56370,56370
+200,32,752,0.0135,11274000,56370,56370
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,756,0.0133,11334000,56670,56670
+200,32,756,0.0136,11334000,56670,56670
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,760,0.0134,11394000,56970,56970
+200,32,760,0.0137,11394000,56970,56970
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,764,0.0134,11454000,57270,57270
+200,32,764,0.0137,11454000,57270,57270
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,768,0.0135,11514000,57570,57570
+200,32,768,0.0138,11514000,57570,57570
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,772,0.0135,11574000,57870,57870
+200,32,772,0.0139,11574000,57870,57870
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,776,0.0136,11634000,58170,58170
+200,32,776,0.0141,11634000,58170,58170
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,780,0.0138,11694000,58470,58470
+200,32,780,0.0140,11694000,58470,58470
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,784,0.0138,11754000,58770,58770
+200,32,784,0.0142,11754000,58770,58770
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,788,0.0139,11814000,59070,59070
+200,32,788,0.0141,11814000,59070,59070
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,792,0.0139,11874000,59370,59370
+200,32,792,0.0142,11874000,59370,59370
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,796,0.0141,11934000,59670,59670
+200,32,796,0.0143,11934000,59670,59670
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,800,0.0140,11994000,59970,59970
+200,32,800,0.0143,11994000,59970,59970
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,804,0.0141,12054000,60270,60270
+200,32,804,0.0145,12054000,60270,60270
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,808,0.0142,12114000,60570,60570
+200,32,808,0.0145,12114000,60570,60570
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,812,0.0143,12174000,60870,60870
+200,32,812,0.0145,12174000,60870,60870
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,816,0.0143,12234000,61170,61170
+200,32,816,0.0148,12234000,61170,61170
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,820,0.0143,12294000,61470,61470
+200,32,820,0.0148,12294000,61470,61470
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,824,0.0144,12354000,61770,61770
+200,32,824,0.0148,12354000,61770,61770
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,828,0.0145,12414000,62070,62070
+200,32,828,0.0148,12414000,62070,62070
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,832,0.0145,12474000,62370,62370
+200,32,832,0.0149,12474000,62370,62370
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,836,0.0146,12534000,62670,62670
+200,32,836,0.0150,12534000,62670,62670
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,840,0.0146,12594000,62970,62970
+200,32,840,0.0150,12594000,62970,62970
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,844,0.0147,12654000,63270,63270
+200,32,844,0.0151,12654000,63270,63270
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,848,0.0148,12714000,63570,63570
+200,32,848,0.0153,12714000,63570,63570
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,852,0.0149,12774000,63870,63870
+200,32,852,0.0153,12774000,63870,63870
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,856,0.0150,12834000,64170,64170
+200,32,856,0.0153,12834000,64170,64170
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,860,0.0150,12894000,64470,64470
+200,32,860,0.0154,12894000,64470,64470
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,864,0.0151,12954000,64770,64770
+200,32,864,0.0154,12954000,64770,64770
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,868,0.0152,13014000,65070,65070
+200,32,868,0.0155,13014000,65070,65070
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,872,0.0151,13074000,65370,65370
+200,32,872,0.0157,13074000,65370,65370
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,876,0.0152,13134000,65670,65670
+200,32,876,0.0156,13134000,65670,65670
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,880,0.0154,13194000,65970,65970
+200,32,880,0.0157,13194000,65970,65970
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,884,0.0154,13254000,66270,66270
+200,32,884,0.0157,13254000,66270,66270
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,888,0.0154,13314000,66570,66570
+200,32,888,0.0158,13314000,66570,66570
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,892,0.0155,13374000,66870,66870
+200,32,892,0.0159,13374000,66870,66870
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,896,0.0156,13434000,67170,67170
+200,32,896,0.0160,13434000,67170,67170
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,900,0.0158,13494000,67470,67470
+200,32,900,0.0160,13494000,67470,67470
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,904,0.0158,13554000,67770,67770
+200,32,904,0.0162,13554000,67770,67770
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,908,0.0159,13614000,68070,68070
+200,32,908,0.0162,13614000,68070,68070
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,912,0.0161,13674000,68370,68370
+200,32,912,0.0163,13674000,68370,68370
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,916,0.0162,13734000,68670,68670
+200,32,916,0.0163,13734000,68670,68670
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,920,0.0162,13794000,68970,68970
+200,32,920,0.0164,13794000,68970,68970
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,924,0.0163,13854000,69270,69270
+200,32,924,0.0165,13854000,69270,69270
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,928,0.0162,13914000,69570,69570
+200,32,928,0.0166,13914000,69570,69570
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,932,0.0164,13974000,69870,69870
+200,32,932,0.0166,13974000,69870,69870
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,936,0.0163,14034000,70170,70170
+200,32,936,0.0167,14034000,70170,70170
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,940,0.0164,14094000,70470,70470
+200,32,940,0.0167,14094000,70470,70470
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,944,0.0165,14154000,70770,70770
+200,32,944,0.0168,14154000,70770,70770
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,948,0.0166,14214000,71070,71070
+200,32,948,0.0170,14214000,71070,71070
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,952,0.0166,14274000,71370,71370
+200,32,952,0.0171,14274000,71370,71370
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,956,0.0170,14334000,71670,71670
+200,32,956,0.0171,14334000,71670,71670
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,960,0.0168,14394000,71970,71970
+200,32,960,0.0171,14394000,71970,71970
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,964,0.0174,14454000,72270,72270
+200,32,964,0.0175,14454000,72270,72270
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,968,0.0172,14514000,72570,72570
+200,32,968,0.0176,14514000,72570,72570
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,972,0.0173,14574000,72870,72870
+200,32,972,0.0176,14574000,72870,72870
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,976,0.0173,14634000,73170,73170
+200,32,976,0.0175,14634000,73170,73170
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,980,0.0175,14694000,73470,73470
+200,32,980,0.0178,14694000,73470,73470
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,984,0.0175,14754000,73770,73770
+200,32,984,0.0180,14754000,73770,73770
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,988,0.0176,14814000,74070,74070
+200,32,988,0.0178,14814000,74070,74070
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,992,0.0176,14874000,74370,74370
+200,32,992,0.0179,14874000,74370,74370
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,996,0.0178,14934000,74670,74670
+200,32,996,0.0181,14934000,74670,74670
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,1000,0.0179,14994000,74970,74970
+200,32,1000,0.0180,14994000,74970,74970
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,1004,0.0178,15054000,75270,75270
+200,32,1004,0.0182,15054000,75270,75270
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,1008,0.0179,15114000,75570,75570
+200,32,1008,0.0181,15114000,75570,75570
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,1012,0.0179,15174000,75870,75870
+200,32,1012,0.0183,15174000,75870,75870
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,1016,0.0181,15234000,76170,76170
+200,32,1016,0.0183,15234000,76170,76170
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,1020,0.0181,15294000,76470,76470
+200,32,1020,0.0186,15294000,76470,76470
iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)
-200,32,1024,0.0179,15354000,76770,76770
-mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.vld.bin.csv .
-bsub -W 60 -nnodes 1 -Is jsrun -n 1 -c 1 -g ALL_GPUS ./bench.sh poisson2d.vst.bin /gpfs/wolf/gen110/scratch/aherten//poisson2d.vst.bin.csv
-Job <4098> is submitted to default queue <batch>.
+200,32,1024,0.0182,15354000,76770,76770
+mv /gpfs/wolf/trn003/scratch/aherten//poisson2d.vld.bin.csv .
+bsub -W 60 -nnodes 1 -Is -P TRN003 jsrun -n 1 -c 1 -g ALL_GPUS ./bench.sh poisson2d.vst.bin /gpfs/wolf/trn003/scratch/aherten//poisson2d.vst.bin.csv
+Job <24642> is submitted to default queue <batch>.
<<Waiting for dispatch ...>>
<<Starting on login1>>
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
@@ -15400,11 +15717,11 @@ iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
200,32,20,0.0013,54200,271,271
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,24,0.0014,66200,331,331
+200,32,24,0.0013,66200,331,331
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
200,32,28,0.0014,78200,391,391
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,32,0.0016,90200,451,451
+200,32,32,0.0015,90200,451,451
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
200,32,36,0.0015,102200,511,511
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
@@ -15420,109 +15737,109 @@ iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
200,32,60,0.0020,174200,871,871
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,64,0.0022,186200,931,931
+200,32,64,0.0020,186200,931,931
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
200,32,68,0.0022,198200,991,991
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,72,0.0021,210200,1051,1051
+200,32,72,0.0023,210200,1051,1051
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,76,0.0023,222200,1111,1111
+200,32,76,0.0022,222200,1111,1111
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
200,32,80,0.0023,234200,1171,1171
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,84,0.0023,246200,1231,1231
+200,32,84,0.0024,246200,1231,1231
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
200,32,88,0.0024,258200,1291,1291
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
200,32,92,0.0025,270200,1351,1351
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,96,0.0027,282200,1411,1411
+200,32,96,0.0025,282200,1411,1411
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
200,32,100,0.0026,294200,1471,1471
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
200,32,104,0.0027,306200,1531,1531
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,108,0.0027,318200,1591,1591
+200,32,108,0.0028,318200,1591,1591
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
200,32,112,0.0028,330200,1651,1651
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,116,0.0028,342200,1711,1711
+200,32,116,0.0029,342200,1711,1711
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
200,32,120,0.0030,354200,1771,1771
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
200,32,124,0.0030,366200,1831,1831
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,128,0.0030,378200,1891,1891
+200,32,128,0.0031,378200,1891,1891
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
200,32,132,0.0032,390200,1951,1951
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
200,32,136,0.0032,402200,2011,2011
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,140,0.0032,414200,2071,2071
+200,32,140,0.0033,414200,2071,2071
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
200,32,144,0.0033,426200,2131,2131
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,148,0.0033,438200,2191,2191
+200,32,148,0.0035,438200,2191,2191
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,152,0.0034,450200,2251,2251
+200,32,152,0.0035,450200,2251,2251
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
200,32,156,0.0035,462200,2311,2311
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
200,32,160,0.0036,474200,2371,2371
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,164,0.0036,486200,2431,2431
+200,32,164,0.0038,486200,2431,2431
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
200,32,168,0.0037,498200,2491,2491
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,172,0.0037,510200,2551,2551
+200,32,172,0.0038,510200,2551,2551
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,176,0.0039,522200,2611,2611
+200,32,176,0.0038,522200,2611,2611
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
200,32,180,0.0039,534200,2671,2671
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,184,0.0039,546200,2731,2731
+200,32,184,0.0040,546200,2731,2731
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
200,32,188,0.0040,558200,2791,2791
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,192,0.0040,570200,2851,2851
+200,32,192,0.0041,570200,2851,2851
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,196,0.0041,582200,2911,2911
+200,32,196,0.0042,582200,2911,2911
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,200,0.0042,594200,2971,2971
+200,32,200,0.0044,594200,2971,2971
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,204,0.0042,606200,3031,3031
+200,32,204,0.0043,606200,3031,3031
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,208,0.0043,618200,3091,3091
+200,32,208,0.0044,618200,3091,3091
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
200,32,212,0.0044,630200,3151,3151
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,216,0.0044,642200,3211,3211
+200,32,216,0.0045,642200,3211,3211
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
200,32,220,0.0046,654200,3271,3271
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
200,32,224,0.0046,666200,3331,3331
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,228,0.0046,678200,3391,3391
+200,32,228,0.0047,678200,3391,3391
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,232,0.0047,690200,3451,3451
+200,32,232,0.0048,690200,3451,3451
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,236,0.0047,702200,3511,3511
+200,32,236,0.0048,702200,3511,3511
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,240,0.0048,714200,3571,3571
+200,32,240,0.0049,714200,3571,3571
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,244,0.0049,726200,3631,3631
+200,32,244,0.0050,726200,3631,3631
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,248,0.0049,738200,3691,3691
+200,32,248,0.0050,738200,3691,3691
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,252,0.0050,750200,3751,3751
+200,32,252,0.0051,750200,3751,3751
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,256,0.0051,762200,3811,3811
+200,32,256,0.0052,762200,3811,3811
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,260,0.0051,774200,3871,3871
+200,32,260,0.0052,774200,3871,3871
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
200,32,264,0.0053,786200,3931,3931
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,268,0.0053,798200,3991,3991
+200,32,268,0.0054,798200,3991,3991
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
200,32,272,0.0054,810200,4051,4051
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
@@ -15530,378 +15847,378 @@ iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
200,32,280,0.0055,834200,4171,4171
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,284,0.0055,846200,4231,4231
+200,32,284,0.0056,846200,4231,4231
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,288,0.0056,858200,4291,4291
+200,32,288,0.0057,858200,4291,4291
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
200,32,292,0.0057,870200,4351,4351
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,296,0.0057,882200,4411,4411
+200,32,296,0.0058,882200,4411,4411
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,300,0.0058,894200,4471,4471
+200,32,300,0.0059,894200,4471,4471
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,304,0.0058,906200,4531,4531
+200,32,304,0.0059,906200,4531,4531
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,308,0.0059,918200,4591,4591
+200,32,308,0.0060,918200,4591,4591
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,312,0.0060,930200,4651,4651
+200,32,312,0.0061,930200,4651,4651
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,316,0.0060,942200,4711,4711
+200,32,316,0.0061,942200,4711,4711
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,320,0.0061,954200,4771,4771
+200,32,320,0.0062,954200,4771,4771
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,324,0.0061,966200,4831,4831
+200,32,324,0.0063,966200,4831,4831
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,328,0.0062,978200,4891,4891
+200,32,328,0.0063,978200,4891,4891
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,332,0.0063,990200,4951,4951
+200,32,332,0.0064,990200,4951,4951
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,336,0.0063,1002200,5011,5011
+200,32,336,0.0065,1002200,5011,5011
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,340,0.0064,1014200,5071,5071
+200,32,340,0.0066,1014200,5071,5071
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,344,0.0065,1026200,5131,5131
+200,32,344,0.0066,1026200,5131,5131
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,348,0.0066,1038200,5191,5191
+200,32,348,0.0067,1038200,5191,5191
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,352,0.0066,1050200,5251,5251
+200,32,352,0.0069,1050200,5251,5251
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,356,0.0067,1062200,5311,5311
+200,32,356,0.0068,1062200,5311,5311
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,360,0.0067,1074200,5371,5371
+200,32,360,0.0068,1074200,5371,5371
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,364,0.0068,1086200,5431,5431
+200,32,364,0.0069,1086200,5431,5431
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,368,0.0068,1098200,5491,5491
+200,32,368,0.0070,1098200,5491,5491
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,372,0.0069,1110200,5551,5551
+200,32,372,0.0071,1110200,5551,5551
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,376,0.0070,1122200,5611,5611
+200,32,376,0.0071,1122200,5611,5611
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,380,0.0071,1134200,5671,5671
+200,32,380,0.0072,1134200,5671,5671
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,384,0.0072,1146200,5731,5731
+200,32,384,0.0073,1146200,5731,5731
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,388,0.0072,1158200,5791,5791
+200,32,388,0.0073,1158200,5791,5791
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,392,0.0072,1170200,5851,5851
+200,32,392,0.0074,1170200,5851,5851
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,396,0.0073,1182200,5911,5911
+200,32,396,0.0075,1182200,5911,5911
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,400,0.0074,1194200,5971,5971
+200,32,400,0.0075,1194200,5971,5971
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,404,0.0074,1206200,6031,6031
+200,32,404,0.0076,1206200,6031,6031
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,408,0.0076,1218200,6091,6091
+200,32,408,0.0077,1218200,6091,6091
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,412,0.0076,1230200,6151,6151
+200,32,412,0.0077,1230200,6151,6151
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,416,0.0077,1242200,6211,6211
+200,32,416,0.0080,1242200,6211,6211
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,420,0.0077,1254200,6271,6271
+200,32,420,0.0078,1254200,6271,6271
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,424,0.0078,1266200,6331,6331
+200,32,424,0.0079,1266200,6331,6331
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,428,0.0078,1278200,6391,6391
+200,32,428,0.0080,1278200,6391,6391
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,432,0.0080,1290200,6451,6451
+200,32,432,0.0081,1290200,6451,6451
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,436,0.0079,1302200,6511,6511
+200,32,436,0.0082,1302200,6511,6511
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,440,0.0081,1314200,6571,6571
+200,32,440,0.0082,1314200,6571,6571
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,444,0.0081,1326200,6631,6631
+200,32,444,0.0083,1326200,6631,6631
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,448,0.0082,1338200,6691,6691
+200,32,448,0.0083,1338200,6691,6691
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,452,0.0082,1350200,6751,6751
+200,32,452,0.0084,1350200,6751,6751
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,456,0.0084,1362200,6811,6811
+200,32,456,0.0085,1362200,6811,6811
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,460,0.0084,1374200,6871,6871
+200,32,460,0.0085,1374200,6871,6871
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,464,0.0084,1386200,6931,6931
+200,32,464,0.0087,1386200,6931,6931
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,468,0.0085,1398200,6991,6991
+200,32,468,0.0086,1398200,6991,6991
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,472,0.0085,1410200,7051,7051
+200,32,472,0.0087,1410200,7051,7051
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,476,0.0086,1422200,7111,7111
+200,32,476,0.0088,1422200,7111,7111
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,480,0.0087,1434200,7171,7171
+200,32,480,0.0090,1434200,7171,7171
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,484,0.0088,1446200,7231,7231
+200,32,484,0.0089,1446200,7231,7231
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,488,0.0088,1458200,7291,7291
+200,32,488,0.0090,1458200,7291,7291
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,492,0.0089,1470200,7351,7351
+200,32,492,0.0092,1470200,7351,7351
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,496,0.0089,1482200,7411,7411
+200,32,496,0.0092,1482200,7411,7411
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,500,0.0090,1494200,7471,7471
+200,32,500,0.0092,1494200,7471,7471
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,504,0.0092,1506200,7531,7531
+200,32,504,0.0093,1506200,7531,7531
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,508,0.0093,1518200,7591,7591
+200,32,508,0.0094,1518200,7591,7591
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,512,0.0092,1530200,7651,7651
+200,32,512,0.0095,1530200,7651,7651
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,516,0.0093,1542200,7711,7711
+200,32,516,0.0096,1542200,7711,7711
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,520,0.0094,1554200,7771,7771
+200,32,520,0.0096,1554200,7771,7771
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,524,0.0094,1566200,7831,7831
+200,32,524,0.0096,1566200,7831,7831
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,528,0.0094,1578200,7891,7891
+200,32,528,0.0097,1578200,7891,7891
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
200,32,532,0.0097,1590200,7951,7951
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,536,0.0096,1602200,8011,8011
+200,32,536,0.0098,1602200,8011,8011
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,540,0.0097,1614200,8071,8071
+200,32,540,0.0100,1614200,8071,8071
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,544,0.0097,1626200,8131,8131
+200,32,544,0.0099,1626200,8131,8131
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,548,0.0099,1638200,8191,8191
+200,32,548,0.0100,1638200,8191,8191
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,552,0.0099,1650200,8251,8251
+200,32,552,0.0101,1650200,8251,8251
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,556,0.0101,1662200,8311,8311
+200,32,556,0.0102,1662200,8311,8311
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,560,0.0100,1674200,8371,8371
+200,32,560,0.0102,1674200,8371,8371
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,564,0.0101,1686200,8431,8431
+200,32,564,0.0105,1686200,8431,8431
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,568,0.0102,1698200,8491,8491
+200,32,568,0.0104,1698200,8491,8491
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,572,0.0103,1710200,8551,8551
+200,32,572,0.0105,1710200,8551,8551
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,576,0.0103,1722200,8611,8611
+200,32,576,0.0105,1722200,8611,8611
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,580,0.0104,1734200,8671,8671
+200,32,580,0.0108,1734200,8671,8671
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,584,0.0104,1746200,8731,8731
+200,32,584,0.0108,1746200,8731,8731
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,588,0.0105,1758200,8791,8791
+200,32,588,0.0109,1758200,8791,8791
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,592,0.0107,1770200,8851,8851
+200,32,592,0.0109,1770200,8851,8851
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,596,0.0108,1782200,8911,8911
+200,32,596,0.0109,1782200,8911,8911
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,600,0.0107,1794200,8971,8971
+200,32,600,0.0111,1794200,8971,8971
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,604,0.0109,1806200,9031,9031
+200,32,604,0.0111,1806200,9031,9031
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,608,0.0109,1818200,9091,9091
+200,32,608,0.0112,1818200,9091,9091
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,612,0.0109,1830200,9151,9151
+200,32,612,0.0112,1830200,9151,9151
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,616,0.0110,1842200,9211,9211
+200,32,616,0.0114,1842200,9211,9211
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,620,0.0111,1854200,9271,9271
+200,32,620,0.0113,1854200,9271,9271
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,624,0.0112,1866200,9331,9331
+200,32,624,0.0114,1866200,9331,9331
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,628,0.0111,1878200,9391,9391
+200,32,628,0.0114,1878200,9391,9391
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,632,0.0112,1890200,9451,9451
+200,32,632,0.0116,1890200,9451,9451
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,636,0.0113,1902200,9511,9511
+200,32,636,0.0116,1902200,9511,9511
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,640,0.0116,1914200,9571,9571
+200,32,640,0.0117,1914200,9571,9571
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,644,0.0114,1926200,9631,9631
+200,32,644,0.0118,1926200,9631,9631
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,648,0.0115,1938200,9691,9691
+200,32,648,0.0118,1938200,9691,9691
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,652,0.0117,1950200,9751,9751
+200,32,652,0.0121,1950200,9751,9751
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,656,0.0117,1962200,9811,9811
+200,32,656,0.0121,1962200,9811,9811
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,660,0.0117,1974200,9871,9871
+200,32,660,0.0121,1974200,9871,9871
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,664,0.0118,1986200,9931,9931
+200,32,664,0.0121,1986200,9931,9931
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,668,0.0119,1998200,9991,9991
+200,32,668,0.0122,1998200,9991,9991
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,672,0.0120,2010200,10051,10051
+200,32,672,0.0122,2010200,10051,10051
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,676,0.0120,2022200,10111,10111
+200,32,676,0.0124,2022200,10111,10111
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,680,0.0120,2034200,10171,10171
+200,32,680,0.0123,2034200,10171,10171
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,684,0.0121,2046200,10231,10231
+200,32,684,0.0124,2046200,10231,10231
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,688,0.0122,2058200,10291,10291
+200,32,688,0.0126,2058200,10291,10291
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,692,0.0123,2070200,10351,10351
+200,32,692,0.0127,2070200,10351,10351
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,696,0.0124,2082200,10411,10411
+200,32,696,0.0126,2082200,10411,10411
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,700,0.0124,2094200,10471,10471
+200,32,700,0.0128,2094200,10471,10471
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,704,0.0125,2106200,10531,10531
+200,32,704,0.0127,2106200,10531,10531
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,708,0.0125,2118200,10591,10591
+200,32,708,0.0128,2118200,10591,10591
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,712,0.0125,2130200,10651,10651
+200,32,712,0.0129,2130200,10651,10651
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,716,0.0125,2142200,10711,10711
+200,32,716,0.0130,2142200,10711,10711
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,720,0.0126,2154200,10771,10771
+200,32,720,0.0130,2154200,10771,10771
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,724,0.0127,2166200,10831,10831
+200,32,724,0.0131,2166200,10831,10831
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,728,0.0128,2178200,10891,10891
+200,32,728,0.0131,2178200,10891,10891
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,732,0.0128,2190200,10951,10951
+200,32,732,0.0132,2190200,10951,10951
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,736,0.0130,2202200,11011,11011
+200,32,736,0.0134,2202200,11011,11011
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,740,0.0130,2214200,11071,11071
+200,32,740,0.0134,2214200,11071,11071
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,744,0.0130,2226200,11131,11131
+200,32,744,0.0134,2226200,11131,11131
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,748,0.0131,2238200,11191,11191
+200,32,748,0.0135,2238200,11191,11191
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,752,0.0133,2250200,11251,11251
+200,32,752,0.0136,2250200,11251,11251
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,756,0.0133,2262200,11311,11311
+200,32,756,0.0136,2262200,11311,11311
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,760,0.0133,2274200,11371,11371
+200,32,760,0.0137,2274200,11371,11371
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,764,0.0134,2286200,11431,11431
+200,32,764,0.0138,2286200,11431,11431
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,768,0.0135,2298200,11491,11491
+200,32,768,0.0138,2298200,11491,11491
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,772,0.0137,2310200,11551,11551
+200,32,772,0.0139,2310200,11551,11551
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,776,0.0136,2322200,11611,11611
+200,32,776,0.0139,2322200,11611,11611
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,780,0.0137,2334200,11671,11671
+200,32,780,0.0140,2334200,11671,11671
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,784,0.0137,2346200,11731,11731
+200,32,784,0.0141,2346200,11731,11731
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,788,0.0138,2358200,11791,11791
+200,32,788,0.0142,2358200,11791,11791
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,792,0.0139,2370200,11851,11851
+200,32,792,0.0142,2370200,11851,11851
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,796,0.0140,2382200,11911,11911
+200,32,796,0.0144,2382200,11911,11911
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,800,0.0140,2394200,11971,11971
+200,32,800,0.0144,2394200,11971,11971
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,804,0.0141,2406200,12031,12031
+200,32,804,0.0144,2406200,12031,12031
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,808,0.0143,2418200,12091,12091
+200,32,808,0.0146,2418200,12091,12091
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,812,0.0142,2430200,12151,12151
+200,32,812,0.0146,2430200,12151,12151
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,816,0.0143,2442200,12211,12211
+200,32,816,0.0146,2442200,12211,12211
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,820,0.0144,2454200,12271,12271
+200,32,820,0.0147,2454200,12271,12271
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,824,0.0144,2466200,12331,12331
+200,32,824,0.0148,2466200,12331,12331
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,828,0.0145,2478200,12391,12391
+200,32,828,0.0149,2478200,12391,12391
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,832,0.0146,2490200,12451,12451
+200,32,832,0.0149,2490200,12451,12451
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,836,0.0146,2502200,12511,12511
+200,32,836,0.0150,2502200,12511,12511
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,840,0.0147,2514200,12571,12571
+200,32,840,0.0151,2514200,12571,12571
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,844,0.0148,2526200,12631,12631
+200,32,844,0.0152,2526200,12631,12631
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,848,0.0149,2538200,12691,12691
+200,32,848,0.0151,2538200,12691,12691
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,852,0.0149,2550200,12751,12751
+200,32,852,0.0152,2550200,12751,12751
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,856,0.0150,2562200,12811,12811
+200,32,856,0.0153,2562200,12811,12811
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,860,0.0152,2574200,12871,12871
+200,32,860,0.0154,2574200,12871,12871
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,864,0.0151,2586200,12931,12931
+200,32,864,0.0155,2586200,12931,12931
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,868,0.0151,2598200,12991,12991
+200,32,868,0.0155,2598200,12991,12991
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,872,0.0151,2610200,13051,13051
+200,32,872,0.0156,2610200,13051,13051
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,876,0.0152,2622200,13111,13111
+200,32,876,0.0156,2622200,13111,13111
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,880,0.0155,2634200,13171,13171
+200,32,880,0.0157,2634200,13171,13171
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,884,0.0154,2646200,13231,13231
+200,32,884,0.0158,2646200,13231,13231
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,888,0.0155,2658200,13291,13291
+200,32,888,0.0159,2658200,13291,13291
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,892,0.0155,2670200,13351,13351
+200,32,892,0.0159,2670200,13351,13351
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,896,0.0156,2682200,13411,13411
+200,32,896,0.0160,2682200,13411,13411
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,900,0.0157,2694200,13471,13471
+200,32,900,0.0160,2694200,13471,13471
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,904,0.0159,2706200,13531,13531
+200,32,904,0.0162,2706200,13531,13531
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,908,0.0160,2718200,13591,13591
+200,32,908,0.0162,2718200,13591,13591
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,912,0.0161,2730200,13651,13651
+200,32,912,0.0163,2730200,13651,13651
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,916,0.0162,2742200,13711,13711
+200,32,916,0.0163,2742200,13711,13711
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,920,0.0161,2754200,13771,13771
+200,32,920,0.0164,2754200,13771,13771
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,924,0.0162,2766200,13831,13831
+200,32,924,0.0165,2766200,13831,13831
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,928,0.0163,2778200,13891,13891
+200,32,928,0.0166,2778200,13891,13891
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,932,0.0165,2790200,13951,13951
+200,32,932,0.0168,2790200,13951,13951
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,936,0.0165,2802200,14011,14011
+200,32,936,0.0167,2802200,14011,14011
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,940,0.0165,2814200,14071,14071
+200,32,940,0.0169,2814200,14071,14071
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,944,0.0166,2826200,14131,14131
+200,32,944,0.0169,2826200,14131,14131
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,948,0.0166,2838200,14191,14191
+200,32,948,0.0169,2838200,14191,14191
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,952,0.0168,2850200,14251,14251
+200,32,952,0.0170,2850200,14251,14251
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,956,0.0167,2862200,14311,14311
+200,32,956,0.0170,2862200,14311,14311
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,960,0.0168,2874200,14371,14371
+200,32,960,0.0171,2874200,14371,14371
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,964,0.0173,2886200,14431,14431
+200,32,964,0.0175,2886200,14431,14431
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,968,0.0172,2898200,14491,14491
+200,32,968,0.0175,2898200,14491,14491
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,972,0.0172,2910200,14551,14551
+200,32,972,0.0176,2910200,14551,14551
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,976,0.0173,2922200,14611,14611
+200,32,976,0.0176,2922200,14611,14611
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,980,0.0175,2934200,14671,14671
+200,32,980,0.0178,2934200,14671,14671
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,984,0.0176,2946200,14731,14731
+200,32,984,0.0178,2946200,14731,14731
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,988,0.0176,2958200,14791,14791
+200,32,988,0.0179,2958200,14791,14791
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,992,0.0177,2970200,14851,14851
+200,32,992,0.0178,2970200,14851,14851
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,996,0.0178,2982200,14911,14911
+200,32,996,0.0181,2982200,14911,14911
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,1000,0.0177,2994200,14971,14971
+200,32,1000,0.0180,2994200,14971,14971
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,1004,0.0179,3006200,15031,15031
+200,32,1004,0.0181,3006200,15031,15031
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,1008,0.0179,3018200,15091,15091
+200,32,1008,0.0182,3018200,15091,15091
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,1012,0.0180,3030200,15151,15151
+200,32,1012,0.0183,3030200,15151,15151
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,1016,0.0180,3042200,15211,15211
+200,32,1016,0.0183,3042200,15211,15211
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,1020,0.0182,3054200,15271,15271
+200,32,1020,0.0184,3054200,15271,15271
iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)
-200,32,1024,0.0178,3066200,15331,15331
-mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.vst.bin.csv .
+200,32,1024,0.0182,3066200,15331,15331
+mv /gpfs/wolf/trn003/scratch/aherten//poisson2d.vst.bin.csv .
</pre>
</div>
</div>
@@ -15914,14 +16231,14 @@ mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.vst.bin.csv .
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>Let's plot it again, as soon as the run finishes! Non-interactively, call <code>graph_task2b</code>.</p>
-<p><em>We need to read in two CSV files now, which we combine to one common dataframe <code>df_vldvst</code>.</em></p>
+<p><em>Because we couldn't measure the two vector counters at the same time, we have two CSV files to read in now. We combine them into one common dataframe <code>df_vldvst</code> in the following.</em></p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
-<div class="prompt input_prompt">In [8]:</div>
+<div class="prompt input_prompt">In [31]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_vld</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">"poisson2d.vld.bin.csv"</span><span class="p">,</span> <span class="n">skiprows</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">50000</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
@@ -15936,11 +16253,10 @@ mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.vst.bin.csv .
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
-<div class="prompt input_prompt">In [9]:</div>
+<div class="prompt input_prompt">In [32]:</div>
<div class="inner_cell">
<div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">common</span><span class="o">.</span><span class="n">normalize</span><span class="p">(</span><span class="n">df_vldvst</span><span class="p">,</span> <span class="s2">"PM_VECTOR_LD_CMPL (min)"</span><span class="p">,</span> <span class="s2">"Vector Loads / Loop Iteration"</span><span class="p">)</span>
-<span class="n">common</span><span class="o">.</span><span class="n">normalize</span><span class="p">(</span><span class="n">df_vldvst</span><span class="p">,</span> <span class="s2">"PM_VECTOR_ST_CMPL (min)"</span><span class="p">,</span> <span class="s2">"Vector Stores / Loop Iteration"</span><span class="p">)</span>
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_vldvst</span><span class="p">[</span><span class="s2">"Grid Points"</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_vldvst</span><span class="p">[</span><span class="s2">"nx"</span><span class="p">]</span> <span class="o">*</span> <span class="n">df_vldvst</span><span class="p">[</span><span class="s2">"ny"</span><span class="p">]</span>
<span class="n">df_vldvst</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>
@@ -15954,7 +16270,7 @@ mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.vst.bin.csv .
<div class="output_area">
- <div class="prompt output_prompt">Out[9]:</div>
+ <div class="prompt output_prompt">Out[32]:</div>
@@ -15987,8 +16303,7 @@ mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.vst.bin.csv .
<th>PM_VECTOR_ST_CMPL (total)</th>
<th>PM_VECTOR_ST_CMPL (min)</th>
<th>PM_VECTOR_ST_CMPL (max)</th>
- <th>Vector Loads / Loop Iteration</th>
- <th>Vector Stores / Loop Iteration</th>
+ <th>Grid Points</th>
</tr>
</thead>
<tbody>
@@ -16004,8 +16319,7 @@ mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.vst.bin.csv .
<td>200</td>
<td>1</td>
<td>1</td>
- <td>0.000000</td>
- <td>0.007812</td>
+ <td>128</td>
</tr>
<tr>
<th>1</th>
@@ -16019,8 +16333,7 @@ mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.vst.bin.csv .
<td>18200</td>
<td>91</td>
<td>91</td>
- <td>2.226562</td>
- <td>0.355469</td>
+ <td>256</td>
</tr>
<tr>
<th>2</th>
@@ -16034,38 +16347,35 @@ mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.vst.bin.csv .
<td>30200</td>
<td>151</td>
<td>151</td>
- <td>2.265625</td>
- <td>0.393229</td>
+ <td>384</td>
</tr>
<tr>
<th>3</th>
<td>16</td>
<td>200</td>
<td>32</td>
- <td>0.0013</td>
+ <td>0.0012</td>
<td>234000</td>
<td>1170</td>
<td>1170</td>
<td>42200</td>
<td>211</td>
<td>211</td>
- <td>2.285156</td>
- <td>0.412109</td>
+ <td>512</td>
</tr>
<tr>
<th>4</th>
<td>20</td>
<td>200</td>
<td>32</td>
- <td>0.0014</td>
+ <td>0.0013</td>
<td>294000</td>
<td>1470</td>
<td>1470</td>
<td>54200</td>
<td>271</td>
<td>271</td>
- <td>2.296875</td>
- <td>0.423438</td>
+ <td>640</td>
</tr>
</tbody>
</table>
@@ -16080,12 +16390,103 @@ mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.vst.bin.csv .
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
-<div class="prompt input_prompt">In [10]:</div>
+<div class="prompt input_prompt">In [33]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="p">(</span><span class="n">ax1</span><span class="p">,</span> <span class="n">ax2</span><span class="p">)</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">df_vldvst</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"Grid Points"</span><span class="p">)[</span><span class="s2">"PM_VECTOR_LD_CMPL (min)"</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">True</span><span class="p">);</span>
+<span class="n">df_vldvst</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"Grid Points"</span><span class="p">)[</span><span class="s2">"PM_VECTOR_ST_CMPL (min)"</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax2</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">True</span><span class="p">);</span>
+</pre></div>
+
+ </div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+ <div class="prompt"></div>
+
+
+
+
+<div class="output_png output_subarea ">
+<img 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nrdRKOxbDdDJkB9lnvUZ7lF/ZU9mejv1FS7mL0uabTo78zeSI1+N8cu9bO/NZ7Fls9dDkce1dUugAXAzvFepxEnkaNk5crVnHTSKXR3d094L6fx6OvrY82atYcsmkREROTIJJMW+/Z3DUu1C0Xiw6K/ffNKCfhdnLyq1h5JGi36O1/7JeUcFU455tprrxwWBABw7LEruOWW27LUosn51a9eYMeOr454/brrbhhziuBo3nrL5M47Pzfi9YsvvpTzz79wUm2cDtPZlqKiomEbIIuIiMjk9fYn2B1Np9ql90baHYnTNzAk+tubiv4ODlmPVFqsj9ezlXo2xzz22Ley3YQpceKJGydVIB3KkiUGjz/+nSm7n4iIiMwdsa6+VKJdJLUeaVc4xr79XUOivwsI+lycuqaOxnSBpOjvuWfOFE6WZSmyUea0WbaeUUREZMIsyyLa3kNoX2Yt0ujR341+N+sNH0F/an8kj6K/hTlSOBUUFNHZ2YHTWa4/9DInWZZFZ2cHBQWKMxURkblhaPS3HdoQidHdm1ryYEd/B+fZG8gGfC7civ6WQ5gThVNlpZe2tijx+IFpf5bD4SCZVKpeLpkrfVZQUERlpTfbzRAREZlyXT0DNEVi9lqkpnCc5pZOEslM9LeDgNfFhuU1dqpdvcdJkaK/ZQLmROGUn1+Ax1N7VJ6lONDcoz4TERHJDZZl0RbrHZxml16XFD3QY5/jLisk6HfznoVV9kiSv7IMh1Ls5AjNicJJRERERHKLHf0dHhxJCoXjxLuHRH9XltLod3PKqrr0VLvRo79FpoIKJxERERHJqkz0d2ZvpF3hOM3Rwejvgvw86j0u1izx2HsjKfpbjjb9aRMRERGRo6ajqy+1N9KQkaSDo78b/S42ram390eqrS5T9LdknQonEREREZlyScui5UC3vQ4pk253IN5nn1NVXkzQ5+b4ZT4CPjeNfhfViv6WGUqFk4iIiIgckYFEkuZop10gNYVjNEXjdvS3Iy+P2uoyjmmsJJAObAj63bhKC7PccpHxG1fhZBhGCXA/cCbQA/zKNM2PGoaxFHgCqAZagatM03wrfc2UHxMRERGR7LKjv4dMt9tzcPS3LxX9nSmQFP0ts8F4R5zuIVUwLTVN0zIMw59+/RHgK6ZpPmkYxhXADuD0aTwmIiIiIkeBHf2dnmrXFI6zKxyjpX0w+rs8Hf29YmEVjenABkV/y2w1ZuFkGIYLuApoME3TAjBNM2wYhg9YB5yVPvWfgYcMw/ACeVN9zDTN6BG9UxEREREZVSKZZN/+bnvz2MyUu6HR3/7KUubXlnPq6jqC/tR0u3mu4iy2WuToGs+I0yJSU+Y+YxjGaUAcuB3oBppN00wAmKaZMAxjDxAgVQBN9TEVTiIiIiJHqLcvHf09ZG+k3dE4/QdFf69d4rELpAavor9FxvMbUAAsBH5nmuYthmGcAPwI+MtpbdkRqK52ZfX5Xq87q8+XiVOf5R71We5Rn+Ue9VluGa2/DsR6+fOedt5pbufPze38eU87e6Jx0suRcJYWsrCugnNPWsDC+nIW1s+jwedS9PdRot+x3JJnZULzD8EwDA+wFyjKTNUzDON14BrgZ0B1emQon9TI1BJSI0dvTuWxcU7Vmw+809oaJ5k8/PuaLl6vm2g0lpVny+Soz3KP+iz3qM9yj/ost1RXu3j97ciwaXYHR39XlxcPS7QL+l1Ulyv6O1v0O5Y9DkdeZqBlAbBzvNeNOeJkmmaLYRi/ILXu6Gfp1DsfqQLnZeBy4Mn0199lChzDMKb8mIiIiMhc1z+QZE9L57ANZHdHO+nuHQDS0d+eVPR30O8m6HMRUPS3yBEb72TVLcDXDcO4D+gHrjRN84BhGFuAJwzD+DTQRipEYug1U31MREREZM7o6umnKRJnV3pvpF3hOHtbB6O/iwvzCfhcnHZcA76KEgI+l6K/RabJmFP1csx8NFVPJkh9lnvUZ7lHfZZ71GdH17Do7yEjScOiv51FqWl2Q6bb+eaV4nDkqb9ykPose6Ztqp6IiIiITJ1EMsm+1q5hqXZNkZHR3wtqy9m0ps6ebleh6G+RrFLhJCIiIjJNevsSNEUHp9k1RVLrkYZFf3tdrFvqsYMbFP0tMjPpt1JERERkCnR09g2bZhcKxwnv7yKzeMBZUpBaj7S23p5yV1NdpuhvkRyhwklERERkApKWRfRAt70eKRXeEKN9WPR3CUG/i3cd46PR7yag6G+RnKfCSUREROQQMtHfu8Ixe4+kpkicnr4EkIr+rvOUsbyxyg5sCPhciv4WmYVUOImIiIgAnT39qeLInm43evT3SStq7A1k6z1OCgsU/S0yF6hwEhERkTnFsiz2d/SmRo/CqWl2TZH4sOjvCmcRAb+L1YurCfhcNPrdeCtLcWiqncicpcJJREREZq1EMsne1i57ml1mXVJnzwAAeYCvqkzR3yIyJhVOIiIiMiv09A2wO9ppJ9qFwqno74FEJvrbQYPXyXGGN10guWnwOSkp0schERmb/qYQERGRnNPe2ZfeGymTahcnclD0d9Dv5vR19XaqXU2Vor9FZPJUOImIiMiMlbQsom3dw/ZGCoVjtHeOjP7esNxv749UVV6s6G8RmVIqnERERGRG6B9I0NzSaRdHoUicpkic3oOiv49dUEXQl47+9rtwlij6W0SmnwonEREROeo6e/oJhePp6XZxmiIx9rZ2DUZ/F6WivzeuqCXgdyn6W0SyToWTiIiITBs7+tveGyk13a61Y0j0t6uIoM/N6sUeO9VO0d8iMtOocBIREZEpMZBIsm9/17C1SE2R+LDob39VGYvqyzltXT1Bn4uA302Fsyi7DRcRGQcVTiIiIjJhPX0D7I50plPtUtPtmodEfxcWZKK/fanABr+bBq+iv0Ukd+lvLxERETms9njv8FS7yOjR32ccV0/Q5ybod1FTXUa+Q9HfIjJ7qHASERERIBX9HWnrJhSO0fLrJv64s5WmcHxY9LenooSg382Jy/0E/C4a/W4q3Yr+FpHZT4WTiIjIHNQ/kGB3tJOmISNJTZE4vf2p6O98Rx611c5U9Hc6sCHod1Gm6G8RmaNUOImIiMxy8e5+muxUuzihSIy9LV0krcHo76DPxcaVtfZ6pNXH+DnQ1pXllouIzBwqnERERGYJy7Jo7egZlmgXCsdo7ei1z5nnKiLod7NmsYfG9Aay3nkjo7+1X5KIyHAqnERERHLQQCLJvtYuQpGxor8rOG1dKrAh4FP0t4jIZKlwEhERmeG6ewfYHY3bBVIoMnr09/plvvRaJDcNXhfFRRo1EhGZKiqcREREZpAD8d50UENqb6SmcIxIW/eI6O8zj2sgkF6PVFNVquhvEZFppsJJREQkC5KWRXh/V3od0uBIUsdo0d8rauz9kRT9LSKSHSqcREREplkm+jtkJ9vF2B3pHBb9XedxsjIT/e13EfAp+ltEZCaZUOFkGMZngM8CK03T/INhGBuAHUApsBO4wjTNSPrcKT8mIiIy02Wiv3elp9uFwnH2tg5Gf5dkor9XpaO/fW7qPE4KCzTVTkRkJht34WQYxjpgAxBKf58HPAlcY5rmC4Zh3A7cDXxoOo5N1RsWERGZCpZl0dreY48gZfZH2j9K9PfapR57qp1nlOhvERGZ+cZVOBmGUQx8BfgA8Iv0y+uBHtM0X0h//wipEaIPTdMxERGRrBhIJNnb2mUXSJmRpK7ewejvmuoyljTMI+hzpUIbfG7KFf0tIjJrjHfE6R+AJ03TfMcwjMxrQWBX5hvTNFsMw3AYhlE1HcdM09w/yfcoIiIybt29AzRF4vbmsaFwnOaWOAOJ1FS7VPS3i3cd4yPgdxP0uRT9LSIyB4xZOBmGcSJwPPC309+cqVFd7crq871ed1afLxOnPss96rPcM9P6zLIs2mK9/Lm5PfW/Pamve1s67XPcZUUsqq/guGP8LKivYGFdOfVeF/n5c2M90kzrMzk89VfuUZ/llvGMOG0ClgGZ0aYG4L+AB4HGzEmGYXgAyzTN/YZhhKb62ETeVGtrnGTSGvvEaeD1uolGY1l5tkyO+iz3qM9yT7b7LJm0CLd12euQmtLx3x1d/YNtnFdC0OfmhGN8qWQ73+jR3/v3dx58+1kp230mE6P+yj3qs+xxOPImNdAyZuFkmubdpAIaADAMYydwHvA68FHDMDam1yRtAf4lfdpvgdIpPiYiIjKmvv4EzS2dwwIbDo7+rvc4Wbmo2g5sUPS3iIiMZdL7OJmmmTQM40pgh2EYJaSjw6frmIiIyMHi3f3DCqSmg6K/S4vzCfjcnLKqloDfRaPfTW21or9FRGTi8iwrO1Papsl84B1N1ZOJUJ/lHvVZ7jnSPrMsi5b2nmGJdgdHf1e6iwn4XPY0u2CNG09FiaK/J0m/Z7lF/ZV71GfZM2Sq3gJSAzXjMukRJxERkelwcPR3KBwjFInTnYn+zoOaqnT0dzr2O+B3UV6m6G8REZk+KpxERCRrMtHfmeIoFI6xp6XTjv4uKnDQ4HPZgQ0Bfzr6u1DR3yIicnSpcBIRkWmXif5uisTYFY7TlB5Nihzots9xlRbS6Hdx5vqAPZJUU1WGw6GpdiIikn0qnEREZEoNi/5OjyQ1Rzs5EB9cj+SdV0LQ7+bklTWpNUl+N/NcRSOiv0VERGYKFU4iIjJpff0Jdkc7CaUDG5rCMZqicfr6k8Bg9Pf6Y/x4K4oJ+lwEfG7KSvSfHxERyS36L5eIiIxLrKuPUCRubx4bisTZ29pJJpw1E/196uo6e3+kOo+TgnyH0qNERCTnqXASEZFhBqO/h6fatcWGR38HfS6OW+pNbSDrd+OtKNFUOxERmbVUOImIzGEDiSR7WjqHbSB7cPR3bbUTIzCPgD+1R1LAp+hvERGZe1Q4iYjMEZno713hTIE0Mvo74HNxwnJ/agNZv5t6r1PR3yIiIqhwEhGZdSzL4kC8Lz3VLmavSxoa/e0uKyTod3PW+ioCfheNfjf+SkV/i4iIHIoKJxGRHJZMWuzb3zU4zS5dKMW6+u1zfPNKCfhdnLyq1h5JUvS3iIjIxKhwEhHJEb39CXZH4/Y6pFA4xu5InL6BIdHfXierF3lSG8im1yOVFuuvehERkSOl/5qKiMxAmejvUHo90q5wjH37u4ZEfxcQ9Lk4dU0djekCKRP9LSIiIlNPhZOISBZZlkW0vYemcIxd6Q1kR4v+bvS7WW/4CPpT+yN5FP0tIiJyVKlwEhE5SoZFf2dCGyIxunsTwJDo7+A8ewPZgM+FW9HfIiIiWafCSURkGnT1DNAUiQ2bbtfc0kkimY7+LnQQ8LrYsLzGTrWr9zgpUvS3iIjIjKTCSUTkCGSiv1N7I8XsjWSjB3rsczLR3+9ZWGWPJCn6W0REJLeocBIRGSc7+js8OJIUCseJdw+J/q4spdHv5pRVdXayXYVT0d8iIiK5ToWTiMgoMtHfoXRgw65wnOboYPR3QX4e9R4Xa5Z47FQ7RX+LiIjMXvovvIjMeR1dfcM2jw0dFP1dVlxA0O9i05p6exSptrpM0d8iIiJziAonEZkzkpZFy4Fuex1SKByn6aDo76ryYoI+N8cv8xHwuWn0u6hW9LeIiMicp8JJRGalgUSS5mjnYIEUjtEUjdvR3468PGqry1gWnEcgHdgQ9LtxlRZmueUiIiIyE6lwEpGcZ0d/DxlJ2nNw9LfPxYZjawj6UgWSor9FRERkIlQ4iUjOsCyLtlgv70Q6efWtCE3hOLvCMVraB6O/y9PR3ysWVtmhDYr+FhERkSOlwklEZqREMsm+/d325rGZkaSh0d/+ylLm15Zz6uo6gv7UdLt5ruIstlpERERmKxVOIpJ1vX3p6O8heyPtjsbpHxr97XWxdomHoN/NKsOHq9Ch6G8RERE5avSpQ0SOqo6uPnsUaVc4RlMkPmr092lr6wn4XDT63dQcFP3t9bqJRmNZegciIiIyF41ZOBmGUQ18C1gE9AJvA9eZphk1DGMDsAMoBXYCV5imGUlfN+XHRCR3jBb9HQrHOBDvs8+pLi8mkI7+zky1qy5X9LeIiIjMPOMZcbKAe0zTfBbAMIwvAncbhvFh4EngGtM0XzAM43bgbuBDhmHkTfWxqXzTIjK1+geS7GnpHLaBbFMkTk/fkOhvTxnHNFamCiSfi4CqGVAKAAAgAElEQVSiv0VERCSHjFk4maa5H3h2yEv/D7geWA/0mKb5Qvr1R0iNEH1omo6JyAzQ1dNPUyTOrvTeSKHI8Ojv4sJ8Aj4XJ66osVPtGrxOCgsU/S0iIiK5a0JrnAzDcJAqmn4IBIFdmWOmabYYhuEwDKNqOo6lCzgROUoy0d+ZKXaZkaRh0d/OIoJ+FysXVtsbyPrmlSr6W0RERGadiYZDfBmIAw8BfzH1zZka1dWurD7f63Vn9fkycXO9zxKJJLujcd5pbudPze28s6edPzd3EOsaXI9U53GybH4VC+srUv+rq6CyvCRrbZ7rfZaL1Ge5R32WW9RfuUd9llvGXTgZhnEvsAQ43zTNpGEYIaBxyHEPYJmmuX86jk3kTbW2xkmmpw0dbUr7yj1zrc96+xI0RQen2YXCMXZHO4dEfzuo9zpZu6SagC8V2NDgdY2I/h7o7Sca7R/tEdNurvXZbKA+yz3qs9yi/so96rPscTjyJjXQMq7CyTCMO4HjgM2mafamX/4tUGoYxsb0mqQtwL9M4zERmaCOzr5h0+xC4Tjh/V1k/lnBWVJAwJeK/g76XQR9I6O/RURERGR8ceTHArcBbwK/NAwD4B3TNP/CMIwrgR2GYZSQjg4HSI9ITekxETm0pGURzUR/pxPtdoVjtA+L/i4h6HfxrmN8qdAGRX+LiIiIjFueZWVnSts0mQ+8o6l6MhG51meZ6O9d6U1kQ5GR0d91njJ7ml0wnWw3m6K/c63PRH2Wi9RnuUX9lXvUZ9kzZKreAlIDNeMy0XAIETmKOnv608XR4FS7va1Dor+LUtHfJ62osTeQrfco+ltERERkqqlwEpkBLMtif0dvavQonJpm1xSJD4v+rnAWEfS7Wb24moDPRaPfjbeyFIem2omIiIhMOxVOIkdZIplkX2tXaj1SJGavS+rsGQAgD/BVlbGgtpxNa+pSI0k+FxWu4uw2XERERGQOU+EkMo16+gbYHe20p9llor8HEoPR3w1eJ8cZ3nSB5KbB56SkSL+aIiIiIjOJPp2JTJH2zj6awjF7mt2ucJzIQdHfQb+b09fV26l2tdVl5DsU/S0iIiIy06lwEpmgpGURbeseFtgQCsdo7xwZ/b1hud/eH6mqvFjR3yIiIiI5SoWTyGH0DyRpbonbxVEoEqcpEqf3oOjvYxdUEfSlo7/9Lpwlsyf6W0RERERUOInYOnv6CYXj6el2cZoiMfa2do2I/t64opaAP5VqV+cpU/S3iIiIyBygwknmHDv6Oz2CtK+tm7ebDtDaMST621VE0Odm9WKPvT+Sd56iv0VERETmKhVOMqslkkn2tnbZa5GaIiOjv+u8LhbVl3PaunqCPhcBv5sKZ1F2Gy4iIiIiM4oKJ5k1evoG2B3pTKfapQqlodHfhQWZ6G8fjf5UgdTgdRKoryQajWW59SIiIiIyk6lwkpzUHu8dnmoXGT36+4zj6u0NZGsU/S0iIiIik6TCSWa0pGURaesmZO+NFKMpHB8W/e2pKCHod3Picr8d2lDpVvS3iIiIiEwdFU4yY/QPJNgd7bTXIWXWJPX2p6K/8x151FY7WbGgikB6FCnod1Gm6G8RERERmWYqnCQr4t39NKVT7VJT7WLsbekiaQ1Gfwd9LjaurE1tIOt3U+dxUligqXYiIiIicvSpcJJpZVkWrR09NIXT0+zSo0mtHb32OfNcRQT9btYs9tCY3kBW0d8iIiIiMpOocJIpM5BIsq+1i1A60S6zLmlo9HdNdRmL6is4bV1qb6SAT9HfIiIiIjLzqXCSSenuHWB3NG4XSKFInOYR0d8u1i/zpdciuWnwuiguys9yy0VEREREJk6Fk4ypPd7LrnCcpkgs9TUcI9LWPSL6+8zjGgik1yPVVJUq+ltEREREZg0VTmIbGv09dCSpY7To7xU1BH2p6XaK/hYRERGR2U6F0xyVif4O2cl2MXZHOodFf9d5nKxcUJXaQNbvIuBT9LeIiIiIzE0qnOaATPR3ZrpdKBIfFv1dkon+XpWO/vYp+ltEREREZCgVTrOIZVm0tvfYI0ihdKE0WvT32iUee6qdR9HfIiIiIiKHpcIpRw0kkuxt7RpWIIXCcbp6h0d/L26Yx+npVLuAz0W5or9FRERERCZMhVMO6O4doCkStzePDYXjNLfEGUikptplor/fdYyPgN9N0OdS9LeIiIiIyBRS4TSDWJZFe2ffsES7UDr6O8NVWkjQ7+LM9QGCPhcBRX+LiIiIiEy7GVk4GYaxFHgCqAZagatM03wru62aWsmkRbiti6ZInF3hGE3pYqmjq98+xzuvhKDPzckrauyRJEV/i4iIiIgcfTOycAIeAb5imuaThmFcAewATs9ymyatfyDJ7ujgNLtQZGT0d73HycpF1anob5+LgM9NWclM7R4RERERkbllxn0yNwzDB6wDzkq/9M/AQ4ZheE3TjGavZRNjWRZvNh3gl3/Yx4tmlO50aENpcT4Bn5tTVtUS8Lto9KeivwvyNdVORERERGSmmnGFExAAmk3TTACYppkwDGNP+vWcKZx++Yd9PPbjNyguyue4pV7WLPYQrHHjqShR9LeIiIiISI6ZiYXTEauudmX1+V6vm9PeVUR9TTkrF3koKZ6VP+ZZxet1Z7sJMkHqs9yjPss96rPcov7KPeqz3DITP9E3AfWGYeSnR5vygbr06+PS2honmbSmrYGH4/W6iUZjAMz3Ool1dBPLSktkvIb2meQG9VnuUZ/lHvVZblF/5R71WfY4HHmTGmiZcQtrTNOMAC8Dl6dfuhz4XS6tbxIRERERkdllJo44AWwBnjAM49NAG3BVltsjIiIiIiJz2IwsnEzT/CNwQrbbISIiIiIiAjO0cDoC+ZCat5hN2X6+TJz6LPeoz3KP+iz3qM9yi/or96jPsmPIzz1/ItflWVZ2QhSmyUbg+Ww3QkREREREZrxTgBfGe/JsK5yKgeOBvUAiy20REREREZGZJx+oBX4D9I73otlWOImIiIiIiEy5GRdHLiIiIiIiMtOocBIRERERERmDCicREREREZExqHASEREREREZgwonERERERGRMahwEhERERERGYMKJxERERERkTGocBIRERERERmDCicREREREZExqHASEREREREZgwonERERERGRMahwEhERERERGYMKJxERERERkTGocBIRERERERmDCicREREREZExqHASEREREREZgwonERERERGRMahwEhERERERGYMKJxERERERkTGocBIRERERERmDCicREREREZExqHASEREREREZgwonERERERGRMahwEhERERERGYMKJxERERERkTGocBIRERERERlDQbYbMMWKgeOBvUAiy20REREREZGZJx+oBX4D9I73otlWOB0PPJ/tRoiIiIiIyIx3CvDCeE+ebYXTXoC2tk6SSSsrDaiudtHaGs/Ks2Vy1Ge5R32We9RnuUd9llvUX7lHfZY9DkcelZVOSNcO4zXbCqcEQDJpZa1wyjxfcov6LPeoz3KP+iz3qM9yi/or96jPsm5CS3sUDiEiIiIiIjIGFU4iIiIiIiJjUOEkIiIiIiIyhnGtcTIM417gYmA+sNI0zT+kX98J9KT/B3CraZr/lT62AdgBlAI7gStM04wcybHJSiQGaGuLMjDQdyS3GZdIxEEymZz258jUmQt9VlBQRGWll/z82basUURERGaarp5+miJxQuE4oXCMUCRO30CSO659FwX5uTtuM95PUT8AHmD0qO9LMoVUhmEYecCTwDWmab5gGMbtwN3AhyZ7bDJvLqOtLUpJSRlOZw15eXlHcqsxFRQ4GBiY3R/CZ5vZ3meWZdHZ2UFbWxSPpzbbzREREZFZwrIs2mK9qQIpErMLpZb2HvucCmcRAb+L5Y1VOBzT+zl8uo2rcDJN8wUAwzDGe9/1QE/mOuARUqNHHzqCY5M2MNB3VIomkZkoLy8Pp7OcePxAtpsiIiIiOSqZtNi7v4umcGxYoRTv7rfP8VeWsqC2nE1r6gj63QR9LipcxVls9dSaink7306PFL0A3Gaa5gEgCOzKnGCaZothGA7DMKome8w0zf1H0kgVTTKX6c+/iIiIjFdvf4Ld0dRUu6ZwjF3hOM3R1HQ7gIL8POq9LtYu8aQKJL+LBq+L0uLZvSTgSN/dKaZpNhmGUQx8CXgIuOLIm3Vkqqtdw76PRBwUFBy9+ZRH81kyNeZCnzkcDrxed7abMWVm03uZK9RnuUd9llvUX7lnJvRZe7yXPze3886edv6U/tociZPZYspZWsii+greu2QBC+srWFhfQYPPldNrlSbriAon0zSb0l97DcP4KvDD9KEQ0Jg5zzAMD2CZprnfMIxJHZtIu1pb48M2FEsmk0dtDct41stccsn5FBUVUVhYRDKZ4Oqrr+XMM8/mpZde5GMf28Lll1/J1q032edv2/ZRXn75JX72s+coKysbcb+WliiXX34R3/ve07jdg7+AL730InfffQdPPfUDbrzxOsLhME6n0z7+iU/cysqVq7Esi3/91+/ywx9+H7BIJpOsXr2Wc87ZzP33fxGAjo52uro6qampA+D88y/k4osv5ZVXfscjjzxEW1sbiUSCtWvXsW3bzZSXl9ttzzy3t7eHs88+l2uu+fBhfz5vvfUmDzxwL/F4nIGBflwuN5///Bf5xje+xquvvgLAzp1/pq6unqKi1PDvY499i/z8/FHv98Ybr7Fjx1dobm6mpKSYefMqufba61izZh3btn2U11//Az/4wU8oL6+wf24f+9gWLrvsCrZt+zgvvfQit9xyE4FAI4nEANXVHm699XZqa+u4887PsmzZMVx88fsP+56SySRbt36Ez33u8/h8/sOee7BrrvkAO3Z8neLiksOet23bR7ntts9QV1d/yDZEo7EJPXum8nrds+a9zBXqs9yjPsst6q/cc7T7zLIsou099ghSUzq0oS3Wa59TXV5MwOdm7eLBkaTq8pIRM1fa9ncetXZPB4cjb8RAy3hMunAyDMMJFJim2Z6eqncZ8HL68G+BUsMwNqbXK20B/uUIj80q27d/gYULF/Pmm39ky5ZrWb/+BACCwUaef/5ZtmzZRn5+Pnv2NNPb23PYe3k8XlavXsszz/wXF154if3600//iHPPPd/+w/7xj/8NJ598yojrH330YV5++SUefPBhqqqqSSaTPP/8s3g8Xh5//Dv2vX75y+fZvv0e+7rdu5v41Kdu4Y47vsDatceRTCZ56KH7+fu//1seeOCr9nmZ57a0tHDFFZdw/PEbOPbYFYd8P5/73O1cf/2NdlubmkKUlJTyiU/cap9zySXn2z/Dw/nTn97mlls+zt///T9wwgkn2u1+++037XPmz1/If//3z7joor+036thHDPsPvPnL+Sxx74FwJe//I98+cv38/nPf/Gwzx7qF7/4bxYsWDjhogmw+2Asl176Ab7+9X/i9ts/N+FniIiIyOwxkEiyp6Vz2FqkpkiM7t4EAI68PGqry1gWnEfA56bR7yLgd+MqLcxyy2e28caRPwhcBNQA/20YRitwPvDvhmHkA/nA68ANAKZpJg3DuBLYYRhGCelY8SM5NpX+99W9vPD7vVN9WwA2ra1jw/KacZ+/dOkyysrK2Lu3GYDS0jLmz1/Ar3/9K048cSM/+cl/cs45m3njjdcPe5/Nm9/Hd77zLbtw6urq5LnnnuVb33rqsNd1dXXx3e9+m8cf/zZVVdVAakrXpk2nj9n2b37z62zefAFr1x5nX3fDDTdx6aUX8Morv2P16rXDzvd4PAQCjYTD+w5bOEWjYbxer/19IBAcsy2H8u1vP8F5511gF00ADQ0BGhoC9vebN5/PT3/6Yy666C/p6uri1Vdf4Ywz3kNf3+jx9evXv4uvfOXBCbXjhz/8/rCRtm3bPophHMMbb7zGvn17ueSSy/B6vfz7v/8LLS1RbrjhJk4//UwANm5cb482XnLJ+ZxzzmZ+85v/o7W1hcsvv8Ie7TrppI3cc8+ddHV1UlbmHLUdIiIiMrt09w6ko78HQxv2tHQykEjNvioqdBDwudiwvIag30XQ76be46SocPSZOnJo403V+xjwsVEOrR3ltcw1vwRWTuWx2eill16kr6+PhoYgb71lAnDuuefzH//xPTZsOJlnnvkZDz/8mD1l7lA2btzEfffdzTvv/JkFCxbyP//zc1asWIXfP1jEfelL9/Loow/b399//0Ps3buHoqJCgsH5E277n/70Nldffe2w1woKCli61ODtt98cUTiFQrvo6Gi3C61DueqqD7F160dYsWIVK1as4swzz6axceLtA3jzzT/y7nefcdhz6uvrKSoqYufOd3jttVc55ZR3H3LaXzKZ5Nln/4elS8edMMnAwACvvvp7li8/dtjr0WiEhx76J/bvb+X977+QSy/9AI888nVef/0PfOpTn7QLp4P19PSwY8c32Lt3D1dd9X7e+97zKSsro6CggIULF/H737/Chg0njbt9IiIiMvNZlsWBeJ+9L1IoHKMpHCdyoNs+x11WSNDv5qzjqwj6UlPt/JVlOR8DPlPM7uiLQzh5ZS0nr5ye/WzGuyfQ7bffSlFRMU6nkzvv/MKwtUnr1q3nvvvu5rnnnmXhwkVUVMwb836FhYWcddZ7efrpH7F16038+Mc/4pJLLht2zmhT9fbs2TPOdzaSZVljn0SqYHv44S8TCu3kppv+hsrKysOe/8EPXs3ZZ5/Lb3/7G1588ddce+0V3Hvvg6xZs27a2njOOZv5yU/+k9dee5W//utP8otfPDPs+M6df+aaaz6AZVksXryYG2+8edxtOHDgAIWFBSPWKJ122hk4HA48Hi8VFfPYtOk0AAzjGKLRCL29vRQXj4zwPPPM9wBQW1uH211ONBqxC8uqqmqi0SPaL1pERESyLJm0CLd1DdtANhSOEesajP72zSsl6Hdx8qra1FQ7n5t5riIl6U6jOVk4zQSHW5+Tl5fH6aefxT33bOe22z477nued94F3HzzVjZvfh+h0E5OOWXTmNcsWLCQvr4+QqFdBIONY54/1OLFS3jttVc59dR3268NDAzw5psml102OMMyU7D9/vcvc/PNW1mzZh2LFh1+bZLH4+Xss8/l7LPPpbi4mGeffWZShVNmOtzQNo7m9NPP4oor/pLq6moWLlw8onAausZpooqLi0ed9pcJtoDUNMeioiIAe7QrkUiMer/MeZnrEokB+/u+vr5Riy0RERGZmfr6EzS3dLIrPYIUCsdoisbp60/9Q3y+I496r5PVizz2VLuAb/ZHf89E+onPUBdccBGlpaXD1uaMZdGixXi9PrZv/wxnnXXOsA/Yh1JWVsall36Ae+65kzvuuJvKyiosy+LnP/8pxx67kvr6hkNee8UV13D99R9iw4aT7HCIr371ARoaAqMWOatWreGiiy7la197hLvuuveQ933uuWc56aSNFBQU0Nvby86d74yrCBzN5Zdfyc0338C6des5/vhUAEcotJM33zQ588yzh/0ctm69iepqz6Seczhut5vKyir27t1DbW3dlN9/qF273mHx4iXT+gwRERGZnHh3v70WKdLew1uhNva2dpFMz5ApLS4g6HNx6uo6e6pdncc5J6O/ZyIVTjOU1+vjgx+8esLXnXfeBdx3393ceuunRhw7eI3Thz98HRs3buK667by1FPf5sYbrwNS09tWrVrLiSduPOyzgsFGtm+/hx07vsKBAwdIJAZYs2Yd27d/4ZDXXHXVh7jssgt56y2TJUtGXyf07LPP8PDDD1JUVEwiMcD69SeMGfd9KEuWLOXuu+/n0Ue/yhe/+HlKSkrSceRbRpx7xhnvmdQzHn30EZ588gn7+09+8rYRP7tTTz2N//u/X3HhhRdP6hnjsW9fKvBkrKRBERERmV6WZdHa3mNPscuENuzvGBL9XVFCg8fJ2qVeGtMjSZ6KkdHfMnPkjXcNSI6YD7xz8D5O+/btoqZmYtPQJmu8a5xk5jgafbZnTzOf/eyn2LHjG9P2F+IjjzxEQ0MD55134ajHj+bvwXTTfiW5R32We9RnuUX9lT0DiST7WrtSU+2GFEpdvamp9Hl5UFNVRqPfTWDIVLtFjdXqsywZso/TAlIp3uOiESeRo6Curp7LLruC1tYWPB7v2BdMgsfj4dxz3zct9xYRERHo6ctEfw+GNjRHOxlIpP4BtqjAQYPPxbuO8aUKJL+LBq+LYkV/zwoqnHLMtddeOSI04NhjV3DLLbdlqUWT86tfvcCOHV8d8fp1190w5hTBg7W17efmm7eNeH3TptP4q7/6yKTbONUOFS8+VQ5OURQREZHJa4/3Dp9qF44RaesmM6fJVVpI0O/izPUNBH2pkSR/VSn5Dq1Hmq1UOOWYySa7zTQnnrhxwgXSoVRWVvH449+ZknuJiIjI3JK0LCJt3cPWIoXCcTo6BxNxPRUlNPrdnLiihqDfTdDnotJdrPVIc8ycKZwsy9IfbpmzZtlaRhERkUnpH0hFf9tT7cJxmiJxevtTs3nyHXnUeZysXFCVKpD8LgI+F2UlhVluucwEc6JwKigoorOzA6ezXMWTzDmWZdHZ2UFBwdjx9CIiIrNFZ09/qjAKx9gVjtMUibG3tYtEOkCspCifgM/FxlW19lS7Oo+TwgJNtZPRzYnCqbLSS1tblHj8wLQ/y+FwkEwqVS+XzIU+KygoorJyekIpREREssmyLPZ39NpT7DIjSa0dPfY5Fa4iGv1uVi/22Ol23nmlOPQP6jIBc6Jwys8vwOOpPSrPUhxo7lGfiYiI5IZEMhX9PXQtUigco7MnHf0N+KvKWFRfzmnr6gn6XAT8biqcmnUhR25OFE4iIiIiklt6+xI0RYdPtdsd7aQ/vfdiQb6DBq+T4wwfjf5UgRTwuiguUvS3TA8VTiIiIiKSVR2dfSOm2oX3d9nR386SAoJ+N6evqyfoS4U21FSXKfpbjioVTiIiIiJyVCQti5YD3SOm2h2ID0Z/V5eXEPS7OGG5n6DfRdDnpqpc0d+SfSqcRERERGTK9Q8k2dPSaRdITeEYoUicnr5U9LcjL486TxnHNFbZU+2CfhdORX/LDKXCSURERESOSFfPAE1Dp9pF4uxp6bSjv4sLU9HfJ2U2kPW7qPc4KSzQeiTJHSqcRERERGRcLMuiLdZLKJIqkJrCcXaFY7S0D0Z/lzuLCPpdrFxYnZpq53fjq1T0t+Q+FU4iIiIiMkIyabFvf5c9gpQJbYh399vn+CtLmV9bzqY1dQR8bhr9LipcxVlstcj0UeEkIiIiMsf19ifYHY3TNGSq3e5InD47+juPeo+LtUs89lS7Bq+L0mJ9lJS5Q3/aRUREROaQWFffiKl2+/Z3YaWzv8uKCwj6XWxaU29PtautLqMgX9HfMrepcBIRERGZhSzLoqW9x55ilxlJaov12udUlRcT9Lk5fpkvNZLkc1FdUaLob5FRqHASERERyXEDiWRqBCmSGkEKheM0ReJ09w4AkJcHddVOjOA8ewPZoN+Nq1TR3yLjpcJJREREJId09w7QNCSsIRSJsaeli4FEaj1SUaGDgNfFhuV+An4XjX439R4nRYWK/hY5EiqcRERERGYgy7I4EO+jKRJjV2YD2XCcyIFu+xx3WSFBv5sLTq3B4y4m6HfhryzD4dBUO5GppsJJREREJMuSSYtwW5c9ghRKF0odXYPR3755pQT8Lk5eVUvQl5pqN89VRF5eHl6vm2g0lsV3IDL7qXASEREROYr6+hM0t3QOm2q3O9JJb38CgHxHHvUeJ6sWeeypdg1eF2Ul+tgmkk36DRQRERGZJvHufprC6al26ZGkva1dJNPZ36XF+QR8bk5ZVWvvj1TncSr6W2QGUuEkIiIicoQsy6K1o2cw9jtdKLV2DEZ/V7qLCfpcrF3qpdHvIuB346kowaHob5GcoMJJREREZAIGEkn2tXbZa5EyMeCdPYPR3zVVZSxumMfpfhdBn5uA30V5WVGWWy4iR0KFk4iIiMgh9PQNsDvSya5wzE63a4522tHfhQUOGrwujl/mI5CeatfgcVFcpOhvkdlGhZOIiIgI0B7vJTR0f6RwjEhbN1b6uKu0kKDfxZnHNRBMT7WrqSol36H1SCJzgQonERERmVOSlkW0rTs9ihRPfQ3Hae/ss8/xVJQQ9Ls5cUUNQV9qJKnSXUye1iOJzFkqnERERGTW6h9I0twSHwxtiMRpisTp7RuM/q6tdrJiQRUBvzsV2uBzUVZSmOWWi8hMo8JJREREZoXOnn6ahhRIoXCMva1dJJKpyXbFRfkEfS42rqgl6E9tIFvncVJYoKl2IjK2MQsnwzDuBS4G5gMrTdP8Q/r1pcATQDXQClxlmuZb03VMREREBFLR322xXnuKXWbKXUt7j31OhauIoM/N6sUee38k77xSRX+LyKSNZ8TpB8ADwPMHvf4I8BXTNJ80DOMKYAdw+jQeExERkTkmkcxEfw/dHylOvLsfgDzAV1XGwrpyNq2po9HvJuB3U+FU9LeITK0xCyfTNF8AMAzDfs0wDB+wDjgr/dI/Aw8ZhuEl9XfYlB4zTTM62TcoIiIiuaG3L8Hu6PCpdrujnfQPpKK/C/IdNHidrFvqtafaNXidlBRp5YGITL/J/k0TAJpN00wAmKaZMAxjT/r1vGk4psJJRERkFuno7Buxgey+1i47+ttZUkDQ7+a0tfXpUSQXtdVliv4WkayZlf9EU13tyurzvV53Vp8vE6c+yz3qs9yjPss9U9FnyaRFeH8Xf25u58972lNfm9vZ3zG4HslXWcqCugpOOy7AgvoKFtZX4J1XqujvCdLvWO5Rn+WWyRZOTUC9YRj56ZGhfKAu/XreNBybkNbWOMmkNfaJ08DrdRONxrLybJkc9VnuUZ/lHvVZ7plMnw0kkjRHO+2RpKZwjKZonO7eVPS3Iy+PWk8ZRmCePdUu4HPhKi08+Ea0tMSn6q3MCfody7Y5HY4AABtUSURBVD3qs+xxOPImNdAyqcLJNM2IYRgvA5cDT6a//i6zFmk6jomIiMjM0dUzQFNmql36656WzsHo78J8Aj4XG46tSU2187lo8DopLMjPcstFRCZnPHHkDwIXATXAfxuG0Wqa5rHAFuAJwzA+DbQBVw25bDqOiYiIyFFmWRYH4n3p6O/BQil6YHCqXXlZIUG/m5ULq+2RJN+8UhwOTbUTkdkjz7KyM6VtmswH3tFUPZkI9VnuUZ/lHvVZbkgmLfbt7yIUidHS0Ye5s5Vd4cHob0itRwr63QR9Lnt/pHmu4iy2WkC/Y7lIfZY9Q6bqLQB2jve6WRkOISIiIofX259IrUcaGv0didM3JPq73uNkzRKPPdUu4HNRWqyPDiIyN+lvPxERkVku1tVHKBKnKTy4R9Le1k4yk05Kiwto9LvYtKbenmq3apmftv2d2W24iMgMosJJRERklrAsi5b2nlRxNKRIaov12udUlRcT9LlZb3gJ+Nw0+l1UV5SMiP4uyNd+SSIiQ6lwEhERyUEDiSR7WjppisTTwQ1xQpE43b0DAOTlQW21EyM4j6AvtRYp4HPhLivKcstFRHKTCicREZEZrrt3gKZIfNh6pD0tnQwkUnPtigocBHwuTljuT02187lp8DopKlT0t4jIVFHhJCIiMkNYlkV7Z9+IqXaRtm77HFdpIY1+F2etDxDwu2j0u/FXlin6W0RkmqlwEhERyYL/3969xsaV3vcd//J+OyNSImdGIjWj7k3PRnvRXuNde23XziZpghhua9eX+hInCBC7uaBN3BoInN4At4bjIjfbXSNpUidODBgwagcFavdF6jpbp0Dq2rXdtE/jrFeitFreJFEc3smZvjiHQ2q1Eiledjjk9wMIIuc5M/NIj45Gfz3/8zvVWo2xy3PXt9qNzXBtbj36Oz/QTbmY4zX3H8+iv3MMJJ03XI8kSdp7Fk6SJO2x5ZVVLtwQ/T3L4vIqAG2tLYwM9fHAXYP1eySVCjl6u/2YlqT9wr+RJUnaRZX5ZUY3FEjnxytcmpyjmmV/93S1USrkeO2DJ+o3kB0e6jPFTpL2OQsnSZK2oVarMXVtoX4t0lp4w9S19ejvo7kuSoWEh+/JUy4klI/nGOrvptVWO0lqOhZOkiRtYmW1yotTc5wfn7muUJpdyKK/geODvdx9coA3FtIbyJYKCUf6jP6WpIPCwkmSpA0Wlla4MD6bBjaMz3BurMLFiVlWVqsAdLS3cjKf8Ni9hfr1SCfzCV2dRn9L0kFm4SRJOrTWo7+znaTxCuOX56hl433d7ZSLOZ5+9CSlYrqTdPxYD22tXo8kSYeNhZMk6cCr1mpMXJlfD2zI2u2mZ5fqxwz1p9HfT54p1kMbjua6jP6WJAEWTpKkA2Z5pcoLk7P1eyOdG0+vR1pcWo/+PjHYx313HKu32pWLCb3dHQ2euSRpP7NwkiQ1rbmF5XqL3dpO0qWpWVarabNdV2cbpULCU/efoFRMOFXMMTzUS0e71yNJkm6PhZMkad+r1WpcmVmst9itFUqT0wv1Y/r7OikXc5y9e/0msvmjPUZ/S5J2hYWTJGlfWa2uRX+v7yKNjleozC8DafR34Vgvdw4f4fUPDdeLpP6kq7ETlyQdaBZOkqSGWVxa5cLE9btIFyZmWV5Jo7/b21o5me/jkdNDWYGU42Shj+5OP74kSa8sP3kkSa+Ia3Np9PfoWCW7R1KFFy/PUcuyv/u62ykVEt7w8AjlevR3L+1tRn9LkhrPwkmStKtqtRoTV+ez0Ia01e7CxCyXr61fjzR4pItyMcfj9xY4VcxRKiYMHuk2+luStG9ZOEmStm1l9fro7/NjM4xOVJhfTKO/W1taODHUy4P3DFHs76ZcSCgVcyQ9Rn9LkpqLhZMkaUvmFlYYHV+/Fml0rMLFyfXo786OVkqFhCfuO57dGynHyFAfnR1t5PM5JiZmGvwrkCRp+yycJEnXqdVqXK0sZYl264XSxNX1VrsjvR2Uiznuu/NY2mpXSCge7aW11VY7SdLBZOEkSYdYtVpj7Mrcda1258crzMwt148pHO3hVDHHax8croc29Pd1ej2SJOlQsXCSpENiaXmVCxOz10d/j1dYyqK/21pbGMn3cfbuoXqrXamQ0NPlR4UkSX4aStIBVJlfvmEX6dLUbD36u6ernXIh4fUPpdHfpULC8FCf0d+SJN2EhZMkNbFarcbk9EIa/Z3dG+nc2AxXZhbrxxzNdXGqmOPR0/n0JrLFhKF+o78lSbodFk6S1CRWVqtcmprLQhvWd5LmF1cAaGmBE4N9hNJA2mZXTCgXEnK9nQ2euSRJzc/CSZL2ofnFFUbHK/UdpDT6u8LKahb93d7KyULCq84U16O/8310dbQ1eOaSJB1MFk6S1GBXK4vX7SCdH5th/Mp8fTzp6eBUMeHpx0ppql0hx/FjRn9LkvRKsnCSpFdItVZj/Mr8Da1212aX6sfkB7opF3K85v7jlIo5ThVzDCRGf0uS1GgWTpK0B5ZXro/+Hh1L2+4Wl1eBNPp7eKiPB+48RrmQy5LtcvR2+9eyJEn7kZ/QkrRDlfllRje02Z0fr3Bpco5qlv3d3dlGuZDw1IMn6q12w0N9dLQb/S1JUrOwcJKkLarVakxdW2B0LAtsyAqlqWvr0d8DSSflYo6H7xmq7yQNDfTQaqudJElNbceFUwjheWAh+wHwoRjjV0IITwCfBnqA54F3xxjHs+dsa0ySXimr1Rujv0fHK8wuZNHfwPHBXu4a6eeNj6xFf+c40mf0tyRJB9Fu7Ti9Ncb43bVvQggtwGeB98UYnw0hfBj4KPDT2x3bpXlK0g0Wlla4MD7L+fGZeqF0YWKWldUqAB3trZzM9/HYvYV69PfJfEJXp9HfkiQdFnvVqvcYsBBjfDb7/hnS3aOf3sGYJO3Y9OwSo2Mz9Va7c2MVxi/PUcvG+7rbKRdz/NCjI5SLOcqFhOODvbS1ej2SJEmH2W4VTn+U7RY9C/wKUAbOrQ3GGCdDCK0hhGPbHYsxXt6luUo6BKq1GhNX59djv8cqnB+fYbqyHv091N9NqZDw5JkipWLCqWKOo7kuo78lSdINdqNwem2McTSE0AX8BvAJ4D/swutu2+Bg0si3J5/PNfT9dftcs+azcc2WV1Y59+IM3784zXMXp3nuhWm+/8I15hfT65FaW1soF3M8em+RO4b7uWuknzuGj5D0ej3SK8nzrPm4Zs3F9Wo+rllz2XHhFGMczX5eDCF8CvgT4DeBU2vHhBCGgFqM8XII4fx2xm5nTlNTFarV2uYH7oF8PsfExExD3lvb45o1l7mFZa4tVfl2HM9a7ipcmpplNTvnuzrb0l2k+4ppq10xYWSoj472669Hmp9dZH528eXeQnvA86z5uGbNxfVqPq5Z47S2tmxro2VHhVMIoQ9ojzFOZ6167wC+BXwD6AkhPJVdr/R+4PPZ07Y7JukQqdVqXJlZXG+1y6K/J6cX6sf096XR32fvHqRUSFvt8keN/pYkSbtvpztOReALIYQ2oA34S+AfxBirIYT3AJ8OIXSTxYoDbHdM0sG1Wq3y4uX5NPJ7wz2SKvPLQBr9XTjWyx0njvD6h4Z54HSB/q42+pOuxk5ckiQdGjsqnGKMzwEP32Ts68ADuzkmqfktLq9yYbxS30FKo78rLK+k0d/tbS2M5BMeOT1EqZDjVDHHSL6Pnq71v65sb5AkSa+0vYojlySuzS0x+pJWuxcvz1HLLkHs7WqnXEx4w8MjlLMbyB4f7KW9zehvSZK0v1g4SdqxWq3GxPQC519cL5BGxytcmVkPXxg80kWpkOPxewv10IbBI91Gf0uSpKZg4STptqysVnlhcva60IbR8RnmF1cBaG1p4cRgL/eWB7JWu4RSMUfS09HgmUuSJG2fhZOkm5pfXFkvjrJC6eLkevR3Z0crpULCE2eOp612xRwjQ310drRt8sqSJEnNxcJJErVajauVpSysYb3dbuLqevR3rreDcjHHj9x5jHIhbbUrHu2ltdVWO0mSdPBZOEmHTLVaY+zKXBr5vaHdbmZuuX5MYaCHU8UcTz04nLbaFXIMJJ1ejyRJkg4tCyfpAFtaXuXCxCznx9PY79GxGUYnKiwtp9Hfba0tjOT7OHv3EOVC2mpXKiTXRX9LkiTJwkk6MCrzy/X7Iq0VSpemZuvR3z1d7ZQLCa87O8yprEAaHuoz+luSJGkLLJykJlOr1ZiaXuDcWJpmt1YoXb62Hv19NNdFuZDw6Ol8PbRhqN/ob0mSpO2ycJL2sZXVKpem5uo7SWuF0tziCgAtLXD8WC+nTw5QKq632h3p7WzwzCVJkg4WCydpn5hfXOHCRGX9/khjFS5OVlhZzaK/21s5WUj4wR9IbyBbKiaczCd0Gf0tSZK05yycpAaYrizWW+3OZaEN41fmyS5HIunpoFxMePqxUj20oXish7ZWr0eSJElqBAsnaQ9VazXGr8zfENpwbXapfsxQfzenijmevP845WKOciHhaK7L65EkSZL2EQsnaZcsr6TR36Pjlfo9kkbHKywurwJp9PfwUB8P3HEsLZCKCaVCQm93R4NnLkmSpM1YOEnbMLuwfN21SOfHZ7g0OUc1y/7u7myjVEh46sET9Va74aE+OtpttZMkSWpGFk7SLdRqNS5fW0wLpPH1Qmnq2kL9mIGkk3Ixx8P3DFEupKEN+YEeWm21kyRJOjAsnKTMajWN/h4dy1rtskJpdiGL/gaKx3q5a+QIb3hkJGu1y9HfZ/S3JEnSQWfhpENpYWmFCxOznB+bYeLaIvH5y1yYmGVltQpAR3srJ/N9PBoKnComlIo5SvmErk6jvyVJkg4jCycdeNOzS4y+pNVu7PLcddHfpULCDz06QrmQhjYcH+w1+luSJEl1Fk46MKq1GhNX528IbZiurEd/Dx7pplxMeNWZIuViQrmQI9w1xORkpYEzlyRJ0n5n4aSmtLxS5YXJ2esKpNHxCgtLafR3a0sLw0O9nDl1rN5qVy4m9L1M9Lf3S5IkSdJmLJy0780tLGf3RqowOjbDubEKl6ZmWa2mzXZdHWn096vXbiBbTBgZ6qOj3euRJEmStDssnLRv1Go1rsws1neQ1lruJqfXo7+P9HVSLiY8eNdg2mpXzFE4avS3JEmS9paFkxqiWq1x6fJcGtqwoVCqzC/Xjyke7eGOE0d4/UPD6U5SIaE/6WrgrCVJknRYWThpzy0ur3JhosL5Da12FycqLK2k0d/tbS2M5JP0BrJZq93JfEJPl388JUmStD/4L1Ptqpm5pRta7V68PEcty/7u7WqnXEz4mw+PUCoknCrmOD7YS3ub0d+SJEnavyyctC21Wo2J6YX6DtLafZKuzCzWjzl2pItyIcfj9xbqrXaD/d2m2EmSJKnpWDhpUyura9Hf6ztJo+MzzC+m0d8tLTA82EcoD9RvIFsu5kh6boz+liRJkpqRhZOuM7+4kkV/zzCatdpdnFyP/u7saKWUT3jizHFKxbTVbmSoj84Oo78lSZJ0cFk4HVK1Wo2rlaX0BrLjaYE0OlZh/Op8/ZhcbwflYo4fueNYPbSheLSX1lZb7SRJknS4WDgdAtVqjbErc/WwhrVCaWZuPfq7MNBDuZjwmgdPUC6krXYDSafXI0mSJElYOB04S8urXJycva7VbnSiwtJyGv3d1trCSL6Ps3cN1a9FKhWM/pYkSZJuxX8tN7HK/HK6g5SFNoyOVbg0NUc1y/7u6WqjVMjxurPD9dCG4aE+o78lSZKk22Th1ARqtRpT0wv1Fru1QunytfXo76O5LsqFhIdP5zlVTCgVc+SN/pYkSZJ2hYXTPrOyWuXFqbm01W5DoTS3uAKk0d/Hj/Vyz8mBtNWukKNUTDjS29ngmUuSJEkH174snEIIp4HPAIPAFPDeGONfNXZWu29+cYULE5XrQhsuTsyysppej9TR3srJfMIP/kCBUpZqdzKf0GX0tyRJkvSK2peFE/AM8MkY42dDCO8GPg28scFz2pHpyiLnshvHnhurMDo2w/iVeWrZeNLTQbmY8PSjJylnrXbHj/XQ1ur1SJIkSVKj7bvCKYRQAB4Bfjh76HPAJ0II+RjjRONmdntWq1W+/ddTfOe5y3z3uSkmpxfqY0P93ZSLOZ68/3g9tOForsvrkSRJkqR9at8VTkAJuBhjXAWIMa6GEF7IHm+awunPvn2JP/hypKuzjTOnjvL0Y6U0tKGQ0Nvd0ejpSZIkSboN+7Fw2rHBwaSh75/P5/iJ193N2VDkjuF+Otptt9vv8vlco6eg2+SaNR/XrPm4Zs3F9Wo+rllz2Y+F0ygwEkJoy3ab2oDh7PEtmZqqUK3WNj9wD+TzOSYmZgA42tPO1SuzDZmHtm7jmqk5uGbNxzVrPq5Zc3G9mo9r1jitrS3b2mjZd1shMcZx4FvAO7OH3gl8s5mub5IkSZJ0sOzHHSeA9wOfCSH8U+AK8N4Gz0eSJEnSIbYvC6cY4/8FXtXoeUiSJEkS7NPCaQfaIO1bbKRGv79un2vWfFyz5uOaNR/XrLm4Xs3HNWuMDb/vbbfzvJZarTEhCnvkKeDPGj0JSZIkSfvea4Fnt3rwQSucuoDHgUvAaoPnIkmSJGn/aQNOAH8BLG71SQetcJIkSZKkXbfv4sglSZIkab+xcJIkSZKkTVg4SZIkSdImLJwkSZIkaRMWTpIkSZK0CQsnSZIkSdqEhZMkSZIkbaK90RM4SEIIp4HPAIPAFPDeGONfNXZWh08I4XlgIfsB8KEY41dCCE8AnwZ6gOeBd8cYx7PnbGtM2xNC+DjwFuBvAA/EGL+bPX7Tc2gvxrR1t1iz53mZ8y0b85xrkBDCIPCHwF2kN3f8HvCzMcaJvVgX12znNlmzGvAdoJod/p4Y43ey570J+DXSf9N9A/ipGOPcTsa0dSGELwJ3kK5NBfiFGOO3/Dw7mNxx2l3PAJ+MMZ4GPkn6IaLGeGuM8aHsx1dCCC3AZ4Gfy9bna8BHAbY7ph35IvA64NxLHr/VObQXY9q6m60ZvOR8g+2fV55zu6YGfCzGGGKMDwJ/DXx0L9bFNds1L7tmG8ZfveE8WyuaEuB3gDfFGO8GZoAP7mRMt+0nY4xnY4wPAx8Hfi973M+zA8jCaZeEEArAI8Dnsoc+BzwSQsg3blba4DFgIcb4bPb9M8DbdjimbYoxPhtjHN342K3Oob0Y26tf20H1cmu2Cc+5BooxXo4xfnXDQ/8dOMXerItrtgtusWa38mPA/9iw6/AM8PYdjuk2xBinN3zbD1T9PDu4LJx2Twm4GGNcBch+fiF7XK+8PwohfDuE8KkQwgBQZsP/lMcYJ4HWEMKxHYxpd93qHNqLMe2el55v4Dm3b4QQWoEPAH/C3qyLa7bLXrJma74aQvhWCOFfhxC6sseu+70HzrP+99t2x3SbQgi/G0I4D3wE+En8PDuwLJx0EL02xngWeBxoAT7R4PlIB5nn2/7326TXXrg2zeOla1aOMT5G2i57BvjVRk1MN4ox/kyMsQz8Cul1YzqgLJx2zygwEkJoA8h+Hs4e1ytorZ0oxrgIfAp4Den/ptVbHkIIQ0Atxnh5B2PaXbc6h/ZiTLvgJucbeM7tC1moxz3A22OMVfZmXVyzXfQya7bxPLsG/C43Oc9Id5JGdzimbYox/iHwBuACfp4dSBZOuyRLD/oW8M7soXcC34wxTjRuVodPCKEvhNCffd0CvIN0Xb4B9IQQnsoOfT/w+ezr7Y5pF93qHNqLsb3/FR18tzjfwHOu4UIIHwEeBf52VtjC3qyLa7ZLXm7NQghHQwg92dftwFtZP8++DDweQrgn+37j7/12x7RFIYQkhFDa8P2bgMuAn2cHVEutVmv0HA6MEMK9pDGRR4ErpDGRsbGzOlxCCHcCXwDash9/CfxijPFSCOHVpAk03azH5Y5lz9vWmLYnhPBbwN8FjgOTwFSM8b5bnUN7Maate7k1A97ETc637Dmecw0SQrgP+C7w/4D57OHvxxj/zl6si2u2czdbM+BjpL+3NaAD+DrwD2OMlex5b86OaQO+Cbwvxji7kzFtTQihCHwJ6ANWSYumD8YY/6efZweThZMkSZIkbcJWPUmSJEnahIWTJEmSJG3CwkmSJEmSNmHhJEmSJEmbsHCSJEmSpE1YOEmS9qUQwjMhhF+9xXgthHD3Lr/nu0II/3k3X1OSdDAYRy5J2nMhhHcA/wi4H5glvT/NZ4B/G2Pc1gdRCKEG3BNj/N7LjH0VeAJYARaArwE/t3aPqd0QQngf8DMxxqc2O1aS1PzccZIk7akQwi8Dvwn8GukNdIvA+4HXAJ03eU7bLrz1z8cYE+A0MAD8+i68piTpkGpv9AQkSQdXCKEf+Jekd7j/woahbwLv2nDcvwfmgVPA64E3hxDeDVyIMX44O+YfA78E1IAPb3UOMcbLIYQvAB/YMKffBn4MmAN+B/hXMcbqS3eRsl2tDwC/DAwBfwz8PHAv8AzQEUKoACsxxoEQwo8DHwdKwDXg12OMH9/qXCVJ+5c7TpKkvfQk0AV8aQvH/n3gI0AOeHbjQAjhbwEfBH4YuAd4eqsTCCEMAW8hLdYgLZr6gTtJi7T3Aj91i5f4CeBx4CzwNuBHY4z/h3TX7M9jjEmMcSA79t8BPxtjzJG2Jf7pVucpSdrf3HGSJO2lIWAyxriy9kAI4evAGdKC6kdjjF/Lhr4UY/xv2dcLIYSNr/M24PdjjN/NXuOfA+/c5L1/K4TwcdJrqr4K/FLWAvh24OEY4wwwE0L4N8B7SIuel/PRGONV4GoI4b8ADwFfvsmxy8CZEML/ijFeAa5sMkdJUpNwx0mStJemgKEQQv0/6mKMr852aKa4/nNo9BavM/yS8XNbeO9fjDEOxBhHYozvijFOkBZynS95/jlg5Bav8+KGr+eA5BbHvgX4ceBcCOG/hhCe3MI8JUlNwMJJkrSX/hxYBN68hWNvla53ifS6oTXlbc5nknRX6NRLXuviNl7rhvnGGP8ixvhmoAB8Efj8diYpSdp/bNWTJO2ZGOPVEMK/AD4VQmghbXGbAx4E+m7jpT4P/H4I4Q+A54F/ts35rIYQPg98JITwXuAYaeDEdgIcxoCTIYTOGONSCKET+HvAf4wxTocQrgGr25mnJGn/ccdJkrSnYowfIy1O/gkwTlpwfBr4EPD1Lb7GfwJ+gzRs4XvsLHThF0ive3qONITij4Hf28br/Cnwv4EXQwiT2WPvAZ7Piqb3A+/ewTwlSfuIN8CVJEmSpE244yRJkiRJm7BwkiRJkqRNWDhJkiRJ0iYsnCRJkiRpExZOkiRJkrQJCydJkiRJ2oSFkyRJkiRtwsJJkiRJkjZh4SRJkiRJm/j/+ZOl1FsRhfUAAAAASUVORK5CYII=
+"
+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div><div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Also here seems to be a linear correlation. Let's do our fitting and plot directly.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [34]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">_fit</span><span class="p">,</span> <span class="n">_cov</span> <span class="o">=</span> <span class="n">common</span><span class="o">.</span><span class="n">print_and_return_fit</span><span class="p">(</span>
+ <span class="p">[</span><span class="s2">"PM_VECTOR_LD_CMPL (min)"</span><span class="p">,</span> <span class="s2">"PM_VECTOR_ST_CMPL (min)"</span><span class="p">],</span>
+ <span class="n">df_vldvst</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"Grid Points"</span><span class="p">),</span>
+ <span class="n">linear_function</span><span class="p">,</span>
+ <span class="n">format_value</span><span class="o">=</span><span class="s2">".4f"</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">fit_parameters</span> <span class="o">=</span> <span class="p">{</span><span class="o">**</span><span class="n">fit_parameters</span><span class="p">,</span> <span class="o">**</span><span class="n">_fit</span><span class="p">}</span>
+<span class="n">fit_covariance</span> <span class="o">=</span> <span class="p">{</span><span class="o">**</span><span class="n">fit_covariance</span><span class="p">,</span> <span class="o">**</span><span class="n">_cov</span><span class="p">}</span>
+</pre></div>
+
+ </div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+ <div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Counter PM_VECTOR_LD_CMPL (min) is proportional to the grid points (nx*ny) by a factor of 2.3439 (± 0.000111)
+Counter PM_VECTOR_ST_CMPL (min) is proportional to the grid points (nx*ny) by a factor of 0.4688 (± 0.000012)
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [35]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="p">(</span><span class="n">ax1</span><span class="p">,</span> <span class="n">ax2</span><span class="p">)</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="n">df_vldvst</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"nx"</span><span class="p">)[</span><span class="s2">"Vector Loads / Loop Iteration"</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">True</span><span class="p">);</span>
-<span class="n">df_vldvst</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"nx"</span><span class="p">)[</span><span class="s2">"Vector Stores / Loop Iteration"</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax2</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">True</span><span class="p">);</span>
+<span class="k">for</span> <span class="n">ax</span><span class="p">,</span> <span class="n">pmu_counter</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">([</span><span class="n">ax1</span><span class="p">,</span> <span class="n">ax2</span><span class="p">],</span> <span class="p">[</span><span class="s2">"PM_VECTOR_LD_CMPL (min)"</span><span class="p">,</span> <span class="s2">"PM_VECTOR_ST_CMPL (min)"</span><span class="p">]):</span>
+ <span class="n">df_vldvst</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"Grid Points"</span><span class="p">)[</span><span class="n">pmu_counter</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">True</span><span class="p">);</span>
+ <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
+ <span class="n">df_vldvst</span><span class="p">[</span><span class="s2">"Grid Points"</span><span class="p">],</span>
+ <span class="n">linear_function</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s2">"Grid Points"</span><span class="p">],</span> <span class="o">*</span><span class="n">fit_parameters</span><span class="p">[</span><span class="n">pmu_counter</span><span class="p">]),</span>
+ <span class="n">linestyle</span><span class="o">=</span><span class="s2">"--"</span><span class="p">,</span>
+ <span class="n">label</span><span class="o">=</span><span class="s2">"Fit: </span><span class="si">{:.2f}</span><span class="s2"> * x + </span><span class="si">{:.2f}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="o">*</span><span class="n">fit_parameters</span><span class="p">[</span><span class="n">pmu_counter</span><span class="p">])</span>
+ <span class="p">)</span>
+ <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">();</span>
</pre></div>
</div>
@@ -16104,7 +16505,7 @@ mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.vst.bin.csv .
<div class="output_png output_subarea ">
-<img 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
"
>
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@@ -16137,14 +16538,14 @@ n_\text{ld}^\text{scalar} &= n_\text{ld}^\text{total} - n_\text{ld}^\text{ve
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-<div class="prompt input_prompt">In [83]:</div>
+<div class="prompt input_prompt">In [37]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_byte</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">()</span>
-<span class="n">df_byte</span><span class="p">[</span><span class="s2">"Loads / Loop Iteration"</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">df_vldvst</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"nx"</span><span class="p">)[</span><span class="s2">"Vector Loads / Loop Iteration"</span><span class="p">]</span> <span class="o">+</span> <span class="n">df_ldst</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"nx"</span><span class="p">)[</span><span class="s2">"Loads / Loop Iteration"</span><span class="p">])</span><span class="o">*</span><span class="mi">8</span>
-<span class="n">df_byte</span><span class="p">[</span><span class="s2">"Stores / Loop Iteration"</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">df_vldvst</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"nx"</span><span class="p">)[</span><span class="s2">"Vector Stores / Loop Iteration"</span><span class="p">]</span> <span class="o">+</span> <span class="n">df_ldst</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"nx"</span><span class="p">)[</span><span class="s2">"Stores / Loop Iteration"</span><span class="p">])</span><span class="o">*</span><span class="mi">8</span>
+<span class="n">df_byte</span><span class="p">[</span><span class="s2">"Loads"</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">df_vldvst</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"Grid Points"</span><span class="p">)[</span><span class="s2">"PM_VECTOR_LD_CMPL (min)"</span><span class="p">]</span> <span class="o">+</span> <span class="n">df_ldst</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"Grid Points"</span><span class="p">)[</span><span class="s2">"PM_LD_CMPL (min)"</span><span class="p">])</span><span class="o">*</span><span class="mi">8</span>
+<span class="n">df_byte</span><span class="p">[</span><span class="s2">"Stores"</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">df_vldvst</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"Grid Points"</span><span class="p">)[</span><span class="s2">"PM_VECTOR_ST_CMPL (min)"</span><span class="p">]</span> <span class="o">+</span> <span class="n">df_ldst</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"Grid Points"</span><span class="p">)[</span><span class="s2">"PM_ST_CMPL (min)"</span><span class="p">])</span><span class="o">*</span><span class="mi">8</span>
<span class="n">ax</span> <span class="o">=</span> <span class="n">df_byte</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">"Bytes / Loop Iteration"</span><span class="p">);</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">"Bytes"</span><span class="p">);</span>
</pre></div>
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@@ -16163,7 +16564,7 @@ n_\text{ld}^\text{scalar} &= n_\text{ld}^\text{total} - n_\text{ld}^\text{ve
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
"
>
</div>
@@ -16173,16 +16574,27 @@ n_\text{ld}^\text{scalar} &= n_\text{ld}^\text{total} - n_\text{ld}^\text{ve
</div>
</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div><div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Let's quantify the difference by, again, fitting a linear function to the data.</p>
+
+</div>
+</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
-<div class="prompt input_prompt">In [12]:</div>
+<div class="prompt input_prompt">In [38]:</div>
<div class="inner_cell">
<div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="n">mean_byte_ld</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">polyfit</span><span class="p">(</span><span class="n">df_byte</span><span class="p">[</span><span class="n">df_byte</span><span class="o">.</span><span class="n">index</span> <span class="o">></span> <span class="mi">200</span><span class="p">]</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">df_byte</span><span class="p">[</span><span class="n">df_byte</span><span class="o">.</span><span class="n">index</span> <span class="o">></span> <span class="mi">200</span><span class="p">][</span><span class="s2">"Loads / Loop Iteration"</span><span class="p">],</span> <span class="mi">0</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-<span class="n">mean_byte_st</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">polyfit</span><span class="p">(</span><span class="n">df_byte</span><span class="p">[</span><span class="n">df_byte</span><span class="o">.</span><span class="n">index</span> <span class="o">></span> <span class="mi">200</span><span class="p">]</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">df_byte</span><span class="p">[</span><span class="n">df_byte</span><span class="o">.</span><span class="n">index</span> <span class="o">></span> <span class="mi">200</span><span class="p">][</span><span class="s2">"Stores / Loop Iteration"</span><span class="p">],</span> <span class="mi">0</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">"Mean byte loaded: </span><span class="si">{}</span><span class="se">\t</span><span class="s2">Mean byte stored: </span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">mean_byte_ld</span><span class="p">,</span> <span class="n">mean_byte_st</span><span class="p">))</span>
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">_fit</span><span class="p">,</span> <span class="n">_cov</span> <span class="o">=</span> <span class="n">common</span><span class="o">.</span><span class="n">print_and_return_fit</span><span class="p">(</span>
+ <span class="p">[</span><span class="s2">"Loads"</span><span class="p">,</span> <span class="s2">"Stores"</span><span class="p">],</span>
+ <span class="n">df_byte</span><span class="p">,</span>
+ <span class="n">linear_function</span>
+<span class="p">)</span>
+<span class="n">fit_parameters</span> <span class="o">=</span> <span class="p">{</span><span class="o">**</span><span class="n">fit_parameters</span><span class="p">,</span> <span class="o">**</span><span class="n">_fit</span><span class="p">}</span>
+<span class="n">fit_covariance</span> <span class="o">=</span> <span class="p">{</span><span class="o">**</span><span class="n">fit_covariance</span><span class="p">,</span> <span class="o">**</span><span class="n">_cov</span><span class="p">}</span>
</pre></div>
</div>
@@ -16199,7 +16611,8 @@ n_\text{ld}^\text{scalar} &= n_\text{ld}^\text{total} - n_\text{ld}^\text{ve
<div class="output_subarea output_stream output_stdout output_text">
-<pre>Mean byte loaded: 37.52662546714877 Mean byte stored: 8.428951320998907
+<pre>Counter Loads is proportional to the grid points (nx*ny) by a factor of 37.5010 (± 0.000592)
+Counter Stores is proportional to the grid points (nx*ny) by a factor of 8.4379 (± 0.000247)
</pre>
</div>
</div>
@@ -16207,6 +16620,14 @@ n_\text{ld}^\text{scalar} &= n_\text{ld}^\text{total} - n_\text{ld}^\text{ve
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+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div><div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Analagously to the proportionality factors, this mich is loaded/stored per grid point.</p>
+
+</div>
+</div>
</div>
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@@ -16218,11 +16639,11 @@ n_\text{ld}^\text{scalar} &= n_\text{ld}^\text{total} - n_\text{ld}^\text{ve
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-<div class="prompt input_prompt">In [13]:</div>
+<div class="prompt input_prompt">In [50]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_bandwidth</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">()</span>
-<span class="n">df_bandwidth</span><span class="p">[</span><span class="s2">"Bandwidth / Byte/Cycle"</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">df_byte</span><span class="p">[</span><span class="s2">"Loads / Loop Iteration"</span><span class="p">]</span> <span class="o">+</span> <span class="n">df_byte</span><span class="p">[</span><span class="s2">"Stores / Loop Iteration"</span><span class="p">])</span> <span class="o">/</span> <span class="n">df</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"nx"</span><span class="p">)[</span><span class="s2">"Cycles / Loop Iteration"</span><span class="p">]</span>
+<span class="n">df_bandwidth</span><span class="p">[</span><span class="s2">"Bandwidth / Byte/Cycle"</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">df_byte</span><span class="p">[</span><span class="s2">"Loads"</span><span class="p">]</span> <span class="o">+</span> <span class="n">df_byte</span><span class="p">[</span><span class="s2">"Stores"</span><span class="p">])</span> <span class="o">/</span> <span class="n">df</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"Grid Points"</span><span class="p">)[</span><span class="s2">"PM_RUN_CYC (min)"</span><span class="p">]</span>
</pre></div>
</div>
@@ -16233,14 +16654,14 @@ n_\text{ld}^\text{scalar} &= n_\text{ld}^\text{total} - n_\text{ld}^\text{ve
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-<p>Let's display it as a function of <code>nx</code>. And also compare it to the available L1 cache bandwidth in a second (sub-)plot. Non-interactive users, call <code>make graph_task2c</code>.</p>
+<p>Let's display it as a function of grid points. And also compare it to the available L1 cache bandwidth in a second (sub-)plot. Non-interactive users, call <code>make graph_task2c</code>.</p>
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-<div class="prompt input_prompt">In [15]:</div>
+<div class="prompt input_prompt">In [51]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="p">(</span><span class="n">ax1</span><span class="p">,</span> <span class="n">ax2</span><span class="p">)</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
@@ -16267,7 +16688,7 @@ n_\text{ld}^\text{scalar} &= n_\text{ld}^\text{total} - n_\text{ld}^\text{ve
<div class="output_png output_subarea ">
-<img src="data:image/png;base64,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
"
>
</div>
@@ -16291,7 +16712,7 @@ n_\text{ld}^\text{scalar} &= n_\text{ld}^\text{total} - n_\text{ld}^\text{ve
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Task-E1:-Measuring-FlOps">Task E1: Measuring FlOps<a class="anchor-link" href="#Task-E1:-Measuring-FlOps">¶</a></h2><p><a name="taske1"></a></p>
<p>If you still have time, feel free to work on the following extended task.</p>
-<p><strong>TASK</strong>: Please measure counters for <em>vectorized</em> floating point operations and <em>scalar</em> floating point operations. The two counters can also not be measured during the same run. So please see the TODOs in <a href="/edit/Tasks/poisson2d.sflops.c"><code>poisson2d.sflops.c</code></a> and <a href="/edit/Tasks/poisson2d.vflops.c"><code>poisson2d.vflops.c</code></a>. By now you should be able to find out the names of the counters by yourself (<em>Hint: they include the words scalar and vector…</em>).</p>
+<p><strong>TASK</strong>: Please measure counters for <em>vectorized</em> floating point operations and <em>scalar</em> floating point operations. The two counters can also not be measured during the same run. So please see the TODOs in <a href="/edit/Tasks/poisson2d.sflops.c"><code>poisson2d.sflops.c</code></a> and <a href="/edit/Tasks/poisson2d.vflops.c"><code>poisson2d.vflops.c</code></a>. By now you should be able to find out the names of the counters by yourself (<em>Hint: they include the words »scalar« and »vector«…</em>).</p>
<p>As usual, compile, test, and bench-run your program.</p>
<p><a href="#toc">Back to top</a></p>
@@ -16300,7 +16721,7 @@ n_\text{ld}^\text{scalar} &= n_\text{ld}^\text{total} - n_\text{ld}^\text{ve
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
-<div class="prompt input_prompt">In [42]:</div>
+<div class="prompt input_prompt">In [4]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">!</span>make bench_task4
@@ -16320,8 +16741,8 @@ n_\text{ld}^\text{scalar} &= n_\text{ld}^\text{total} - n_\text{ld}^\text{ve
<div class="output_subarea output_stream output_stdout output_text">
-<pre>bsub -W 60 -nnodes 1 -Is -P GEN110 jsrun -n 1 -c 1 -g ALL_GPUS ./bench.sh poisson2d.sflop.bin /gpfs/wolf/gen110/scratch/aherten//poisson2d.sflop.bin.csv
-Job <4299> is submitted to default queue <batch>.
+<pre>bsub -W 60 -nnodes 1 -Is -P TRN003 jsrun -n 1 -c 1 -g ALL_GPUS ./bench.sh poisson2d.sflop.bin /gpfs/wolf/trn003/scratch/aherten//poisson2d.sflop.bin.csv
+Job <24645> is submitted to default queue <batch>.
<<Waiting for dispatch ...>>
<<Starting on login1>>
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
@@ -16335,7 +16756,7 @@ iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCA
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
200,32,20,0.0013,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,24,0.0014,0,0,0
+200,32,24,0.0013,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
200,32,28,0.0014,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
@@ -16351,21 +16772,21 @@ iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCA
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
200,32,52,0.0018,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,56,0.0019,0,0,0
+200,32,56,0.0022,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,60,0.0020,0,0,0
+200,32,60,0.0019,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
200,32,64,0.0021,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
200,32,68,0.0022,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,72,0.0022,0,0,0
+200,32,72,0.0021,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
200,32,76,0.0022,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
200,32,80,0.0023,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,84,0.0024,0,0,0
+200,32,84,0.0025,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
200,32,88,0.0024,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
@@ -16373,39 +16794,39 @@ iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCA
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
200,32,96,0.0025,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,100,0.0028,0,0,0
+200,32,100,0.0026,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
200,32,104,0.0027,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
200,32,108,0.0027,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,112,0.0029,0,0,0
+200,32,112,0.0028,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
200,32,116,0.0028,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,120,0.0029,0,0,0
+200,32,120,0.0031,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
200,32,124,0.0030,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,128,0.0031,0,0,0
+200,32,128,0.0030,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
200,32,132,0.0031,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
200,32,136,0.0032,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,140,0.0033,0,0,0
+200,32,140,0.0032,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,144,0.0034,0,0,0
+200,32,144,0.0033,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
200,32,148,0.0034,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,152,0.0034,0,0,0
+200,32,152,0.0035,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
200,32,156,0.0035,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
200,32,160,0.0036,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,164,0.0037,0,0,0
+200,32,164,0.0036,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
200,32,168,0.0037,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
@@ -16415,13 +16836,13 @@ iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCA
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
200,32,180,0.0039,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,184,0.0039,0,0,0
+200,32,184,0.0040,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
200,32,188,0.0040,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
200,32,192,0.0041,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,196,0.0041,0,0,0
+200,32,196,0.0042,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
200,32,200,0.0042,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
@@ -16433,9 +16854,9 @@ iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCA
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
200,32,216,0.0045,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,220,0.0046,0,0,0
+200,32,220,0.0045,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,224,0.0047,0,0,0
+200,32,224,0.0046,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
200,32,228,0.0047,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
@@ -16447,11 +16868,11 @@ iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCA
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
200,32,244,0.0049,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,248,0.0050,0,0,0
+200,32,248,0.0051,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,252,0.0050,0,0,0
+200,32,252,0.0051,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,256,0.0051,0,0,0
+200,32,256,0.0053,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
200,32,260,0.0052,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
@@ -16459,79 +16880,79 @@ iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCA
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
200,32,268,0.0054,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,272,0.0055,0,0,0
+200,32,272,0.0054,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,276,0.0055,0,0,0
+200,32,276,0.0054,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
200,32,280,0.0055,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
200,32,284,0.0056,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,288,0.0057,0,0,0
+200,32,288,0.0056,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
200,32,292,0.0057,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
200,32,296,0.0058,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,300,0.0059,0,0,0
+200,32,300,0.0058,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
200,32,304,0.0059,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,308,0.0059,0,0,0
+200,32,308,0.0060,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
200,32,312,0.0060,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,316,0.0061,0,0,0
+200,32,316,0.0062,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,320,0.0061,0,0,0
+200,32,320,0.0062,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
200,32,324,0.0062,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
200,32,328,0.0063,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,332,0.0065,0,0,0
+200,32,332,0.0064,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,336,0.0064,0,0,0
+200,32,336,0.0065,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
200,32,340,0.0065,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,344,0.0065,0,0,0
+200,32,344,0.0066,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
200,32,348,0.0066,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
200,32,352,0.0067,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,356,0.0067,0,0,0
+200,32,356,0.0068,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,360,0.0068,0,0,0
+200,32,360,0.0069,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
200,32,364,0.0069,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
200,32,368,0.0070,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,372,0.0070,0,0,0
+200,32,372,0.0072,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
200,32,376,0.0071,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,380,0.0072,0,0,0
+200,32,380,0.0071,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
200,32,384,0.0072,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,388,0.0072,0,0,0
+200,32,388,0.0073,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,392,0.0075,0,0,0
+200,32,392,0.0074,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,396,0.0074,0,0,0
+200,32,396,0.0076,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
200,32,400,0.0075,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,404,0.0075,0,0,0
+200,32,404,0.0076,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
200,32,408,0.0076,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
200,32,412,0.0077,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,416,0.0077,0,0,0
+200,32,416,0.0078,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
200,32,420,0.0078,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
@@ -16541,27 +16962,27 @@ iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCA
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
200,32,432,0.0080,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,436,0.0080,0,0,0
+200,32,436,0.0081,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,440,0.0081,0,0,0
+200,32,440,0.0082,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,444,0.0083,0,0,0
+200,32,444,0.0082,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
200,32,448,0.0084,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,452,0.0084,0,0,0
+200,32,452,0.0083,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
200,32,456,0.0084,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
200,32,460,0.0085,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,464,0.0086,0,0,0
+200,32,464,0.0085,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
200,32,468,0.0086,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,472,0.0088,0,0,0
+200,32,472,0.0087,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,476,0.0087,0,0,0
+200,32,476,0.0089,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
200,32,480,0.0088,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
@@ -16571,7 +16992,7 @@ iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCA
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
200,32,492,0.0090,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,496,0.0090,0,0,0
+200,32,496,0.0091,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
200,32,500,0.0092,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
@@ -16579,266 +17000,266 @@ iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCA
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
200,32,508,0.0093,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,512,0.0092,0,0,0
+200,32,512,0.0094,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,516,0.0093,0,0,0
+200,32,516,0.0094,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,520,0.0094,0,0,0
+200,32,520,0.0095,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,524,0.0094,0,0,0
+200,32,524,0.0096,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,528,0.0094,0,0,0
+200,32,528,0.0096,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,532,0.0095,0,0,0
+200,32,532,0.0098,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,536,0.0096,0,0,0
+200,32,536,0.0097,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
200,32,540,0.0098,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,544,0.0097,0,0,0
+200,32,544,0.0099,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,548,0.0098,0,0,0
+200,32,548,0.0100,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,552,0.0099,0,0,0
+200,32,552,0.0101,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,556,0.0099,0,0,0
+200,32,556,0.0101,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,560,0.0100,0,0,0
+200,32,560,0.0102,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,564,0.0102,0,0,0
+200,32,564,0.0103,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,568,0.0102,0,0,0
+200,32,568,0.0104,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,572,0.0103,0,0,0
+200,32,572,0.0105,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,576,0.0103,0,0,0
+200,32,576,0.0105,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,580,0.0105,0,0,0
+200,32,580,0.0106,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,584,0.0104,0,0,0
+200,32,584,0.0107,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,588,0.0106,0,0,0
+200,32,588,0.0107,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,592,0.0107,0,0,0
+200,32,592,0.0108,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,596,0.0106,0,0,0
+200,32,596,0.0109,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,600,0.0107,0,0,0
+200,32,600,0.0110,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,604,0.0109,0,0,0
+200,32,604,0.0111,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,608,0.0109,0,0,0
+200,32,608,0.0111,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,612,0.0109,0,0,0
+200,32,612,0.0112,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,616,0.0110,0,0,0
+200,32,616,0.0112,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,620,0.0117,0,0,0
+200,32,620,0.0113,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,624,0.0112,0,0,0
+200,32,624,0.0114,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,628,0.0111,0,0,0
+200,32,628,0.0115,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,632,0.0112,0,0,0
+200,32,632,0.0115,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,636,0.0113,0,0,0
+200,32,636,0.0115,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,640,0.0115,0,0,0
+200,32,640,0.0116,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,644,0.0114,0,0,0
+200,32,644,0.0118,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,648,0.0115,0,0,0
+200,32,648,0.0117,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,652,0.0116,0,0,0
+200,32,652,0.0119,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,656,0.0117,0,0,0
+200,32,656,0.0119,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,660,0.0117,0,0,0
+200,32,660,0.0121,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,664,0.0118,0,0,0
+200,32,664,0.0120,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,668,0.0119,0,0,0
+200,32,668,0.0122,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,672,0.0119,0,0,0
+200,32,672,0.0121,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,676,0.0119,0,0,0
+200,32,676,0.0124,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,680,0.0120,0,0,0
+200,32,680,0.0123,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,684,0.0121,0,0,0
+200,32,684,0.0125,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,688,0.0122,0,0,0
+200,32,688,0.0124,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,692,0.0122,0,0,0
+200,32,692,0.0125,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,696,0.0123,0,0,0
+200,32,696,0.0126,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,700,0.0124,0,0,0
+200,32,700,0.0127,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,704,0.0124,0,0,0
+200,32,704,0.0126,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,708,0.0125,0,0,0
+200,32,708,0.0127,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,712,0.0125,0,0,0
+200,32,712,0.0129,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,716,0.0126,0,0,0
+200,32,716,0.0128,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,720,0.0126,0,0,0
+200,32,720,0.0129,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,724,0.0127,0,0,0
+200,32,724,0.0132,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,728,0.0128,0,0,0
+200,32,728,0.0131,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,732,0.0128,0,0,0
+200,32,732,0.0131,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,736,0.0129,0,0,0
+200,32,736,0.0133,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,740,0.0130,0,0,0
+200,32,740,0.0133,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,744,0.0130,0,0,0
+200,32,744,0.0133,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,748,0.0131,0,0,0
+200,32,748,0.0134,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,752,0.0131,0,0,0
+200,32,752,0.0136,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,756,0.0132,0,0,0
+200,32,756,0.0136,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,760,0.0133,0,0,0
+200,32,760,0.0136,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,764,0.0134,0,0,0
+200,32,764,0.0136,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,768,0.0134,0,0,0
+200,32,768,0.0138,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,772,0.0136,0,0,0
+200,32,772,0.0138,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,776,0.0136,0,0,0
+200,32,776,0.0139,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,780,0.0136,0,0,0
+200,32,780,0.0139,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,784,0.0137,0,0,0
+200,32,784,0.0140,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,788,0.0138,0,0,0
+200,32,788,0.0140,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,792,0.0139,0,0,0
+200,32,792,0.0141,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,796,0.0139,0,0,0
+200,32,796,0.0142,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,800,0.0140,0,0,0
+200,32,800,0.0143,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,804,0.0141,0,0,0
+200,32,804,0.0143,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,808,0.0142,0,0,0
+200,32,808,0.0144,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,812,0.0142,0,0,0
+200,32,812,0.0144,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,816,0.0143,0,0,0
+200,32,816,0.0145,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,820,0.0143,0,0,0
+200,32,820,0.0146,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,824,0.0144,0,0,0
+200,32,824,0.0148,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,828,0.0145,0,0,0
+200,32,828,0.0147,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,832,0.0145,0,0,0
+200,32,832,0.0148,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,836,0.0146,0,0,0
+200,32,836,0.0149,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,840,0.0147,0,0,0
+200,32,840,0.0150,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,844,0.0147,0,0,0
+200,32,844,0.0150,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,848,0.0148,0,0,0
+200,32,848,0.0150,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,852,0.0149,0,0,0
+200,32,852,0.0151,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,856,0.0149,0,0,0
+200,32,856,0.0152,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,860,0.0150,0,0,0
+200,32,860,0.0152,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,864,0.0150,0,0,0
+200,32,864,0.0153,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,868,0.0152,0,0,0
+200,32,868,0.0154,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,872,0.0151,0,0,0
+200,32,872,0.0156,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,876,0.0153,0,0,0
+200,32,876,0.0156,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,880,0.0153,0,0,0
+200,32,880,0.0156,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,884,0.0153,0,0,0
+200,32,884,0.0157,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,888,0.0155,0,0,0
+200,32,888,0.0157,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,892,0.0156,0,0,0
+200,32,892,0.0158,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,896,0.0156,0,0,0
+200,32,896,0.0159,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,900,0.0158,0,0,0
+200,32,900,0.0159,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,904,0.0158,0,0,0
+200,32,904,0.0161,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,908,0.0159,0,0,0
+200,32,908,0.0162,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,912,0.0159,0,0,0
+200,32,912,0.0164,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,916,0.0162,0,0,0
+200,32,916,0.0163,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,920,0.0162,0,0,0
+200,32,920,0.0164,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,924,0.0162,0,0,0
+200,32,924,0.0165,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,928,0.0162,0,0,0
+200,32,928,0.0166,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,932,0.0163,0,0,0
+200,32,932,0.0166,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,936,0.0164,0,0,0
+200,32,936,0.0167,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,940,0.0165,0,0,0
+200,32,940,0.0167,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,944,0.0165,0,0,0
+200,32,944,0.0168,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,948,0.0166,0,0,0
+200,32,948,0.0169,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,952,0.0167,0,0,0
+200,32,952,0.0172,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,956,0.0168,0,0,0
+200,32,956,0.0171,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,960,0.0168,0,0,0
+200,32,960,0.0172,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,964,0.0172,0,0,0
+200,32,964,0.0175,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,968,0.0173,0,0,0
+200,32,968,0.0175,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,972,0.0173,0,0,0
+200,32,972,0.0176,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,976,0.0173,0,0,0
+200,32,976,0.0177,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,980,0.0175,0,0,0
+200,32,980,0.0178,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,984,0.0176,0,0,0
+200,32,984,0.0178,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,988,0.0175,0,0,0
+200,32,988,0.0179,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,992,0.0176,0,0,0
+200,32,992,0.0179,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,996,0.0178,0,0,0
+200,32,996,0.0182,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,1000,0.0177,0,0,0
+200,32,1000,0.0181,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,1004,0.0178,0,0,0
+200,32,1004,0.0182,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,1008,0.0178,0,0,0
+200,32,1008,0.0182,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,1012,0.0181,0,0,0
+200,32,1012,0.0184,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,1016,0.0180,0,0,0
+200,32,1016,0.0184,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,1020,0.0182,0,0,0
+200,32,1020,0.0186,0,0,0
iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)
-200,32,1024,0.0179,0,0,0
-mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.sflop.bin.csv .
-bsub -W 60 -nnodes 1 -Is -P GEN110 jsrun -n 1 -c 1 -g ALL_GPUS ./bench.sh poisson2d.vflop.bin /gpfs/wolf/gen110/scratch/aherten//poisson2d.vflop.bin.csv
-Job <4300> is submitted to default queue <batch>.
+200,32,1024,0.0182,0,0,0
+mv /gpfs/wolf/trn003/scratch/aherten//poisson2d.sflop.bin.csv .
+bsub -W 60 -nnodes 1 -Is -P TRN003 jsrun -n 1 -c 1 -g ALL_GPUS ./bench.sh poisson2d.vflop.bin /gpfs/wolf/trn003/scratch/aherten//poisson2d.vflop.bin.csv
+Job <24646> is submitted to default queue <batch>.
<<Waiting for dispatch ...>>
<<Starting on login1>>
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
@@ -16852,7 +17273,7 @@ iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VEC
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
200,32,20,0.0013,438000,2190,2190
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,24,0.0014,534000,2670,2670
+200,32,24,0.0013,534000,2670,2670
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
200,32,28,0.0014,630000,3150,3150
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
@@ -16864,29 +17285,29 @@ iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VEC
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
200,32,44,0.0017,1014000,5070,5070
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,48,0.0018,1110000,5550,5550
+200,32,48,0.0017,1110000,5550,5550
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
200,32,52,0.0018,1206000,6030,6030
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,56,0.0020,1302000,6510,6510
+200,32,56,0.0019,1302000,6510,6510
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,60,0.0020,1398000,6990,6990
+200,32,60,0.0019,1398000,6990,6990
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,64,0.0021,1494000,7470,7470
+200,32,64,0.0020,1494000,7470,7470
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
200,32,68,0.0022,1590000,7950,7950
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,72,0.0022,1686000,8430,8430
+200,32,72,0.0021,1686000,8430,8430
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
200,32,76,0.0022,1782000,8910,8910
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
200,32,80,0.0023,1878000,9390,9390
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,84,0.0024,1974000,9870,9870
+200,32,84,0.0025,1974000,9870,9870
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
200,32,88,0.0024,2070000,10350,10350
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,92,0.0025,2166000,10830,10830
+200,32,92,0.0026,2166000,10830,10830
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
200,32,96,0.0025,2262000,11310,11310
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
@@ -16894,13 +17315,13 @@ iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VEC
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
200,32,104,0.0027,2454000,12270,12270
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,108,0.0028,2550000,12750,12750
+200,32,108,0.0027,2550000,12750,12750
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,112,0.0028,2646000,13230,13230
+200,32,112,0.0029,2646000,13230,13230
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
200,32,116,0.0029,2742000,13710,13710
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,120,0.0032,2838000,14190,14190
+200,32,120,0.0029,2838000,14190,14190
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
200,32,124,0.0030,2934000,14670,14670
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
@@ -16910,15 +17331,15 @@ iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VEC
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
200,32,136,0.0032,3222000,16110,16110
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,140,0.0033,3318000,16590,16590
+200,32,140,0.0032,3318000,16590,16590
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
200,32,144,0.0033,3414000,17070,17070
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,148,0.0034,3510000,17550,17550
+200,32,148,0.0036,3510000,17550,17550
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,152,0.0034,3606000,18030,18030
+200,32,152,0.0035,3606000,18030,18030
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,156,0.0036,3702000,18510,18510
+200,32,156,0.0035,3702000,18510,18510
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
200,32,160,0.0036,3798000,18990,18990
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
@@ -16928,13 +17349,13 @@ iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VEC
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
200,32,172,0.0038,4086000,20430,20430
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,176,0.0039,4182000,20910,20910
+200,32,176,0.0038,4182000,20910,20910
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
200,32,180,0.0039,4278000,21390,21390
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
200,32,184,0.0040,4374000,21870,21870
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,188,0.0040,4470000,22350,22350
+200,32,188,0.0041,4470000,22350,22350
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
200,32,192,0.0041,4566000,22830,22830
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
@@ -16944,25 +17365,25 @@ iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VEC
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
200,32,204,0.0043,4854000,24270,24270
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,208,0.0043,4950000,24750,24750
+200,32,208,0.0044,4950000,24750,24750
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
200,32,212,0.0044,5046000,25230,25230
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
200,32,216,0.0045,5142000,25710,25710
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,220,0.0047,5238000,26190,26190
+200,32,220,0.0046,5238000,26190,26190
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
200,32,224,0.0046,5334000,26670,26670
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,228,0.0047,5430000,27150,27150
+200,32,228,0.0048,5430000,27150,27150
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,232,0.0047,5526000,27630,27630
+200,32,232,0.0049,5526000,27630,27630
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
200,32,236,0.0048,5622000,28110,28110
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
200,32,240,0.0049,5718000,28590,28590
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,244,0.0050,5814000,29070,29070
+200,32,244,0.0049,5814000,29070,29070
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
200,32,248,0.0050,5910000,29550,29550
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
@@ -16972,19 +17393,19 @@ iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VEC
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
200,32,260,0.0052,6198000,30990,30990
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,264,0.0052,6294000,31470,31470
+200,32,264,0.0053,6294000,31470,31470
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,268,0.0053,6390000,31950,31950
+200,32,268,0.0054,6390000,31950,31950
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
200,32,272,0.0054,6486000,32430,32430
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,276,0.0058,6582000,32910,32910
+200,32,276,0.0054,6582000,32910,32910
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
200,32,280,0.0055,6678000,33390,33390
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
200,32,284,0.0056,6774000,33870,33870
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,288,0.0056,6870000,34350,34350
+200,32,288,0.0057,6870000,34350,34350
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
200,32,292,0.0057,6966000,34830,34830
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
@@ -16992,23 +17413,23 @@ iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VEC
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
200,32,300,0.0059,7158000,35790,35790
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,304,0.0060,7254000,36270,36270
+200,32,304,0.0059,7254000,36270,36270
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
200,32,308,0.0060,7350000,36750,36750
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,312,0.0061,7446000,37230,37230
+200,32,312,0.0062,7446000,37230,37230
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
200,32,316,0.0061,7542000,37710,37710
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
200,32,320,0.0062,7638000,38190,38190
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,324,0.0063,7734000,38670,38670
+200,32,324,0.0062,7734000,38670,38670
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,328,0.0064,7830000,39150,39150
+200,32,328,0.0063,7830000,39150,39150
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
200,32,332,0.0064,7926000,39630,39630
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,336,0.0064,8022000,40110,40110
+200,32,336,0.0065,8022000,40110,40110
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
200,32,340,0.0065,8118000,40590,40590
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
@@ -17016,21 +17437,21 @@ iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VEC
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
200,32,348,0.0066,8310000,41550,41550
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,352,0.0068,8406000,42030,42030
+200,32,352,0.0067,8406000,42030,42030
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,356,0.0069,8502000,42510,42510
+200,32,356,0.0068,8502000,42510,42510
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
200,32,360,0.0068,8598000,42990,42990
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
200,32,364,0.0069,8694000,43470,43470
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,368,0.0069,8790000,43950,43950
+200,32,368,0.0070,8790000,43950,43950
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
200,32,372,0.0070,8886000,44430,44430
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
200,32,376,0.0071,8982000,44910,44910
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,380,0.0071,9078000,45390,45390
+200,32,380,0.0072,9078000,45390,45390
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
200,32,384,0.0072,9174000,45870,45870
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
@@ -17048,23 +17469,23 @@ iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VEC
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
200,32,412,0.0077,9846000,49230,49230
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,416,0.0077,9942000,49710,49710
+200,32,416,0.0079,9942000,49710,49710
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
200,32,420,0.0078,10038000,50190,50190
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,424,0.0079,10134000,50670,50670
+200,32,424,0.0080,10134000,50670,50670
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,428,0.0079,10230000,51150,51150
+200,32,428,0.0080,10230000,51150,51150
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
200,32,432,0.0080,10326000,51630,51630
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,436,0.0080,10422000,52110,52110
+200,32,436,0.0083,10422000,52110,52110
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,440,0.0081,10518000,52590,52590
+200,32,440,0.0082,10518000,52590,52590
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,444,0.0082,10614000,53070,53070
+200,32,444,0.0083,10614000,53070,53070
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,448,0.0082,10710000,53550,53550
+200,32,448,0.0083,10710000,53550,53550
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
200,32,452,0.0083,10806000,54030,54030
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
@@ -17076,284 +17497,284 @@ iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VEC
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
200,32,468,0.0086,11190000,55950,55950
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,472,0.0088,11286000,56430,56430
+200,32,472,0.0087,11286000,56430,56430
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,476,0.0089,11382000,56910,56910
+200,32,476,0.0087,11382000,56910,56910
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
200,32,480,0.0088,11478000,57390,57390
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,484,0.0088,11574000,57870,57870
+200,32,484,0.0089,11574000,57870,57870
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
200,32,488,0.0089,11670000,58350,58350
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,492,0.0090,11766000,58830,58830
+200,32,492,0.0091,11766000,58830,58830
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,496,0.0090,11862000,59310,59310
+200,32,496,0.0091,11862000,59310,59310
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
200,32,500,0.0091,11958000,59790,59790
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
200,32,504,0.0092,12054000,60270,60270
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,508,0.0094,12150000,60750,60750
+200,32,508,0.0093,12150000,60750,60750
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,512,0.0092,12246000,61230,61230
+200,32,512,0.0094,12246000,61230,61230
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,516,0.0093,12342000,61710,61710
+200,32,516,0.0096,12342000,61710,61710
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,520,0.0093,12438000,62190,62190
+200,32,520,0.0096,12438000,62190,62190
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,524,0.0094,12534000,62670,62670
+200,32,524,0.0095,12534000,62670,62670
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,528,0.0094,12630000,63150,63150
+200,32,528,0.0098,12630000,63150,63150
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,532,0.0095,12726000,63630,63630
+200,32,532,0.0097,12726000,63630,63630
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,536,0.0096,12822000,64110,64110
+200,32,536,0.0097,12822000,64110,64110
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,540,0.0100,12918000,64590,64590
+200,32,540,0.0098,12918000,64590,64590
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,544,0.0097,13014000,65070,65070
+200,32,544,0.0100,13014000,65070,65070
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,548,0.0098,13110000,65550,65550
+200,32,548,0.0102,13110000,65550,65550
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,552,0.0099,13206000,66030,66030
+200,32,552,0.0102,13206000,66030,66030
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,556,0.0100,13302000,66510,66510
+200,32,556,0.0101,13302000,66510,66510
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,560,0.0101,13398000,66990,66990
+200,32,560,0.0103,13398000,66990,66990
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,564,0.0102,13494000,67470,67470
+200,32,564,0.0103,13494000,67470,67470
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,568,0.0103,13590000,67950,67950
+200,32,568,0.0104,13590000,67950,67950
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,572,0.0103,13686000,68430,68430
+200,32,572,0.0105,13686000,68430,68430
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,576,0.0103,13782000,68910,68910
+200,32,576,0.0105,13782000,68910,68910
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,580,0.0105,13878000,69390,69390
+200,32,580,0.0107,13878000,69390,69390
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,584,0.0105,13974000,69870,69870
+200,32,584,0.0108,13974000,69870,69870
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,588,0.0106,14070000,70350,70350
+200,32,588,0.0107,14070000,70350,70350
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,592,0.0106,14166000,70830,70830
+200,32,592,0.0108,14166000,70830,70830
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,596,0.0106,14262000,71310,71310
+200,32,596,0.0109,14262000,71310,71310
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,600,0.0108,14358000,71790,71790
+200,32,600,0.0110,14358000,71790,71790
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,604,0.0109,14454000,72270,72270
+200,32,604,0.0110,14454000,72270,72270
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,608,0.0109,14550000,72750,72750
+200,32,608,0.0111,14550000,72750,72750
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,612,0.0109,14646000,73230,73230
+200,32,612,0.0114,14646000,73230,73230
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,616,0.0111,14742000,73710,73710
+200,32,616,0.0112,14742000,73710,73710
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,620,0.0111,14838000,74190,74190
+200,32,620,0.0113,14838000,74190,74190
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,624,0.0112,14934000,74670,74670
+200,32,624,0.0114,14934000,74670,74670
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,628,0.0112,15030000,75150,75150
+200,32,628,0.0116,15030000,75150,75150
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,632,0.0112,15126000,75630,75630
+200,32,632,0.0115,15126000,75630,75630
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,636,0.0114,15222000,76110,76110
+200,32,636,0.0117,15222000,76110,76110
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,640,0.0114,15318000,76590,76590
+200,32,640,0.0116,15318000,76590,76590
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,644,0.0114,15414000,77070,77070
+200,32,644,0.0118,15414000,77070,77070
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,648,0.0115,15510000,77550,77550
+200,32,648,0.0117,15510000,77550,77550
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,652,0.0117,15606000,78030,78030
+200,32,652,0.0119,15606000,78030,78030
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,656,0.0117,15702000,78510,78510
+200,32,656,0.0119,15702000,78510,78510
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,660,0.0117,15798000,78990,78990
+200,32,660,0.0120,15798000,78990,78990
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,664,0.0118,15894000,79470,79470
+200,32,664,0.0120,15894000,79470,79470
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,668,0.0120,15990000,79950,79950
+200,32,668,0.0121,15990000,79950,79950
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,672,0.0120,16086000,80430,80430
+200,32,672,0.0121,16086000,80430,80430
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,676,0.0121,16182000,80910,80910
+200,32,676,0.0123,16182000,80910,80910
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,680,0.0120,16278000,81390,81390
+200,32,680,0.0122,16278000,81390,81390
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,684,0.0121,16374000,81870,81870
+200,32,684,0.0125,16374000,81870,81870
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,688,0.0122,16470000,82350,82350
+200,32,688,0.0124,16470000,82350,82350
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,692,0.0122,16566000,82830,82830
+200,32,692,0.0126,16566000,82830,82830
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,696,0.0124,16662000,83310,83310
+200,32,696,0.0125,16662000,83310,83310
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,700,0.0124,16758000,83790,83790
+200,32,700,0.0127,16758000,83790,83790
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,704,0.0124,16854000,84270,84270
+200,32,704,0.0128,16854000,84270,84270
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,708,0.0125,16950000,84750,84750
+200,32,708,0.0128,16950000,84750,84750
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,712,0.0125,17046000,85230,85230
+200,32,712,0.0128,17046000,85230,85230
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,716,0.0126,17142000,85710,85710
+200,32,716,0.0128,17142000,85710,85710
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,720,0.0126,17238000,86190,86190
+200,32,720,0.0129,17238000,86190,86190
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,724,0.0127,17334000,86670,86670
+200,32,724,0.0130,17334000,86670,86670
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,728,0.0128,17430000,87150,87150
+200,32,728,0.0130,17430000,87150,87150
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,732,0.0130,17526000,87630,87630
+200,32,732,0.0132,17526000,87630,87630
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,736,0.0129,17622000,88110,88110
+200,32,736,0.0132,17622000,88110,88110
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,740,0.0129,17718000,88590,88590
+200,32,740,0.0133,17718000,88590,88590
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,744,0.0130,17814000,89070,89070
+200,32,744,0.0133,17814000,89070,89070
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,748,0.0131,17910000,89550,89550
+200,32,748,0.0134,17910000,89550,89550
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,752,0.0132,18006000,90030,90030
+200,32,752,0.0134,18006000,90030,90030
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,756,0.0132,18102000,90510,90510
+200,32,756,0.0136,18102000,90510,90510
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,760,0.0133,18198000,90990,90990
+200,32,760,0.0136,18198000,90990,90990
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,764,0.0134,18294000,91470,91470
+200,32,764,0.0136,18294000,91470,91470
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,768,0.0135,18390000,91950,91950
+200,32,768,0.0137,18390000,91950,91950
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,772,0.0136,18486000,92430,92430
+200,32,772,0.0139,18486000,92430,92430
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,776,0.0136,18582000,92910,92910
+200,32,776,0.0139,18582000,92910,92910
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,780,0.0137,18678000,93390,93390
+200,32,780,0.0139,18678000,93390,93390
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,784,0.0137,18774000,93870,93870
+200,32,784,0.0140,18774000,93870,93870
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,788,0.0138,18870000,94350,94350
+200,32,788,0.0140,18870000,94350,94350
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,792,0.0138,18966000,94830,94830
+200,32,792,0.0142,18966000,94830,94830
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,796,0.0140,19062000,95310,95310
+200,32,796,0.0142,19062000,95310,95310
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,800,0.0140,19158000,95790,95790
+200,32,800,0.0144,19158000,95790,95790
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,804,0.0140,19254000,96270,96270
+200,32,804,0.0143,19254000,96270,96270
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,808,0.0141,19350000,96750,96750
+200,32,808,0.0144,19350000,96750,96750
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,812,0.0142,19446000,97230,97230
+200,32,812,0.0145,19446000,97230,97230
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,816,0.0143,19542000,97710,97710
+200,32,816,0.0145,19542000,97710,97710
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,820,0.0143,19638000,98190,98190
+200,32,820,0.0146,19638000,98190,98190
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,824,0.0144,19734000,98670,98670
+200,32,824,0.0147,19734000,98670,98670
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,828,0.0146,19830000,99150,99150
+200,32,828,0.0147,19830000,99150,99150
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,832,0.0146,19926000,99630,99630
+200,32,832,0.0148,19926000,99630,99630
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,836,0.0146,20022000,100110,100110
+200,32,836,0.0151,20022000,100110,100110
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,840,0.0147,20118000,100590,100590
+200,32,840,0.0150,20118000,100590,100590
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,844,0.0147,20214000,101070,101070
+200,32,844,0.0150,20214000,101070,101070
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,848,0.0148,20310000,101550,101550
+200,32,848,0.0151,20310000,101550,101550
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,852,0.0148,20406000,102030,102030
+200,32,852,0.0152,20406000,102030,102030
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,856,0.0150,20502000,102510,102510
+200,32,856,0.0152,20502000,102510,102510
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,860,0.0150,20598000,102990,102990
+200,32,860,0.0152,20598000,102990,102990
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,864,0.0151,20694000,103470,103470
+200,32,864,0.0153,20694000,103470,103470
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,868,0.0151,20790000,103950,103950
+200,32,868,0.0154,20790000,103950,103950
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,872,0.0152,20886000,104430,104430
+200,32,872,0.0155,20886000,104430,104430
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,876,0.0153,20982000,104910,104910
+200,32,876,0.0155,20982000,104910,104910
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,880,0.0154,21078000,105390,105390
+200,32,880,0.0157,21078000,105390,105390
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,884,0.0154,21174000,105870,105870
+200,32,884,0.0157,21174000,105870,105870
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,888,0.0154,21270000,106350,106350
+200,32,888,0.0158,21270000,106350,106350
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,892,0.0155,21366000,106830,106830
+200,32,892,0.0158,21366000,106830,106830
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,896,0.0157,21462000,107310,107310
+200,32,896,0.0159,21462000,107310,107310
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,900,0.0156,21558000,107790,107790
+200,32,900,0.0161,21558000,107790,107790
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,904,0.0158,21654000,108270,108270
+200,32,904,0.0162,21654000,108270,108270
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,908,0.0159,21750000,108750,108750
+200,32,908,0.0161,21750000,108750,108750
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,912,0.0159,21846000,109230,109230
+200,32,912,0.0163,21846000,109230,109230
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,916,0.0161,21942000,109710,109710
+200,32,916,0.0164,21942000,109710,109710
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,920,0.0161,22038000,110190,110190
+200,32,920,0.0165,22038000,110190,110190
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,924,0.0162,22134000,110670,110670
+200,32,924,0.0164,22134000,110670,110670
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,928,0.0164,22230000,111150,111150
+200,32,928,0.0166,22230000,111150,111150
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,932,0.0164,22326000,111630,111630
+200,32,932,0.0166,22326000,111630,111630
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,936,0.0164,22422000,112110,112110
+200,32,936,0.0167,22422000,112110,112110
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,940,0.0164,22518000,112590,112590
+200,32,940,0.0168,22518000,112590,112590
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,944,0.0165,22614000,113070,113070
+200,32,944,0.0168,22614000,113070,113070
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,948,0.0167,22710000,113550,113550
+200,32,948,0.0169,22710000,113550,113550
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,952,0.0168,22806000,114030,114030
+200,32,952,0.0170,22806000,114030,114030
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,956,0.0168,22902000,114510,114510
+200,32,956,0.0170,22902000,114510,114510
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,960,0.0168,22998000,114990,114990
+200,32,960,0.0171,22998000,114990,114990
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,964,0.0174,23094000,115470,115470
+200,32,964,0.0176,23094000,115470,115470
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,968,0.0172,23190000,115950,115950
+200,32,968,0.0176,23190000,115950,115950
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,972,0.0173,23286000,116430,116430
+200,32,972,0.0177,23286000,116430,116430
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,976,0.0172,23382000,116910,116910
+200,32,976,0.0177,23382000,116910,116910
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,980,0.0174,23478000,117390,117390
+200,32,980,0.0178,23478000,117390,117390
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,984,0.0174,23574000,117870,117870
+200,32,984,0.0178,23574000,117870,117870
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,988,0.0176,23670000,118350,118350
+200,32,988,0.0179,23670000,118350,118350
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,992,0.0176,23766000,118830,118830
+200,32,992,0.0180,23766000,118830,118830
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,996,0.0179,23862000,119310,119310
+200,32,996,0.0181,23862000,119310,119310
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,1000,0.0177,23958000,119790,119790
+200,32,1000,0.0182,23958000,119790,119790
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,1004,0.0178,24054000,120270,120270
+200,32,1004,0.0182,24054000,120270,120270
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,1008,0.0178,24150000,120750,120750
+200,32,1008,0.0182,24150000,120750,120750
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,1012,0.0180,24246000,121230,121230
+200,32,1012,0.0184,24246000,121230,121230
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,1016,0.0180,24342000,121710,121710
+200,32,1016,0.0185,24342000,121710,121710
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,1020,0.0181,24438000,122190,122190
+200,32,1020,0.0184,24438000,122190,122190
iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)
-200,32,1024,0.0178,24534000,122670,122670
-mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.vflop.bin.csv .
+200,32,1024,0.0182,24534000,122670,122670
+mv /gpfs/wolf/trn003/scratch/aherten//poisson2d.vflop.bin.csv .
</pre>
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@@ -17364,35 +17785,153 @@ mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.vflop.bin.csv .
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-<div class="prompt input_prompt">In [47]:</div>
+<div class="prompt input_prompt">In [39]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_sflop</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">"poisson2d.sflop.bin.csv"</span><span class="p">,</span> <span class="n">skiprows</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">50000</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
<span class="n">df_vflop</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">"poisson2d.vflop.bin.csv"</span><span class="p">,</span> <span class="n">skiprows</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">50000</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
<span class="n">df_flop</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">df_sflop</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"nx"</span><span class="p">),</span> <span class="n">df_vflop</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"nx"</span><span class="p">)[[</span><span class="s1">'PM_VECTOR_FLOP_CMPL (total)'</span><span class="p">,</span> <span class="s1">'PM_VECTOR_FLOP_CMPL (min)'</span><span class="p">,</span> <span class="s1">' PM_VECTOR_FLOP_CMPL (max)'</span><span class="p">]]],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span>
+<span class="n">df_flop</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+ <div class="prompt output_prompt">Out[39]:</div>
+
+
+
+<div class="output_html rendered_html output_subarea output_execute_result">
+<div>
+<style scoped>
+ .dataframe tbody tr th:only-of-type {
+ vertical-align: middle;
+ }
+
+ .dataframe tbody tr th {
+ vertical-align: top;
+ }
+
+ .dataframe thead th {
+ text-align: right;
+ }
+</style>
+<table border="1" class="dataframe">
+ <thead>
+ <tr style="text-align: right;">
+ <th></th>
+ <th>nx</th>
+ <th>iter</th>
+ <th>ny</th>
+ <th>Runtime</th>
+ <th>PM_SCALAR_FLOP_CMPL (total)</th>
+ <th>PM_SCALAR_FLOP_CMPL (min)</th>
+ <th>PM_SCALAR_FLOP_CMPL (max)</th>
+ <th>PM_VECTOR_FLOP_CMPL (total)</th>
+ <th>PM_VECTOR_FLOP_CMPL (min)</th>
+ <th>PM_VECTOR_FLOP_CMPL (max)</th>
+ </tr>
+ </thead>
+ <tbody>
+ <tr>
+ <th>0</th>
+ <td>4</td>
+ <td>200</td>
+ <td>32</td>
+ <td>0.0010</td>
+ <td>96000</td>
+ <td>480</td>
+ <td>480</td>
+ <td>0</td>
+ <td>0</td>
+ <td>0</td>
+ </tr>
+ <tr>
+ <th>1</th>
+ <td>8</td>
+ <td>200</td>
+ <td>32</td>
+ <td>0.0011</td>
+ <td>0</td>
+ <td>0</td>
+ <td>0</td>
+ <td>150000</td>
+ <td>750</td>
+ <td>750</td>
+ </tr>
+ <tr>
+ <th>2</th>
+ <td>12</td>
+ <td>200</td>
+ <td>32</td>
+ <td>0.0012</td>
+ <td>0</td>
+ <td>0</td>
+ <td>0</td>
+ <td>246000</td>
+ <td>1230</td>
+ <td>1230</td>
+ </tr>
+ <tr>
+ <th>3</th>
+ <td>16</td>
+ <td>200</td>
+ <td>32</td>
+ <td>0.0012</td>
+ <td>0</td>
+ <td>0</td>
+ <td>0</td>
+ <td>342000</td>
+ <td>1710</td>
+ <td>1710</td>
+ </tr>
+ <tr>
+ <th>4</th>
+ <td>20</td>
+ <td>200</td>
+ <td>32</td>
+ <td>0.0013</td>
+ <td>0</td>
+ <td>0</td>
+ <td>0</td>
+ <td>438000</td>
+ <td>2190</td>
+ <td>2190</td>
+ </tr>
+ </tbody>
+</table>
+</div>
+</div>
+
+</div>
+
+</div>
+</div>
+
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-<p>The name of the vector counter is a bit misleading; not floating point operations are measured but floating point instructions. To get <em>real</em> floating point operations, each value needs to be multiplied by the vector width (2). We can plot the values afterwards (non-interactive: <code>make graph_task4</code>).</p>
+<p>Again, the name of the vector counter is a bit misleading; not floating point operations are measured but floating point instructions. To get <em>real</em> floating point operations, each value needs to be multiplied by the vector width (2). We can plot the values afterwards (non-interactive: <code>make graph_task4</code>).</p>
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-<div class="prompt input_prompt">In [49]:</div>
+<div class="prompt input_prompt">In [40]:</div>
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-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">common</span><span class="o">.</span><span class="n">normalize</span><span class="p">(</span><span class="n">df_flop</span><span class="p">,</span> <span class="s2">"PM_SCALAR_FLOP_CMPL (min)"</span><span class="p">,</span> <span class="s2">"Scalar FlOps / Loop Iteration"</span><span class="p">)</span>
-<span class="n">common</span><span class="o">.</span><span class="n">normalize</span><span class="p">(</span><span class="n">df_flop</span><span class="p">,</span> <span class="s2">"PM_VECTOR_FLOP_CMPL (min)"</span><span class="p">,</span> <span class="s2">"Vector Instructions / Loop Iteration"</span><span class="p">)</span>
-<span class="n">df_flop</span><span class="p">[</span><span class="s2">"Vector FlOps / Loop Iteration"</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_flop</span><span class="p">[</span><span class="s2">"Vector Instructions / Loop Iteration"</span><span class="p">]</span> <span class="o">*</span> <span class="mi">2</span>
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_flop</span><span class="p">[</span><span class="s2">"Grid Points"</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_flop</span><span class="p">[</span><span class="s2">"nx"</span><span class="p">]</span> <span class="o">*</span> <span class="n">df_flop</span><span class="p">[</span><span class="s2">"ny"</span><span class="p">]</span>
+<span class="n">df_flop</span><span class="p">[</span><span class="s2">"Vector FlOps (min)"</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_flop</span><span class="p">[</span><span class="s2">"PM_VECTOR_FLOP_CMPL (min)"</span><span class="p">]</span> <span class="o">*</span> <span class="mi">2</span>
+<span class="n">df_flop</span><span class="p">[</span><span class="s2">"Scalar FlOps (min)"</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_flop</span><span class="p">[</span><span class="s2">"PM_SCALAR_FLOP_CMPL (min)"</span><span class="p">]</span>
</pre></div>
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@@ -17402,10 +17941,10 @@ mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.vflop.bin.csv .
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-<div class="prompt input_prompt">In [50]:</div>
+<div class="prompt input_prompt">In [41]:</div>
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-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_flop</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"nx"</span><span class="p">)[[</span><span class="s2">"Scalar FlOps / Loop Iteration"</span><span class="p">,</span> <span class="s2">"Vector FlOps / Loop Iteration"</span><span class="p">]]</span><span class="o">.</span><span class="n">plot</span><span class="p">();</span>
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_flop</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"Grid Points"</span><span class="p">)[[</span><span class="s2">"Scalar FlOps (min)"</span><span class="p">,</span> <span class="s2">"Vector FlOps (min)"</span><span class="p">]]</span><span class="o">.</span><span class="n">plot</span><span class="p">();</span>
</pre></div>
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@@ -17424,7 +17963,7 @@ mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.vflop.bin.csv .
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
"
>
</div>
@@ -17434,6 +17973,52 @@ mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.vflop.bin.csv .
</div>
</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In [43]:</div>
+<div class="inner_cell">
+ <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">_fit</span><span class="p">,</span> <span class="n">_cov</span> <span class="o">=</span> <span class="n">common</span><span class="o">.</span><span class="n">print_and_return_fit</span><span class="p">(</span>
+ <span class="p">[</span><span class="s2">"Scalar FlOps (min)"</span><span class="p">,</span> <span class="s2">"Vector FlOps (min)"</span><span class="p">],</span>
+ <span class="n">df_flop</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"Grid Points"</span><span class="p">),</span>
+ <span class="n">linear_function</span>
+<span class="p">)</span>
+<span class="n">fit_parameters</span> <span class="o">=</span> <span class="p">{</span><span class="o">**</span><span class="n">fit_parameters</span><span class="p">,</span> <span class="o">**</span><span class="n">_fit</span><span class="p">}</span>
+<span class="n">fit_covariance</span> <span class="o">=</span> <span class="p">{</span><span class="o">**</span><span class="n">fit_covariance</span><span class="p">,</span> <span class="o">**</span><span class="n">_cov</span><span class="p">}</span>
+</pre></div>
+
+ </div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area">
+
+ <div class="prompt"></div>
+
+
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>Counter Scalar FlOps (min) is proportional to the grid points (nx*ny) by a factor of -0.0003 (± 0.0002)
+Counter Vector FlOps (min) is proportional to the grid points (nx*ny) by a factor of 7.5004 (± 0.0002)
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div><div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Interesting! We seem to be using the vector registers of our system very well. Basically all operations are vector operations!</p>
+
+</div>
+</div>
</div>
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@@ -17449,13 +18034,13 @@ mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.vflop.bin.csv .
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-<div class="prompt input_prompt">In [66]:</div>
+<div class="prompt input_prompt">In [56]:</div>
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-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">I_flop_scalar</span> <span class="o">=</span> <span class="n">df_flop</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"nx"</span><span class="p">)[</span><span class="s2">"Scalar FlOps / Loop Iteration"</span><span class="p">]</span>
-<span class="n">I_flop_vector</span> <span class="o">=</span> <span class="n">df_flop</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"nx"</span><span class="p">)[</span><span class="s2">"Vector FlOps / Loop Iteration"</span><span class="p">]</span>
-<span class="n">I_mem_load</span> <span class="o">=</span> <span class="n">df_byte</span><span class="p">[</span><span class="s2">"Loads / Loop Iteration"</span><span class="p">]</span>
-<span class="n">I_mem_store</span> <span class="o">=</span> <span class="n">df_byte</span><span class="p">[</span><span class="s2">"Stores / Loop Iteration"</span><span class="p">]</span>
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">I_flop_scalar</span> <span class="o">=</span> <span class="n">df_flop</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"Grid Points"</span><span class="p">)[</span><span class="s2">"Scalar FlOps (min)"</span><span class="p">]</span>
+<span class="n">I_flop_vector</span> <span class="o">=</span> <span class="n">df_flop</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">"Grid Points"</span><span class="p">)[</span><span class="s2">"Vector FlOps (min)"</span><span class="p">]</span>
+<span class="n">I_mem_load</span> <span class="o">=</span> <span class="n">df_byte</span><span class="p">[</span><span class="s2">"Loads"</span><span class="p">]</span>
+<span class="n">I_mem_store</span> <span class="o">=</span> <span class="n">df_byte</span><span class="p">[</span><span class="s2">"Stores"</span><span class="p">]</span>
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@@ -17465,7 +18050,7 @@ mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.vflop.bin.csv .
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_ai</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">()</span>
@@ -17490,7 +18075,7 @@ mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.vflop.bin.csv .
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
"
>
</div>
@@ -17514,6 +18099,7 @@ mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.vflop.bin.csv .
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Task-E2:-Measuring-a-Larger-Range">Task E2: Measuring a Larger Range<a class="anchor-link" href="#Task-E2:-Measuring-a-Larger-Range">¶</a></h2><p><a name="taske2"></a></p>
<p>If you still still have time, you might venture into your own benchmarking adventure.</p>
+<p>Maybe you noticed already, for instance in Task 2 C: At the very right to very large numbers of grid points, the behaviour of the graph changed. Something is happening there!</p>
<p><strong>TASK</strong>: Revisit the counters measured above for a larger range of <code>nx</code>. Right now, we only studied <code>nx</code> until 1000. New effects appear above that value – partly only well above, though ($nx > 15000$).</p>
<p>You're on your own here. Edit the <code>bench.sh</code> script to change the range and the stepping increments.</p>
<p><strong>Good luck!</strong></p>
diff --git a/2-Performance_Counters/Handson/Solutions/Hands-On-Performance-Counters.ipynb b/2-Performance_Counters/Handson/Solutions/Hands-On-Performance-Counters.ipynb
index ae4037283522b909c8fe6ef29d2108cf3af5cc07..91f993b1553d71a39298693fdaa16fc55240d18b 100644
--- a/2-Performance_Counters/Handson/Solutions/Hands-On-Performance-Counters.ipynb
+++ b/2-Performance_Counters/Handson/Solutions/Hands-On-Performance-Counters.ipynb
@@ -1,4402 +1 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Hands-On: Performance Counters\n",
- "\n",
- "This Notebook is part of the exercises for the SC18 Tutorial »Application Porting and Optimization on GPU-accelerated POWER Architectures«. It is to be run on a POWER9 machine; in the tutorial: on Ascent, the POWER9 training cluster of Oak Ridge National Lab.\n",
- "\n",
- "This Notebook can be run interactively on Ascent. If this capability is unavailable to you, use it as a description for executing the tasks on Ascent via a shell access. During data evaluation, the Notebook mentions the corresponding commands to execute in case you are not able to run the Notebook interactively directly on Ascent.\n",
- "\n",
- "## Table of Contents\n",
- "<a name=\"toc\"></a>\n",
- "\n",
- "* [Task 1: Measuring Cycles and Instructions](#task1)\n",
- "* [Task 2: Measuring Loads and Stores](#task2)\n",
- " - [A: Loads and Stores](#task2-a)\n",
- " - [B: More Loads and Stores](#task2-b)\n",
- " - [C: Bandwidth](#task2-c)\n",
- "* [Task E1: Measuring FLOP](#taske1)\n",
- "* [Task E2: Measuring a Greater Range](#taske2)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Task 1: Measuring Cycles and Instructions\n",
- "<a name=\"task1\"></a>\n",
- "\n",
- "Throughout this exercise, the core loop of the Jacobi algorithm is instrumented and analyzed. The part in question is\n",
- "\n",
- "```c\n",
- "for (int iy = iy_start; iy < iy_end; iy++)\n",
- "{\n",
- " for( int ix = ix_start; ix < ix_end; ix++ )\n",
- " {\n",
- " Anew[iy*nx+ix] = -0.25 * (rhs[iy*nx+ix] - (A[ iy *nx+ix+1] + A[ iy *nx+ix-1]\n",
- " + A[(iy-1)*nx+ix ] + A[(iy+1)*nx+ix ]));\n",
- " error = fmaxr( error, fabsr(Anew[iy*nx+ix]-A[iy*nx+ix]));\n",
- " }\n",
- "}\n",
- "```\n",
- "\n",
- "After `PAPI_add_named_event()` is used to add named PMU events outside of the relaxation iteration, `PAPI_start()`\n",
- "and `PAPI_stop()` can be used to count how often a PMU event is incremented.\n",
- "\n",
- "For the first task, we will measure quantities often used to characterize an application, cycles and instructions.\n",
- "\n",
- "**TASK**: Please measure counters for completed instructions and run cycles. See the TODOs in [`poisson2d.ins_cyc.c`](/edit/Tasks/poisson2d.ins_cyc.c). Either edit with Jupyter capabilities by clicking on the link of the file or use a dedicated editor (`vim` is available). The names of the counters to be implemented are `PM_INST_CMPL` and `PM_RUN_CYC`.\n",
- "\n",
- "After changing the source code, compile it with `make task1` or by executing the following cell (we need to change directories first, though).\n",
- "\n",
- "[Back to top](#toc)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "/autofs/nccsopen-svm1_home/aherten/OpenPOWER-SC18/2-PAPI/Compiling/Solutions\n"
- ]
- }
- ],
- "source": [
- "%cd Tasks/\n",
- "# Use `%cd Solutions` to look at the solutions for each task"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "gcc -DUSE_DOUBLE -Ofast -std=c99 -lm -lpapi poisson2d.ins_cyc.c -o poisson2d.ins_cyc.bin\r\n"
- ]
- }
- ],
- "source": [
- "!make task1"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Make sure your program is measuring correctly, by invoking it, for instance with these arguments: `./poisson2d.ins_cyc.bin 100 64 32` – see the next cell. The `100` specifies the number of iterations to perform, `64` and `32` are the size of the grid in y and x direction, respectively."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\r\n",
- "100,64,32,0.0011,3324000,33229,34329,1902422,18803,27821\r\n"
- ]
- }
- ],
- "source": [
- "!./poisson2d.ins_cyc.bin 100 64 32\n",
- "# alternatively call !make run_task1"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "For the following runs, we are going to use Ascent's compute backend nodes which are not shared amongst users and also have six GPUs available. We use the available batch scheduler *IBM Spectrum LSF* for this. For convenience, a call to the batch submission system is stored in the environment variable `$SC18_SUBMIT_CMD`. You are welcome to adapt it once you get more familiar with the system.\n",
- "\n",
- "For now, we want to run our first benchmarking run and measure cycles and instructions for different data sizes, as a function of `nx`. The Makefile holds a target for this, call it with `make bench_task1`:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 80,
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "gcc -DUSE_DOUBLE -Ofast -std=c99 -lm -lpapi poisson2d.ins_cyc.c -o poisson2d.ins_cyc.bin\n",
- "bsub -W 60 -nnodes 1 -Is -P GEN110 jsrun -n 1 -c 1 -g ALL_GPUS ./bench.sh poisson2d.ins_cyc.bin /gpfs/wolf/gen110/scratch/aherten//poisson2d.ins_cyc.bin.csv\n",
- "Job <4318> is submitted to default queue <batch>.\n",
- "<<Waiting for dispatch ...>>\n",
- "<<Starting on login1>>\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,4,0.0012,548153,2735,3888,266504,1243,4753\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,8,0.0014,1082153,5405,6558,668070,3227,6573\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,12,0.0014,1442153,7205,8358,872094,4181,12974\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,16,0.0015,1802153,9005,10158,1074585,5230,7975\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,20,0.0015,2162153,10805,11958,1281118,6236,14107\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,24,0.0016,2522153,12605,13758,1479347,7222,10037\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,28,0.0019,2882153,14405,15558,1682827,8251,11219\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,32,0.0017,3242153,16205,17358,1871170,9210,12109\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,36,0.0018,3602153,18005,19158,2075730,10193,13063\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,40,0.0019,3962153,19805,20958,2272736,11258,14491\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,44,0.0019,4322153,21605,22758,2491982,12249,17554\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,48,0.0020,4682153,23405,24558,2692600,13292,16003\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,52,0.0020,5042153,25205,26358,2878730,14277,17055\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,56,0.0021,5402153,27005,28158,3084915,15295,18583\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,60,0.0022,5762153,28805,29958,3291836,16330,19233\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,64,0.0023,6122153,30605,31758,3622134,17946,20887\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,68,0.0024,6482153,32405,33558,3930512,19200,22297\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,72,0.0027,6842153,34205,35358,4270649,20402,22797\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,76,0.0025,7202153,36005,37158,4209408,20894,24035\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,80,0.0025,7562153,37805,38958,4410712,21911,24986\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,84,0.0026,7922153,39605,40758,4631259,23020,25649\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,88,0.0027,8282153,41405,42558,4814218,23914,26743\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,92,0.0027,8642153,43205,44358,5039020,24944,37612\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,96,0.0030,9002153,45005,46158,5247046,26072,29012\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,100,0.0029,9362153,46805,47958,5426721,26963,29831\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,104,0.0029,9722153,48605,49758,5619647,27963,31679\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,108,0.0030,10082153,50405,51558,5828776,28956,31626\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,112,0.0031,10442153,52205,53358,6033005,30029,32674\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,116,0.0031,10802153,54005,55158,6244763,30994,35257\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,120,0.0032,11162153,55805,56958,6425499,31972,34572\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,124,0.0033,11522153,57605,58758,6654149,33094,35931\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,128,0.0033,11882153,59405,60558,6851733,34090,36755\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,132,0.0034,12242153,61205,62358,7052529,35058,39834\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,136,0.0035,12602153,63005,64158,7241645,36039,38957\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,140,0.0035,12962153,64805,65958,7438548,37024,39702\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,144,0.0036,13322153,66605,67758,7649807,38039,46041\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,148,0.0037,13682153,68405,69558,7837686,39006,41671\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,152,0.0037,14042153,70205,71358,8039582,40031,42707\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,156,0.0038,14402153,72005,73158,8272212,41195,43645\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,160,0.0040,14762153,73805,74958,8471858,42200,44594\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,164,0.0039,15122153,75605,76758,8657085,43103,45699\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,168,0.0039,15482153,77405,78558,8856462,44110,46863\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,172,0.0040,15842153,79205,80358,9050337,45084,47600\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,176,0.0041,16202153,81005,82158,9267755,46142,55546\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,180,0.0042,16562153,82805,83958,9452041,47058,49763\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,184,0.0042,16922153,84605,85758,9655929,48043,50875\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,188,0.0043,17282153,86405,87558,9906002,49331,52491\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,192,0.0043,17642153,88205,89358,10089481,50268,52937\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,196,0.0044,18002153,90005,91158,10292606,51256,54507\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,200,0.0045,18362153,91805,92958,10466174,52144,54851\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,204,0.0045,18722153,93605,94758,10710242,53145,77999\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,208,0.0046,19082153,95405,96558,10872705,54177,57081\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,212,0.0047,19442153,97205,98358,11284063,56244,58937\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,216,0.0047,19802153,99005,100158,11267668,56162,58869\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,220,0.0048,20162153,100805,101958,11510801,57350,60362\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,224,0.0051,20522153,102605,103758,11730908,58406,61013\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,228,0.0050,20882153,104405,105558,11891323,59260,62051\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,232,0.0050,21242153,106205,107358,12083458,60220,63113\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,236,0.0050,21602153,108005,109158,12290078,61234,68599\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,240,0.0051,21962153,109805,110958,12547828,62267,88616\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,244,0.0052,22322153,111605,112758,12674066,63146,66333\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,248,0.0052,22682153,113405,114558,12882346,64155,67081\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,252,0.0053,23042153,115205,116358,13140221,65490,68231\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,256,0.0054,23402153,117005,118158,13331460,66431,69187\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,260,0.0054,23762153,118805,119958,13531478,67456,70141\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,264,0.0055,24122153,120605,121758,13710546,68246,81094\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,268,0.0055,24482153,122405,123558,13890638,69208,72412\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,272,0.0056,24842153,124205,125358,14130816,70366,88752\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,276,0.0057,25202153,126005,127158,14355067,71208,93990\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,280,0.0057,25562153,127805,128958,14513593,72251,85857\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,284,0.0059,25922153,129605,130758,14800806,73802,76775\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,288,0.0059,26282153,131405,132558,14959572,74579,77267\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,292,0.0059,26642153,133205,134358,15130033,75389,78361\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,296,0.0060,27002153,135005,136158,15314583,76370,79151\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,300,0.0061,27362153,136805,137958,15515700,77373,80055\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,304,0.0061,27722153,138605,139758,15739536,78395,81351\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,308,0.0062,28082153,140405,141558,15910915,79341,82085\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,312,0.0063,28442153,142205,143358,16119259,80297,83271\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,316,0.0063,28802153,144005,145158,16376727,81668,84481\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,320,0.0064,29162153,145805,146958,16575917,82685,85800\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,324,0.0065,29522153,147605,148758,16752101,83529,86861\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,328,0.0065,29882153,149405,150558,16931954,84456,87199\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,332,0.0066,30242153,151205,152358,17129562,85462,88022\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,336,0.0067,30602153,153005,154158,17522378,87337,90235\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,340,0.0067,30962153,154805,155958,17525540,87379,89947\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,344,0.0068,31322153,156605,157758,17811817,88413,169057\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,348,0.0069,31682153,158405,159558,17999372,89772,92601\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,352,0.0069,32042153,160205,161358,18204371,90776,101494\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,356,0.0070,32402153,162005,163158,18393456,91621,107055\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,360,0.0070,32762153,163805,164958,18567077,92476,114024\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,364,0.0072,33122153,165605,166758,18749614,93562,96291\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,368,0.0073,33482153,167405,168558,18957503,94465,97467\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,372,0.0072,33842153,169205,170358,19137907,95471,98421\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,376,0.0073,34202153,171005,172158,19350029,96457,99505\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,380,0.0075,34562153,172805,173958,19657158,97897,122483\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,384,0.0075,34922153,174605,175758,20019224,98872,199167\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,388,0.0075,35282153,176405,177558,19999785,99747,102911\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,392,0.0077,35642153,178205,179358,20188679,100586,121054\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,396,0.0076,36002153,180005,181158,20368637,101583,105060\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,400,0.0077,36362153,181805,182958,20628698,102607,152896\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,404,0.0078,36722153,183605,184758,20759711,103503,111551\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,408,0.0078,37082153,185405,186558,21008339,104552,136230\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,412,0.0080,37442153,187205,188358,21248565,105961,109252\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,416,0.0080,37802153,189005,190158,21446394,106998,110446\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,420,0.0081,38162153,190805,191958,21618503,107795,119989\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,424,0.0081,38522153,192605,193758,21778142,108604,112064\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,428,0.0081,38882153,194405,195558,21989784,109653,120306\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,432,0.0082,39242153,196205,197358,22191881,110730,113916\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,436,0.0083,39602153,198005,199158,22373426,111587,115657\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,440,0.0084,39962153,199805,200958,22596402,112638,130342\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,444,0.0084,40322153,201605,202758,22868323,114041,124888\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,448,0.0085,40682153,203405,204558,23084361,115132,128588\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,452,0.0086,41042153,205205,206358,23255449,115787,156348\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,456,0.0088,41402153,207005,208158,23400730,116742,119985\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,460,0.0087,41762153,208805,209958,23616057,117782,125672\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,464,0.0088,42122153,210605,211758,23845815,118769,150383\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,468,0.0089,42482153,212405,213558,23982677,119580,123029\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,472,0.0090,42842153,214205,215358,24183894,120688,124270\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,476,0.0090,43202153,216005,217158,24479273,122149,125974\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,480,0.0091,43562153,217805,218958,24768939,123125,164217\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,484,0.0092,43922153,219605,220758,24828983,123895,127390\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,488,0.0091,44282153,221405,222558,25011559,124768,128788\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,492,0.0092,44642153,223205,224358,25219550,125760,132732\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,496,0.0093,45002153,225005,226158,25447017,126853,140428\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,500,0.0093,45362153,226805,227958,25586059,127650,131094\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,504,0.0094,45722153,228605,229758,25796559,128739,131932\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,508,0.0095,46082153,230405,231558,26122261,130275,141242\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,512,0.0095,46442153,232205,233358,26303806,130890,135216\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,516,0.0096,46802153,234005,235158,26441241,131860,137807\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,520,0.0097,47162153,235805,236958,26620814,132726,144193\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,524,0.0097,47522153,237605,238758,26895547,133979,180810\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,528,0.0098,47882153,239405,240558,27103175,134594,195038\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,532,0.0099,48242153,241205,242358,27216804,135653,148537\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,536,0.0100,48602153,243005,244158,27609711,137157,225927\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,540,0.0101,48962153,244805,245958,27856165,138525,222412\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,544,0.0101,49322153,246605,247758,27949313,139206,146089\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,548,0.0102,49682153,248405,249558,28071639,140106,144061\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,552,0.0102,50042153,250205,251358,28221254,140771,147826\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,556,0.0103,50402153,252005,253158,28466442,141994,145849\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,560,0.0105,50762153,253805,254958,28785863,142904,194917\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,564,0.0105,51122153,255605,256758,28851831,143902,156411\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,568,0.0106,51482153,257405,258558,29223120,145608,162476\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,572,0.0108,51842153,259205,260358,29438332,146788,151895\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,576,0.0108,52202153,261005,262158,29557331,147210,151262\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,580,0.0108,52562153,262805,263958,29704990,148198,158557\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,584,0.0108,52922153,264605,265758,29996452,149016,250006\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,588,0.0109,53282153,266405,267558,30123135,150270,154069\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,592,0.0110,53642153,268205,269358,30283611,150978,165439\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,596,0.0110,54002153,270005,271158,30512807,152128,156216\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,600,0.0111,54362153,271805,272958,30713954,153227,157015\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,604,0.0113,54722153,273605,274758,31116246,155098,162946\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,608,0.0113,55082153,275405,276558,31292429,155792,166047\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,612,0.0113,55442153,277205,278358,31367681,156312,187819\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,616,0.0114,55802153,279005,280158,31509163,156923,173955\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,620,0.0115,56162153,280805,281958,31751550,158349,162413\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,624,0.0116,56522153,282605,283758,32010052,159426,164990\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,628,0.0116,56882153,284405,285558,32270071,160471,206182\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,632,0.0118,57242153,286205,287358,32379821,161317,166154\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,636,0.0118,57602153,288005,289158,32621237,162719,174455\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,640,0.0118,57962153,289805,290958,32760054,163283,174727\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,644,0.0119,58322153,291605,292758,32895462,163973,168568\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,648,0.0119,58682153,293405,294558,33046462,164805,176098\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,652,0.0120,59042153,295205,296358,33305627,166069,179927\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,656,0.0121,59402153,297005,298158,33611780,166989,248127\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,660,0.0121,59762153,298805,299958,33791922,168433,184984\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,664,0.0121,60122153,300605,301758,33927065,169140,182483\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,668,0.0124,60482153,302405,303558,34476798,171567,188679\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,672,0.0123,60842153,304205,305358,34350802,171240,175365\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,676,0.0123,61202153,306005,307158,34529315,172118,202239\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,680,0.0124,61562153,307805,308958,34716545,172878,244909\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,684,0.0126,61922153,309605,310758,35111667,174820,186347\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,688,0.0126,62282153,311405,312558,35200811,175517,179013\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,692,0.0126,62642153,313205,314358,35391859,176015,252609\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,696,0.0127,63002153,315005,316158,35696188,177815,200506\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,700,0.0128,63362153,316805,317958,35825556,178736,191521\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,704,0.0129,63722153,318605,319758,36008866,179237,218743\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,708,0.0129,64082153,320405,321558,36282257,180511,214158\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,712,0.0129,64442153,322205,323358,36251857,180793,191833\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,716,0.0131,64802153,324005,325158,36828270,182903,229477\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,720,0.0130,65162153,325805,326958,36775140,183107,213910\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,724,0.0131,65522153,327605,328758,36946255,184028,240244\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,728,0.0132,65882153,329405,330558,37189420,185485,206103\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,732,0.0133,66242153,331205,332358,37526856,187108,192940\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,736,0.0134,66602153,333005,334158,37747623,188004,201070\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,740,0.0134,66962153,334805,335958,37844347,188709,198675\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,744,0.0134,67322153,336605,337758,37874634,189009,203611\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,748,0.0136,67682153,338405,339558,38360815,190893,193995\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,752,0.0137,68042153,340205,341358,38702052,192377,222451\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,756,0.0136,68402153,342005,343158,38548177,192033,249435\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,760,0.0138,68762153,343805,344958,39152996,194437,272148\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,764,0.0138,69122153,345605,346758,39070056,194876,204988\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,768,0.0138,69482153,347405,348558,39192485,195337,208507\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,772,0.0139,69842153,349205,350358,39509976,197063,216644\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,776,0.0140,70202153,351005,352158,39643299,197720,238164\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,780,0.0141,70562153,352805,353958,40047395,199611,212284\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,784,0.0142,70922153,354605,355758,40474213,201350,218018\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,788,0.0143,71282153,356405,357558,40369690,200941,270257\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,792,0.0143,71642153,358205,359358,40667289,202430,244792\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,796,0.0145,72002153,360005,361158,41245212,205315,244622\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,800,0.0144,72362153,361805,362958,41042713,204407,249254\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,804,0.0145,72722153,363605,364758,41137099,205254,211445\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,808,0.0145,73082153,365405,366558,41267168,205869,210553\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,812,0.0146,73442153,367205,368358,41538016,207083,242270\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,816,0.0147,73802153,369005,370158,41856937,208198,257079\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,820,0.0149,74162153,370805,371958,42581251,211598,220361\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,824,0.0148,74522153,372605,373758,42106929,210144,214780\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,828,0.0151,74882153,374405,375558,42954101,213100,216189\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,832,0.0150,75242153,376205,377358,42591682,212393,217281\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,836,0.0150,75602153,378005,379158,42833889,213607,225147\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,840,0.0151,75962153,379805,380958,42888365,213833,258282\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,844,0.0151,76322153,381605,382758,43234463,215605,228741\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,848,0.0152,76682153,383405,384558,43340508,216058,240778\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,852,0.0154,77042153,385205,386358,43964132,218702,263707\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,856,0.0155,77402153,387005,388158,43738562,218168,230126\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,860,0.0154,77762153,388805,389958,44071523,219837,238185\n"
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- "200,32,864,0.0155,78122153,390605,391758,44411093,221177,232408\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,868,0.0157,78482153,392405,393558,44526424,222013,237960\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,872,0.0158,78842153,394205,395358,45188815,224084,346189\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,876,0.0156,79202153,396005,397158,44700630,222996,237268\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,880,0.0158,79562153,397805,398958,45208957,224813,328325\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,884,0.0159,79922153,399605,400758,45474656,226439,239215\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,888,0.0160,80282153,401405,402558,45766475,227867,240911\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,892,0.0160,80642153,403205,404358,45940503,228819,243891\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,896,0.0161,81002153,405005,406158,45973712,229111,241548\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,900,0.0162,81362153,406805,407958,46447521,230613,346027\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,904,0.0163,81722153,408605,409758,46859527,233117,305572\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,908,0.0164,82082153,410405,411558,47123610,234871,284329\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,912,0.0166,82442153,412205,413358,47816182,237201,366650\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,916,0.0166,82802153,414005,415158,47456504,236767,248921\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,920,0.0165,83162153,415805,416958,47592162,237459,265738\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,924,0.0167,83522153,417605,418758,48057683,239541,276783\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,928,0.0167,83882153,419405,420558,48171706,239841,277682\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,932,0.0170,84242153,421205,422358,48721591,242883,245719\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,936,0.0169,84602153,423005,424158,48377712,241387,254877\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,940,0.0169,84962153,424805,425958,48721762,242855,255300\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,944,0.0170,85322153,426605,427758,49035991,243372,370914\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,948,0.0171,85682153,428405,429558,49070436,244800,262067\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,952,0.0171,86042153,430205,431358,49234273,245636,258683\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,956,0.0172,86402153,432005,433158,49586922,247001,316148\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,960,0.0172,86762153,433805,434958,49640943,247637,284307\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,964,0.0177,87122153,435605,436758,51436885,256453,266477\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,968,0.0178,87482153,437405,438558,51146832,254991,267861\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,972,0.0177,87842153,439205,440358,51377929,256333,274159\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,976,0.0179,88202153,441005,442158,51360933,256336,265049\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,980,0.0179,88562153,442805,443958,51845435,258521,293602\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,984,0.0180,88922153,444605,445758,52129373,259818,262711\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,988,0.0181,89282153,446405,447558,52262963,260903,278224\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,992,0.0182,89642153,448205,449358,52407317,261432,272849\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,996,0.0184,90002153,450005,451158,53286503,265403,275404\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,1000,0.0182,90362153,451805,452958,53051777,264487,273734\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,1004,0.0183,90722153,453605,454758,53153647,264834,340140\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,1008,0.0183,91082153,455405,456558,53025643,264711,274578\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,1012,0.0185,91442153,457205,458358,53709439,267192,353247\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,1016,0.0186,91802153,459005,460158,54036527,268786,339099\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,1020,0.0186,92162153,460805,461958,54154888,269844,327020\n",
- "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n",
- "200,32,1024,0.0183,92522153,462605,463758,52875104,262839,332332\n",
- "mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.ins_cyc.bin.csv .\n"
- ]
- }
- ],
- "source": [
- "!make bench_task1"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Once the run is completed, let's have a look at the data!\n",
- "\n",
- "This can be done best in the interactive version of the Jupyter Notebook. In case this version of the description is unavailable to you, call the Makefile target `make graph_task1` (either with X forwarding, or download the resulting PDF)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "import seaborn as sns\n",
- "import pandas as pd\n",
- "import matplotlib.pyplot as plt\n",
- "import common\n",
- "%matplotlib inline\n",
- "sns.set()\n",
- "plt.rcParams['figure.figsize'] = [14, 6]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 77,
- "metadata": {},
- "outputs": [
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- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>iter</th>\n",
- " <th>ny</th>\n",
- " <th>nx</th>\n",
- " <th>Runtime</th>\n",
- " <th>PM_INST_CMPL (total)</th>\n",
- " <th>PM_INST_CMPL (min)</th>\n",
- " <th>PM_INST_CMPL (max)</th>\n",
- " <th>PM_RUN_CYC (total)</th>\n",
- " <th>PM_RUN_CYC (min)</th>\n",
- " <th>PM_RUN_CYC (max)</th>\n",
- " <th>Instructions / Loop Iteration</th>\n",
- " <th>Cycles / Loop Iteration</th>\n",
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- " <td>6623</td>\n",
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- " <td>12.554688</td>\n",
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- " <th>2</th>\n",
- " <td>200</td>\n",
- " <td>32</td>\n",
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- " <td>0.0014</td>\n",
- " <td>1442153</td>\n",
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- " <td>10.903646</td>\n",
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- " <td>32</td>\n",
- " <td>16</td>\n",
- " <td>0.0015</td>\n",
- " <td>1802153</td>\n",
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- "text/plain": [
- " iter ny nx Runtime PM_INST_CMPL (total) PM_INST_CMPL (min) \\\n",
- "0 200 32 4 0.0012 548153 2735 \n",
- "1 200 32 8 0.0014 1082153 5405 \n",
- "2 200 32 12 0.0014 1442153 7205 \n",
- "3 200 32 16 0.0015 1802153 9005 \n",
- "4 200 32 20 0.0016 2162153 10805 \n",
- "\n",
- " PM_INST_CMPL (max) PM_RUN_CYC (total) PM_RUN_CYC (min) \\\n",
- "0 3888 266883 1237 \n",
- "1 6558 668819 3214 \n",
- "2 8358 872913 4187 \n",
- "3 10158 1077532 5254 \n",
- "4 11958 1277957 6209 \n",
- "\n",
- " PM_RUN_CYC (max) Instructions / Loop Iteration Cycles / Loop Iteration \n",
- "0 4793 21.367188 9.664062 \n",
- "1 6623 21.113281 12.554688 \n",
- "2 11640 18.763021 10.903646 \n",
- "3 8147 17.587891 10.261719 \n",
- "4 9015 16.882812 9.701562 "
- ]
- },
- "execution_count": 77,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "plt.rcParams['figure.figsize'] = [14, 6]\n",
- "df = pd.read_csv(\"poisson2d.ins_cyc.bin.csv\", skiprows=range(2, 50000, 2)) # Read in the CSV file from the bench run; parse with Pandas\n",
- "common.normalize(df, \"PM_INST_CMPL (min)\", \"Instructions / Loop Iteration\") # Normalize to each grid cell\n",
- "common.normalize(df, \"PM_RUN_CYC (min)\", \"Cycles / Loop Iteration\")\n",
- "df.head() # Display the head of the Pandas dataframe"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 78,
- "metadata": {},
- "outputs": [
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 1008x432 with 2 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Plot Cycles and Instructions - both per grid cell\n",
- "fig, (ax1, ax2) = plt.subplots(nrows=2, sharex=True)\n",
- "df.set_index(\"nx\")[\"Cycles / Loop Iteration\"].plot(ax=ax1, legend=True);\n",
- "df.set_index(\"nx\")[\"Instructions / Loop Iteration\"].plot(ax=ax2, legend=True);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "What is your result? What value do the graphs come asymptotically close too?\n",
- "\n",
- "We are revisiting the graph in a little while.\n",
- "\n",
- "[Back to top](#toc)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Task 2: Measuring Loads and Stores\n",
- "<a name=\"task2\"></a>\n",
- "\n",
- "Looking at the source code, how many loads and stores from / to memory do you expect? Have a look at the loop which we instrumented.\n",
- "\n",
- "Let's compare your estimate to what the system actually does!\n",
- "\n",
- "<a name=\"task2-a\"></a>**TASK A**: Please measure counters for loads and stores. See the TODOs in [`poisson2d.ld_st.c`](/edit/Tasks/poisson2d.ld_st.c). This time, implement `PM_LD_CMPL` and `PM_ST_CMPL`.\n",
- "\n",
- "Compile with `make task2`, test your program with a single run with `make run_task2`, and then finally submit a benchmarking run to the batch system with `make bench_task2`. The following cell will take care of all this.\n",
- "\n",
- "[Back to top](#toc)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "gcc -DUSE_DOUBLE -Ofast -std=c99 -lm -lpapi poisson2d.ld_st.c -o poisson2d.ld_st.bin\n",
- "bsub -W 60 -nnodes 1 -Is jsrun -n 1 -c 1 -g ALL_GPUS ./bench.sh poisson2d.ld_st.bin /gpfs/wolf/gen110/scratch/aherten//poisson2d.ld_st.bin.csv\n",
- "Job <4032> is submitted to default queue <batch>.\n",
- "<<Waiting for dispatch ...>>\n",
- "<<Starting on login1>>\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,4,0.0012,95115,474,789,21343,106,249\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,8,0.0014,137115,684,999,33343,166,309\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,12,0.0014,197115,984,1299,45343,226,369\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,16,0.0015,257115,1284,1599,63343,316,459\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,20,0.0016,317115,1584,1899,75343,376,519\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,24,0.0016,377115,1884,2199,93343,466,609\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,28,0.0017,437115,2184,2499,105343,526,669\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,32,0.0017,497115,2484,2799,123343,616,759\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,36,0.0018,557115,2784,3099,135343,676,819\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,40,0.0020,617115,3084,3399,153343,766,909\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,44,0.0019,677115,3384,3699,165343,826,969\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,48,0.0020,737115,3684,3999,183343,916,1059\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,52,0.0021,797115,3984,4299,195343,976,1119\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,56,0.0021,857115,4284,4599,213343,1066,1209\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,60,0.0023,917115,4584,4899,225343,1126,1269\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,64,0.0023,977115,4884,5199,243343,1216,1359\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,68,0.0024,1037115,5184,5499,255343,1276,1419\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,72,0.0025,1097115,5484,5799,273343,1366,1509\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,76,0.0025,1157115,5784,6099,285343,1426,1569\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,80,0.0025,1217115,6084,6399,303343,1516,1659\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,84,0.0026,1277115,6384,6699,315343,1576,1719\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,88,0.0027,1337115,6684,6999,333343,1666,1809\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,92,0.0027,1397115,6984,7299,345343,1726,1869\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,96,0.0028,1457115,7284,7599,363343,1816,1959\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,100,0.0029,1517115,7584,7899,375343,1876,2019\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,104,0.0029,1577115,7884,8199,393343,1966,2109\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,108,0.0030,1637115,8184,8499,405343,2026,2169\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,112,0.0030,1697115,8484,8799,423343,2116,2259\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,116,0.0031,1757115,8784,9099,435343,2176,2319\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,120,0.0033,1817115,9084,9399,453343,2266,2409\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,124,0.0032,1877115,9384,9699,465343,2326,2469\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,128,0.0033,1937115,9684,9999,483343,2416,2559\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,132,0.0034,1997115,9984,10299,495343,2476,2619\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,136,0.0035,2057115,10284,10599,513343,2566,2709\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,140,0.0035,2117115,10584,10899,525343,2626,2769\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,144,0.0036,2177115,10884,11199,543343,2716,2859\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,148,0.0036,2237115,11184,11499,555343,2776,2919\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,152,0.0037,2297115,11484,11799,573343,2866,3009\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,156,0.0038,2357115,11784,12099,585343,2926,3069\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,160,0.0038,2417115,12084,12399,603343,3016,3159\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,164,0.0039,2477115,12384,12699,615343,3076,3219\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,168,0.0039,2537115,12684,12999,633343,3166,3309\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,172,0.0040,2597115,12984,13299,645343,3226,3369\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,176,0.0041,2657115,13284,13599,663343,3316,3459\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,180,0.0041,2717115,13584,13899,675343,3376,3519\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,184,0.0042,2777115,13884,14199,693343,3466,3609\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,188,0.0043,2837115,14184,14499,705343,3526,3669\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,192,0.0043,2897115,14484,14799,723343,3616,3759\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,196,0.0044,2957115,14784,15099,735343,3676,3819\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,200,0.0045,3017115,15084,15399,753343,3766,3909\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,204,0.0045,3077115,15384,15699,765343,3826,3969\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,208,0.0046,3137115,15684,15999,783343,3916,4059\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,212,0.0047,3197115,15984,16299,795343,3976,4119\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,216,0.0047,3257115,16284,16599,813343,4066,4209\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,220,0.0048,3317115,16584,16899,825343,4126,4269\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,224,0.0049,3377115,16884,17199,843343,4216,4359\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,228,0.0049,3437115,17184,17499,855343,4276,4419\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,232,0.0050,3497115,17484,17799,873343,4366,4509\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,236,0.0051,3557115,17784,18099,885343,4426,4569\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,240,0.0052,3617115,18084,18399,903343,4516,4659\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,244,0.0052,3677115,18384,18699,915343,4576,4719\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,248,0.0052,3737115,18684,18999,933343,4666,4809\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,252,0.0054,3797115,18984,19299,945343,4726,4869\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,256,0.0054,3857115,19284,19599,963343,4816,4959\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,260,0.0054,3917115,19584,19899,975343,4876,5019\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,264,0.0055,3977115,19884,20199,993343,4966,5109\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,268,0.0056,4037115,20184,20499,1005343,5026,5169\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,272,0.0056,4097115,20484,20799,1023343,5116,5259\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,276,0.0057,4157115,20784,21099,1035343,5176,5319\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,280,0.0057,4217115,21084,21399,1053343,5266,5409\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,284,0.0058,4277115,21384,21699,1065343,5326,5469\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,288,0.0059,4337115,21684,21999,1083343,5416,5559\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,292,0.0059,4397115,21984,22299,1095343,5476,5619\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,296,0.0061,4457115,22284,22599,1113343,5566,5709\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,300,0.0061,4517115,22584,22899,1125343,5626,5769\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,304,0.0061,4577115,22884,23199,1143343,5716,5859\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,308,0.0062,4637115,23184,23499,1155343,5776,5919\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,312,0.0063,4697115,23484,23799,1173343,5866,6009\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,316,0.0064,4757115,23784,24099,1185343,5926,6069\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,320,0.0064,4817115,24084,24399,1203343,6016,6159\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,324,0.0065,4877115,24384,24699,1215343,6076,6219\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,328,0.0065,4937115,24684,24999,1233343,6166,6309\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,332,0.0066,4997115,24984,25299,1245343,6226,6369\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,336,0.0066,5057115,25284,25599,1263343,6316,6459\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,340,0.0068,5117115,25584,25899,1275343,6376,6519\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,344,0.0068,5177115,25884,26199,1293343,6466,6609\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,348,0.0069,5237115,26184,26499,1305343,6526,6669\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,352,0.0071,5297115,26484,26799,1323343,6616,6759\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,356,0.0070,5357115,26784,27099,1335343,6676,6819\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,360,0.0070,5417115,27084,27399,1353343,6766,6909\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,364,0.0071,5477115,27384,27699,1365343,6826,6969\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,368,0.0072,5537115,27684,27999,1383343,6916,7059\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,372,0.0073,5597115,27984,28299,1395343,6976,7119\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,376,0.0073,5657115,28284,28599,1413343,7066,7209\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,380,0.0074,5717115,28584,28899,1425343,7126,7269\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,384,0.0074,5777115,28884,29199,1443343,7216,7359\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,388,0.0075,5837115,29184,29499,1455343,7276,7419\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,392,0.0076,5897115,29484,29799,1473343,7366,7509\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,396,0.0076,5957115,29784,30099,1485343,7426,7569\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,400,0.0078,6017115,30084,30399,1503343,7516,7659\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,404,0.0078,6077115,30384,30699,1515343,7576,7719\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,408,0.0078,6137115,30684,30999,1533343,7666,7809\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,412,0.0079,6197115,30984,31299,1545343,7726,7869\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,416,0.0080,6257115,31284,31599,1563343,7816,7959\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,420,0.0080,6317115,31584,31899,1575343,7876,8019\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,424,0.0081,6377115,31884,32199,1593343,7966,8109\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,428,0.0081,6437115,32184,32499,1605343,8026,8169\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,432,0.0082,6497115,32484,32799,1623343,8116,8259\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,436,0.0083,6557115,32784,33099,1635343,8176,8319\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,440,0.0083,6617115,33084,33399,1653343,8266,8409\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,444,0.0084,6677115,33384,33699,1665343,8326,8469\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,448,0.0085,6737115,33684,33999,1683343,8416,8559\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,452,0.0085,6797115,33984,34299,1695343,8476,8619\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,456,0.0086,6857115,34284,34599,1713343,8566,8709\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,460,0.0087,6917115,34584,34899,1725343,8626,8769\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,464,0.0088,6977115,34884,35199,1743343,8716,8859\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,468,0.0088,7037115,35184,35499,1755343,8776,8919\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,472,0.0089,7097115,35484,35799,1773343,8866,9009\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,476,0.0090,7157115,35784,36099,1785343,8926,9069\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,480,0.0090,7217115,36084,36399,1803343,9016,9159\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,484,0.0091,7277115,36384,36699,1815343,9076,9219\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,488,0.0091,7337115,36684,36999,1833343,9166,9309\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,492,0.0092,7397115,36984,37299,1845343,9226,9369\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,496,0.0093,7457115,37284,37599,1863343,9316,9459\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,500,0.0093,7517115,37584,37899,1875343,9376,9519\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,504,0.0094,7577115,37884,38199,1893343,9466,9609\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,508,0.0095,7637115,38184,38499,1905343,9526,9669\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,512,0.0095,7697115,38484,38799,1923343,9616,9759\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,516,0.0096,7757115,38784,39099,1938343,9691,9834\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,520,0.0097,7817115,39084,39399,1953343,9766,9909\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,524,0.0097,7877115,39384,39699,1968343,9841,9984\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,528,0.0098,7937115,39684,39999,1983343,9916,10059\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,532,0.0099,7997115,39984,40299,1998343,9991,10134\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,536,0.0100,8057115,40284,40599,2013343,10066,10209\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,540,0.0101,8117115,40584,40899,2028343,10141,10284\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,544,0.0101,8177115,40884,41199,2043343,10216,10359\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,548,0.0102,8237115,41184,41499,2058343,10291,10434\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,552,0.0103,8297115,41484,41799,2073343,10366,10509\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,556,0.0104,8357115,41784,42099,2088343,10441,10584\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,560,0.0104,8417115,42084,42399,2103343,10516,10659\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,564,0.0105,8477115,42384,42699,2118343,10591,10734\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,568,0.0106,8537115,42684,42999,2133343,10666,10809\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,572,0.0106,8597115,42984,43299,2148343,10741,10884\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,576,0.0107,8657115,43284,43599,2163343,10816,10959\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,580,0.0109,8717115,43584,43899,2178343,10891,11034\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,584,0.0108,8777115,43884,44199,2193343,10966,11109\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,588,0.0110,8837115,44184,44499,2208343,11041,11184\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,592,0.0110,8897115,44484,44799,2223343,11116,11259\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,596,0.0111,8957115,44784,45099,2238343,11191,11334\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,600,0.0111,9017115,45084,45399,2253343,11266,11409\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,604,0.0112,9077115,45384,45699,2268343,11341,11484\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,608,0.0113,9137115,45684,45999,2283343,11416,11559\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,612,0.0113,9197115,45984,46299,2298343,11491,11634\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,616,0.0114,9257115,46284,46599,2313343,11566,11709\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,620,0.0115,9317115,46584,46899,2328343,11641,11784\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,624,0.0115,9377115,46884,47199,2343343,11716,11859\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,628,0.0115,9437115,47184,47499,2358343,11791,11934\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,632,0.0117,9497115,47484,47799,2373343,11866,12009\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,636,0.0118,9557115,47784,48099,2388343,11941,12084\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,640,0.0119,9617115,48084,48399,2403343,12016,12159\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,644,0.0118,9677115,48384,48699,2418343,12091,12234\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,648,0.0119,9737115,48684,48999,2433343,12166,12309\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,652,0.0121,9797115,48984,49299,2448343,12241,12384\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,656,0.0121,9857115,49284,49599,2463343,12316,12459\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,660,0.0122,9917115,49584,49899,2478343,12391,12534\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,664,0.0122,9977115,49884,50199,2493343,12466,12609\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,668,0.0123,10037115,50184,50499,2508343,12541,12684\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,672,0.0123,10097115,50484,50799,2523343,12616,12759\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,676,0.0125,10157115,50784,51099,2538343,12691,12834\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,680,0.0124,10217115,51084,51399,2553343,12766,12909\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,684,0.0125,10277115,51384,51699,2568343,12841,12984\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,688,0.0126,10337115,51684,51999,2583343,12916,13059\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,692,0.0126,10397115,51984,52299,2598343,12991,13134\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,696,0.0127,10457115,52284,52599,2613343,13066,13209\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,700,0.0128,10517115,52584,52899,2628343,13141,13284\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,704,0.0129,10577115,52884,53199,2643343,13216,13359\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,708,0.0129,10637115,53184,53499,2658343,13291,13434\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,712,0.0129,10697115,53484,53799,2673343,13366,13509\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,716,0.0130,10757115,53784,54099,2688343,13441,13584\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,720,0.0130,10817115,54084,54399,2703343,13516,13659\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,724,0.0132,10877115,54384,54699,2718343,13591,13734\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,728,0.0131,10937115,54684,54999,2733343,13666,13809\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,732,0.0133,10997115,54984,55299,2748343,13741,13884\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,736,0.0135,11057115,55284,55599,2763343,13816,13959\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,740,0.0134,11117115,55584,55899,2778343,13891,14034\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,744,0.0134,11177115,55884,56199,2793343,13966,14109\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,748,0.0135,11237115,56184,56499,2808343,14041,14184\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,752,0.0136,11297115,56484,56799,2823343,14116,14259\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,756,0.0136,11357115,56784,57099,2838343,14191,14334\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,760,0.0138,11417115,57084,57399,2853343,14266,14409\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,764,0.0139,11477115,57384,57699,2868343,14341,14484\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,768,0.0138,11537115,57684,57999,2883343,14416,14559\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,772,0.0140,11597115,57984,58299,2898343,14491,14634\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,776,0.0140,11657115,58284,58599,2913343,14566,14709\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,780,0.0142,11717115,58584,58899,2928343,14641,14784\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,784,0.0141,11777115,58884,59199,2943343,14716,14859\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,788,0.0143,11837115,59184,59499,2958343,14791,14934\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,792,0.0143,11897115,59484,59799,2973343,14866,15009\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,796,0.0146,11957115,59784,60099,2988343,14941,15084\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,800,0.0144,12017115,60084,60399,3003343,15016,15159\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,804,0.0145,12077115,60384,60699,3018343,15091,15234\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,808,0.0146,12137115,60684,60999,3033343,15166,15309\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,812,0.0146,12197115,60984,61299,3048343,15241,15384\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,816,0.0146,12257115,61284,61599,3063343,15316,15459\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,820,0.0148,12317115,61584,61899,3078343,15391,15534\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,824,0.0149,12377115,61884,62199,3093343,15466,15609\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,828,0.0149,12437115,62184,62499,3108343,15541,15684\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,832,0.0149,12497115,62484,62799,3123343,15616,15759\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,836,0.0151,12557115,62784,63099,3138343,15691,15834\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,840,0.0150,12617115,63084,63399,3153343,15766,15909\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,844,0.0152,12677115,63384,63699,3168343,15841,15984\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,848,0.0152,12737115,63684,63999,3183343,15916,16059\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,852,0.0153,12797115,63984,64299,3198343,15991,16134\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,856,0.0153,12857115,64284,64599,3213343,16066,16209\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,860,0.0155,12917115,64584,64899,3228343,16141,16284\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,864,0.0156,12977115,64884,65199,3243343,16216,16359\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,868,0.0157,13037115,65184,65499,3258343,16291,16434\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,872,0.0156,13097115,65484,65799,3273343,16366,16509\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,876,0.0157,13157115,65784,66099,3288343,16441,16584\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,880,0.0158,13217115,66084,66399,3303343,16516,16659\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,884,0.0158,13277115,66384,66699,3318343,16591,16734\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,888,0.0159,13337115,66684,66999,3333343,16666,16809\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,892,0.0160,13397115,66984,67299,3348343,16741,16884\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,896,0.0161,13457115,67284,67599,3363343,16816,16959\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,900,0.0162,13517115,67584,67899,3378343,16891,17034\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,904,0.0163,13577115,67884,68199,3393343,16966,17109\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,908,0.0164,13637115,68184,68499,3408343,17041,17184\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,912,0.0165,13697115,68484,68799,3423343,17116,17259\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,916,0.0165,13757115,68784,69099,3438343,17191,17334\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,920,0.0165,13817115,69084,69399,3453343,17266,17409\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,924,0.0168,13877115,69384,69699,3468343,17341,17484\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,928,0.0167,13937115,69684,69999,3483343,17416,17559\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,932,0.0169,13997115,69984,70299,3498343,17491,17634\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,936,0.0168,14057115,70284,70599,3513343,17566,17709\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,940,0.0169,14117115,70584,70899,3528343,17641,17784\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,944,0.0169,14177115,70884,71199,3543343,17716,17859\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,948,0.0170,14237115,71184,71499,3558343,17791,17934\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,952,0.0171,14297115,71484,71799,3573343,17866,18009\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,956,0.0173,14357115,71784,72099,3588343,17941,18084\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,960,0.0172,14417115,72084,72399,3603343,18016,18159\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,964,0.0177,14477115,72384,72699,3618343,18091,18234\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,968,0.0177,14537115,72684,72999,3633343,18166,18309\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,972,0.0177,14597115,72984,73299,3648343,18241,18384\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,976,0.0179,14657115,73284,73599,3663343,18316,18459\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,980,0.0180,14717115,73584,73899,3678343,18391,18534\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,984,0.0180,14777115,73884,74199,3693343,18466,18609\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,988,0.0180,14837115,74184,74499,3708343,18541,18684\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,992,0.0181,14897115,74484,74799,3723343,18616,18759\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,996,0.0184,14957115,74784,75099,3738343,18691,18834\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,1000,0.0182,15017115,75084,75399,3753343,18766,18909\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,1004,0.0183,15077115,75384,75699,3768343,18841,18984\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,1008,0.0184,15137115,75684,75999,3783343,18916,19059\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,1012,0.0185,15197115,75984,76299,3798343,18991,19134\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,1016,0.0185,15257115,76284,76599,3813343,19066,19209\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,1020,0.0186,15317115,76584,76899,3828343,19141,19284\n",
- "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n",
- "200,32,1024,0.0183,15377115,76884,77199,3843343,19216,19359\n",
- "mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.ld_st.bin.csv .\n"
- ]
- }
- ],
- "source": [
- "!make bench_task2"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Once the run finished, let's plot it again with the following cell (non-interactive: `make graph_task2a`)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>iter</th>\n",
- " <th>ny</th>\n",
- " <th>nx</th>\n",
- " <th>Runtime</th>\n",
- " <th>PM_LD_CMPL (total)</th>\n",
- " <th>PM_LD_CMPL (min)</th>\n",
- " <th>PM_LD_CMPL (max)</th>\n",
- " <th>PM_ST_CMPL (total)</th>\n",
- " <th>PM_ST_CMPL (min)</th>\n",
- " <th>PM_ST_CMPL (max)</th>\n",
- " <th>Loads / Loop Iteration</th>\n",
- " <th>Stores / Loop Iteration</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>200</td>\n",
- " <td>32</td>\n",
- " <td>4</td>\n",
- " <td>0.0012</td>\n",
- " <td>95115</td>\n",
- " <td>474</td>\n",
- " <td>789</td>\n",
- " <td>21343</td>\n",
- " <td>106</td>\n",
- " <td>249</td>\n",
- " <td>3.703125</td>\n",
- " <td>0.828125</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>200</td>\n",
- " <td>32</td>\n",
- " <td>8</td>\n",
- " <td>0.0014</td>\n",
- " <td>137115</td>\n",
- " <td>684</td>\n",
- " <td>999</td>\n",
- " <td>33343</td>\n",
- " <td>166</td>\n",
- " <td>309</td>\n",
- " <td>2.671875</td>\n",
- " <td>0.648438</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>200</td>\n",
- " <td>32</td>\n",
- " <td>12</td>\n",
- " <td>0.0014</td>\n",
- " <td>197115</td>\n",
- " <td>984</td>\n",
- " <td>1299</td>\n",
- " <td>45343</td>\n",
- " <td>226</td>\n",
- " <td>369</td>\n",
- " <td>2.562500</td>\n",
- " <td>0.588542</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>200</td>\n",
- " <td>32</td>\n",
- " <td>16</td>\n",
- " <td>0.0015</td>\n",
- " <td>257115</td>\n",
- " <td>1284</td>\n",
- " <td>1599</td>\n",
- " <td>63343</td>\n",
- " <td>316</td>\n",
- " <td>459</td>\n",
- " <td>2.507812</td>\n",
- " <td>0.617188</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>200</td>\n",
- " <td>32</td>\n",
- " <td>20</td>\n",
- " <td>0.0016</td>\n",
- " <td>317115</td>\n",
- " <td>1584</td>\n",
- " <td>1899</td>\n",
- " <td>75343</td>\n",
- " <td>376</td>\n",
- " <td>519</td>\n",
- " <td>2.475000</td>\n",
- " <td>0.587500</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " iter ny nx Runtime PM_LD_CMPL (total) PM_LD_CMPL (min) \\\n",
- "0 200 32 4 0.0012 95115 474 \n",
- "1 200 32 8 0.0014 137115 684 \n",
- "2 200 32 12 0.0014 197115 984 \n",
- "3 200 32 16 0.0015 257115 1284 \n",
- "4 200 32 20 0.0016 317115 1584 \n",
- "\n",
- " PM_LD_CMPL (max) PM_ST_CMPL (total) PM_ST_CMPL (min) PM_ST_CMPL (max) \\\n",
- "0 789 21343 106 249 \n",
- "1 999 33343 166 309 \n",
- "2 1299 45343 226 369 \n",
- "3 1599 63343 316 459 \n",
- "4 1899 75343 376 519 \n",
- "\n",
- " Loads / Loop Iteration Stores / Loop Iteration \n",
- "0 3.703125 0.828125 \n",
- "1 2.671875 0.648438 \n",
- "2 2.562500 0.588542 \n",
- "3 2.507812 0.617188 \n",
- "4 2.475000 0.587500 "
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df_ldst = pd.read_csv(\"poisson2d.ld_st.bin.csv\", skiprows=range(2, 50000, 2))\n",
- "common.normalize(df_ldst, \"PM_LD_CMPL (min)\", \"Loads / Loop Iteration\")\n",
- "common.normalize(df_ldst, \"PM_ST_CMPL (min)\", \"Stores / Loop Iteration\")\n",
- "df_ldst.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 79,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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S1L9/f33wwQeSpB07dmj9+vXq06dP2J2tC1w8dBUAAACoF6ocGTpy5IgefvhhHTlyRKZpqmnTppo+fboMw9CQIUM0fPhwdenSRc8//7w2bNgg0zTlcrk0ZcqU4GjRH/7wB40ZM0bXX3+9TNPUE088ofj46E97i4RtmTxnCAAAAKgHqgxDLVq00KxZsyrdd/J9Q5mZmac9R2xsrF5++eUIulf32JYhnz8gfyAgkzX1AQAAAMeqcpocynPZpV+Zj6lyAAAAgKMRhsJkW6VfWYk3eos5AAAAAKg+wlCYysIQD14FAAAAnI0wFKayaXKEIQAAAMDZCENhsq3SRRMIQwAAAICzEYbCFLxnyMc9QwAAAICTEYbC5Cq7Z4hnDQEAAACORhgKk809QwAAAEC9QBgKE6vJAQAAAPUDYShMruA9Q4QhAAAAwMkIQ2Gy7eOryfHQVQAAAMDRCENhYpocAAAAUD8QhsLENDkAAACgfiAMhclmaW0AAACgXiAMhYmltQEAAID6gTAUJpdVuoBCiY8FFAAAAAAns0NpNGzYMO3evVumaSo2NlaPP/64UlJSyrWZNm2aFi9eLMuyZNu2RowYoT59+kiSxowZoy+//FJJSUmSpP79++v++++v4Y9ydrCAAgAAAFA/hBSGMjMzlZCQIElaunSpxo4dq7lz55Zr07VrV91zzz1q3Lixvv/+e911111asWKFGjVqJEkaOnSo7rrrrhru/tkXnCbHPUMAAACAo4U0Ta4sCElSYWGhDMOo0KZPnz5q3LixJKlTp04KBALKz8+voW7WHaZhyDINVpMDAAAAHC6kkSFJGjdunFauXKlAIKDXXnvtjG3nzZunc889V+ecc05w25tvvqkPPvhA7dq10x//+Eedf/75kfc6yizLYJocAAAA4HBGIBAIayWAefPmadGiRZoxY0al+//5z39q1KhReuONN9SxY0dJ0r59++TxeGSapubNm6eXXnpJS5culWVZ1f8EUXDH+MX6z25t9X9+0zXaXQEAAAAQobDDkFR6f9A//vGP4IIIZdatW6dHHnlEr7zyijp37nza43v16qWPPvpIbdq0CfmaubmF8vujt4Kbx5OgnJwCSdKIqSt06QUtdPevLo5af1B3nVwrwJlQKwgH9YJQUSsIVX2qFdM01Lx5fPjHVdWgqKhIWVlZwffLli1T06ZNlZiYWK7dt99+qxEjRujll1+uEIT27dsXfL18+XKZpqlWrVqF3dm6wrZMpskBAAAADlflPUNHjhzRww8/rCNHjsg0TTVt2lTTp0+XYRgaMmSIhg8fri5dumjSpEk6evSoJkyYEDx2ypQp6tSpk0aPHq3c3FwZhqH4+Hi9+uqrsu2Qb1eqc2ybMAQAAAA4XZWJpEWLFpo1a1al+06+b+jDDz887TlmzpwZfs/qMJdlqISltQEAAABHC2lpbZRXOk0uevcvAQAAAKg+wlAEmCYHAAAAOB9hKAIuy+ShqwAAAIDDEYYiYFumvNwzBAAAADgaYSgCtmUwTQ4AAABwOMJQBFy2qRIWUAAAAAAcjTAUAabJAQAAAM5HGIpA6dLahCEAAADAyQhDEXARhgAAAADHIwxFwLYNHroKAAAAOBxhKAJMkwMAAACcjzAUAZdlyucPyB9gdAgAAABwKsJQBGy79GtjRTkAAADAuQhDEbCt42GIqXIAAACAYxGGIuCyDEniwasAAACAgxGGIhAcGWKaHAAAAOBYdiiNhg0bpt27d8s0TcXGxurxxx9XSkpKuTY+n0+TJ0/W8uXLZRiGhg4dqoEDB1a5z4mC9wwxTQ4AAABwrJDCUGZmphISEiRJS5cu1dixYzV37txybRYsWKBdu3bps88+U35+vtLT03XVVVepbdu2Z9znRK7jI0MlhCEAAADAsUKaJlcWhCSpsLBQhmFUaLN48WINHDhQpmmqWbNm6tu3r5YsWVLlPidiAQUAAADA+UIaGZKkcePGaeXKlQoEAnrttdcq7M/KylLr1q2D75OTk5WdnV3lPieyjy+g4PWygAIAAADgVCGHoaeeekqSNG/ePE2ZMkUzZsyotU5Vpnnz+LN6vcp4PKUjZC0OHZMkxSXEBLcBJ6MuECpqBeGgXhAqagWhaui1EnIYKpOenq4JEyYoLy9PSUlJwe3Jycnau3evunbtKqn8aNCZ9oUqN7dQfn/0RmI8ngTl5BRIkgoLj0qSDuQWKadpo6j1CXXTybUCnAm1gnBQLwgVtYJQ1adaMU0josGTKu8ZKioqUlZWVvD9smXL1LRpUyUmJpZr179/f82ePVt+v18HDx7U0qVL1a9fvyr3OZGLpbUBAAAAx6tyZOjIkSN6+OGHdeTIEZmmqaZNm2r69OkyDENDhgzR8OHD1aVLF6Wlpembb77RDTfcIEl64IEH1K5dO0k64z4nsoMPXSUMAQAAAE5VZRhq0aKFZs2aVem+k+8bsixLkyZNqrTdmfY5Ec8ZAgAAAJwvpKW1UV5wmpyP1eQAAAAApyIMRaDsOUMl3DMEAAAAOBZhKAI8dBUAAABwPsJQBFz28YeuEoYAAAAAxyIMRcBimhwAAADgeIShCJiGIcs0WEABAAAAcDDCUIRs22SaHAAAAOBghKEIuSyTh64CAAAADkYYipBtGfJyzxAAAADgWIShCNkW0+QAAAAAJyMMRchlmyphAQUAAADAsQhDEbItk2lyAAAAgIMRhiLENDkAAADA2QhDEXJZBmEIAAAAcDDCUIRKnzPEPUMAAACAUxGGImTznCEAAADA0eyqGuTl5WnUqFHatWuX3G63zjvvPD3xxBNq1qxZuXZ333238vLyJEk+n09btmzR/PnzdfHFF2vMmDH68ssvlZSUJEnq37+/7r///lr4OGePi3uGAAAAAEerMgwZhqF7771XvXr1kiRlZmbqz3/+s55++uly7WbOnBl8vXTpUr344ou6+OKLg9uGDh2qu+66q4a6HX22zWpyAAAAgJNVOU0uMTExGIQk6bLLLtPevXvPeMycOXN0yy23VL93dZjNAgoAAACAo4V1z5Df79d7772nX/7yl6dtc+DAAa1atUppaWnltr/55psaMGCAhg0bpm3btkXW2zqk9J4hFlAAAAAAnKrKaXIne/LJJxUbG3vG6W5z585Vnz59yt1TNGLECHk8HpmmqXnz5unee+/V0qVLZVlWyNdu3jw+nK7WCo8nIfg6IT5GPn+g3DagDHWBUFErCAf1glBRKwhVQ6+VkMNQZmamdu7cqenTp8s0Tz+g9NFHH2nUqFHltrVq1Sr4Oj09Xc8884yys7PVpk2bkDuam1sovz96IzEeT4JycgqC773FPpWU+MptA6SKtQKcDrWCcFAvCBW1glDVp1oxTSOiwZOQpsm98MIL+u677zRt2jS53e7Ttlu7dq0KCgp0zTXXlNu+b9++4Ovly5fLNM1yAcmJbNtgaW0AAADAwaocGdqyZYumT5+u9u3b6/bbb5cktW3bVtOmTVNaWpr+9re/BYPNRx99pPT09ArT30aPHq3c3FwZhqH4+Hi9+uqrsu2wZujVObZlKhCQfH6/rDOMlAEAAACom6pMJBdeeKE2b95c6b758+eXez958uRK25287HZ94bJKA5DXG5B1+sEyAAAAAHUUQxoRso+HIabKAQAAAM5EGIqQbR8fGSIMAQAAAI5EGIpQXKPSGYaHCouj3BMAAAAAkSAMRejCtomSpO935UW5JwAAAAAiQRiKUFJCjFo1i9WmnYQhAAAAwIkIQ9WQcl6SfvgpXz4/9w0BAAAATkMYqoaLz03U0WKfdmTXjyf3AgAAAA0JYagaLj43SZL0PVPlAAAAAMchDFVDkzi32njiCEMAAACAAxGGqinl3CRt2X1IJV7uGwIAAACchDBUTRefl6Rir1/bs36OdlcAAAAAhIEwVE2dzk2UIbHENgAAAOAwhKFqimvk0rmtErRpx8FodwUAAABAGAhDNeDSC5pry+5D2p1TGO2uAAAAAAgRYagG9O3eTjFuS/NXbI92VwAAAACEyK6qQV5enkaNGqVdu3bJ7XbrvPPO0xNPPKFmzZqVazdmzBh9+eWXSkoqffZO//79df/990uSDhw4oFGjRmnPnj2KiYnRk08+qUsvvbQWPk50xDd26YYe7fTxyh3amV2g885JiHaXAAAAAFShypEhwzB077336tNPP9WCBQvUrl07/fnPf6607dChQzV//nzNnz8/GIQk6bnnnlP37t316aefasKECRo5cqQCgUDNfYo64IYe7RTXyNa85T9GuysAAAAAQlBlGEpMTFSvXr2C7y+77DLt3bs3rIssWbJEt99+uySpe/fuiomJ0fr168Psat0W28ilfj3P1TfbcrVt76FodwcAAABAFcK6Z8jv9+u9997TL3/5y0r3v/nmmxowYICGDRumbdu2SSqdZhcIBMpNq0tOTlZ2dnY1ul039e3eVvGNXXrn0x90rMQX7e4AAAAAOIMq7xk62ZNPPqnY2FjdddddFfaNGDFCHo9Hpmlq3rx5uvfee7V06dIa62jz5vE1dq5IeTxV3wv0yO2X66mZ/9Tbn/2gMf+7h0zTOAs9Q10TSq0AErWC8FAvCBW1glA19FoJOQxlZmZq586dmj59ukyz4oBSq1atgq/T09P1zDPPKDs7W23atJEkHTx4MDg6lJWVpXPOOSesjubmFsrvj959Rh5PgnJyCqps17FVvH577QV6f9lWvTrn37rt2gvOQu9Ql4RaKwC1gnBQLwgVtYJQ1adaMU0josGTkKbJvfDCC/ruu+80bdo0ud3uStvs27cv+Hr58uUyTTMYkPr376/3339fkrRmzRodPXpUl1xySdiddYrre7TTtZe30ZLVu/Txyu3y17PFIgAAAID6oMqRoS1btmj69Olq3759cBGEtm3batq0aUpLS9Pf/vY3tWrVSqNHj1Zubq4Mw1B8fLxeffVV2Xbp6f/4xz/q0Ucf1bx58xQTE6MpU6ZUOrpUXxiGoUHXX6gjx7yat3y7dmQV6N7UFMU2ckW7awAAAACOMwIOWePaKdPkThYIBLT0692atWyrmjdtpN/166TO7ZtVfSAcrT4NOaN2USsIB/WCUFErCFV9qpVanSaHyBiGoeu7t9Ojd1wuvz+g597/t16c/Y32HCiKdtcAAACABo8wdBZc1C5RTw3ppYHXnq8tuw/p8ddW6/lZ/9a/tx6I6mgXAAAA0JCFtbQ2IueyLf2q13m6ukuylq3do//37z16ec63SkqIUbeLPOreyaML2yayFDcAAABwlhCGzrKEWLfSru6gX191ntZtOaCvNmTrH//eq//79W41jrF18bmJSjkvSee3aaq2nji5bCvaXQYAAADqJcJQlNiWqR4Xt1SPi1vqaLFX327L1cYdB7VxR57WbTkgSTINQ61bxOq8Vgk695wEndsyXi2TYpUY75ZhMIIEAAAAVAdhqA5o5LbVM6WVeqaUPpfpwKEj2pldoJ37CrQzu1Drf8zVyu+yg+3dtilPUmO1TGyslkmN1SyhkZrGu5UYH1P6Oy5GMW5GlAAAAIAzIQzVQS2aNlaLpo11RaeWkkqX6M4vLNaenELtzz+i/XmlP/vyjmj9jwfl9fkrnKNxjKWmcTFKjHerSZxbcY1cimtsKzam9Hd8I5diG9mKa+xSXCOXGrktxbgtmYw4AQAAoIEgDDmAYRhKSohRUkJMhX3+QEBFR0p0qLBY+UXHlF9QrENFx5RfWKxDhceUX1SsHdkFOnzUq6KjJarqqVJul6lGbluNXFYwIDVy28d/W2rksuRymXLblly2KZdlyuU6/tsu/QnuO/nHMuV2WcF2LBQBAACAaCMMOZxpGEqIdSsh1q22OvODpvyBgI4e8+nw0RIVHQ9HZb+PHvPpWIlPR4u9Olrs07Fin44Wl74vOFysA4dOvC/x+uX1VW9JcEOSZRmyLFO2acgyS1+X/S63zTJO38YyZJnm8d+lP6ZhyDQNGUbZ+9IHcZnGSduObzeOby87zjBLv9OybUbwfDqxzTCC5yvbXnY905COBaT8vMMyjNIgaxgKXrv0z+zE9rJjTuwzKjlO3CMGAABQCwhDDYhpGIptZCu2ka0W1TyXPxBQidd/0o+v9LfPr+KS0t+oSiT7AAAgAElEQVQV9p304/UH5PP75fMFSn/8x7f5/PL5y7YF5A2+P36cz1t63PE23rJzlJ3PH5DfXzq10FfPnuFk6DQhyjgesHS6fce3q2LACv4+fgHj+JWMstfHjyvbZxhV7FdpoxNtTwS5E8cY5Y4Ntjulbdmx5fpWybkj73v5Y09uq5OOP3Gtss0ndVjl25+8v7L8apzSOC7OrcNFxRXaBr+HU85dflv5NuGdwzil7ZnPfepnrSyc18g5KrSt/Lus8GdRyfmrPscp/avk/JXtrfTPtZJzVLbx1O/99O0q71PTA4d16NDhM7aMWv8qvcYZ/oyrbnqadqE1rPx7iPzzhfp9hf45QjtfqMeeqrDEr7y8wxW2h/7ZQrvwmf/7ObMz/m9ACDtC7WPI14ikXQiNavLPNVTh/AOq6baVV3DsDOeq4njTUJNYd8jXq4sIQ4iIaRiKcVmKcdXthRr8gYD8/oACgdKQ5D8ekvyBgAL+gPwBye8PyBd8X9q+bPuJ92faXnreQCCgQECKT2ikn38+EnzvP/677H1AOsO+E+/9J28/ZX+F40567dfJ+0rPo5PPWck1dFK/JAWnUwYCAZVFyrLrl7XV8X0ntz3RTgr4j+8PHnPydVS6J3iu0+wP7qt4/rLrlx17oh8njq3wOULs+/Gt5fp3Uo90yq+Tpp8GTnl/QqCycxiVtwUAwCmGDviFrux8TrS7ETHCEOo10zBkWjX4zy0h8HgSlJNTcFavCWeqrFaCwSy44aR9pwlbpwtjp54jcHLUq+ocp213clw89fzlDwrlHKde/7SB8zTnrrTtKeev+H2Ef47TCTXMBnT6zxzqNZKSYoP/2l/Z+ULcdJp21elf9b6ryvtYSX9CPrayTZF/X5X3O7TvK1r9a9Kk9B/lqmoXYveq9WdcrVo9bdvI6zUUoZyr0s9VsVFo1wutWWjnCqHzJ7dIiG+kgsKjVTc8DdM01PX85qF1ro4iDAFAHXKm6W2hT/BAfeTxJCinMf+3jarxj3IIFbUimdHuAAAAAABEA2EIAAAAQINEGAIAAADQIDlm8nFdeEhnXegDnIFaQaioFYSDekGoqBWEqr7USqSfwwiEukQIAAAAANQjTJMDAAAA0CARhgAAAAA0SIQhAAAAAA0SYQgAAABAg0QYAgAAANAgEYYAAAAANEiEIQAAAAANEmEIAAAAQINEGAIAAADQIBGGAAAAADRIhCEAAAAADRJhCAAAAECDRBgCAAAA0CARhgAAAAA0SIQhAAAAAA0SYQgAAABAg0QYAgAAANAgEYYAAAAANEiEIQAAAAANEmEIAAAAQINEGAIAAADQIBGGAAAAADRIhCEAAAAADRJhCAAAAECDRBgCAAAA0CARhgAAAAA0SIQhAAAAAA0SYQgAAABAg0QYAgAAANAgEYYAAAAANEh2tDsQqry8Ivn9gahdv3nzeOXmFkbt+nAOagWholYQDuoFoaJWEKr6VCumaSgpKS7s4xwThvz+QFTDUFkfgFBQKwgVtYJwUC8IFbWCUDX0WmGaHAAAAIAGiTAEAAAAoEEiDAEAAABokBxzz1BdcPioV8+887X+T1pntfXER7s7AAAAZ5XP51VeXo683uJodwU1YP9+U36/P9rdCJttu5WU5JFlVT/KEIbC8PPhYu05UKTd+wsJQwAAoMHJy8tRo0axios7R4ZhRLs7qCbbNuX1OisMBQIBFRX9rLy8HLVokVzt8zFNLgy2VfoffYnPWUUDAABQE7zeYsXFNSEIIWoMw1BcXJMaG50kDIXBZZV+XV5fw16CEAAANFwEIURbTdYgYSgMtl36dZU4bDgRAACgPlq2bKl+//tBuvvuQRo06BZNnDguuO/11/+qkpKSKPZOmjnzNX344awK2x98cKhWrlx+1vvz1FMT9eGHH0iS1q5do9WrV9XKdWbNeld5eQeD7+fNm6MPPvh7rVyrurhnKAwnRoYIQwAAANF04MABPf/8n/T66++oVatzFAgEtHXrD8H9b745Q3fc8Tu5XK6wzuv1emXbNfNX5BUrvtBTT02pkXPVtHXrvtaxY0d1xRW9wj7W5/PJsqzT7p816z11795TSUnNJEnp6bdG3M/aRhgKQ9nIkNNuNAMAAKhvDh48IMuy1bRpoqTSqVMXXthJkvTcc5mSpPvvv0eGYWrq1L+qpKRYzz77jPbu3a1AIKA77vidfvWrVEnSrbcOUGpqmr7++l9q3bqNHntsgj75ZKE++mi2fD6f4uPjNXLkGJ17bnutX/+NXnhhivz+gLxerwYPvkfXX9+/Qv/279+nQCCgVq3OCfkz7d79k5599mnl5+fJsiwNHfqArrzyPyRJX331pf7617/I7/crMTFJjz46Vm3bttPatWv00kvPqVOni7V16w+yLEtjx05Uhw4dT3udbdu2av78jxQI+PXPf67WddfdoN/97m6tWrVCb7/9ho4dK5bL5dJDD/2XLrmki9auXaOXX35el156mTZt2qjBg/+goqIizZ79nrze0tG3Bx54RN2799Rbb72uAwdyNH78aLndMcrImKxlyz7XkSNH9OCDj8jn8+nVV6dq9eovJUm9ev2H7r//IVmWpaeemii3262fftql/fv3qXPnLho/flKtTs0kDIXBNAxZpsECCgAAoMFbuT5LK77NqpVzX901Wb27nHmlsAsuuEi/+EVn3XLLr3X55Veoa9fL1K/fjWraNFF//ONozZ07W6+++oZiY2MlSRMmPKaOHc/XM8/8WQcOHNAf/nCnOnW6WB07XiCpdKRp6tS/SpK++Wadli37XNOmzZDb7daqVSv1zDNP6NVX39Df//6WbrttkPr3/7UCgYAKCwsr7d/y5f/Q1VdfE9bnnjRpvNLSblZqarq2b/9RDz44RO+8M0dSQJMnT9DUqX9Thw4dtXDhPE2aNF4zZrwlSdq2bYseeWSkLr/8Cn3yyUJNnpyh11//79Ne5/zzL1Ba2m907NhRDRv2sCRpz57dmjnzdT3//FTFxcXrxx+3aeTI4froo0WSpB9/3KqRI8doxIhRkqRDh/J1/fX9ZBiGdu3aoYcfHqa5cxdr8OA/aMGCeZo8OTP43Z7s44/nasuWH/TGG6XT5kaOHK6PP56rm2++9fh1tunFF1+RaZr6/e/v1Jo1q9Wjx5VhfY/hIAyFybZMpskBAABEmWmaeuaZ5/Tjj1u1bt1aLV/+//Tuu/+tt99+X02aNK3Qfs2af+rBBx+RJLVo0UJXXXW11q5dE/wLe//+vw62XbnyC23dukVDh94tqXQ554KCnyVJ3bp11zvvzFR2dpZ69LhSnTtfUmn/Vqz4RzBohOLw4SJt3fqDbrzxJklShw4ddcEFnbRhw3pJ0vnnXxQc7bnxxpv03HOZOny4SJLUtm07XX75FZKkfv1u1JQpT6moqFBxcaE/Cmb16lXas2e3HnhgaHCbz+fTwYO5wWtccknX4L49e3Zr4sRxysnJkW3bOngwV7m5B9S8eYszXmfNmtW68cbU4PTFG28coC+++J9gGOrT5z8VExMjSerUqZP27NmtHj1C/hhhIwyFybYMeb2sJgcAABq23l2qHr05Gzp2vEAdO16gW265TXfdNVDr1n2t//W/fllp21OnW538Pja2cfB1ICD9+tc36d5776twjttuG6Teva/Rv/61Wi++OEU9elypoUOHlWtTWFiorKwsXXjhRSF/jkCg8r9fGoahQMCv2l7ELxAIqFevq/T4409U2Ldjx3Y1bhxbbtvEieP04IMjdM01/ym/36++fa9WcXHVy10HAmf+c4iJcQdfm6Yln88X7kcJC6vJhcm2TabJAQAARFlOzn599923wff79+9Tfn6ekpNbS5JiY+NUVHRiClv37j318cdzJUm5uQe0atVKXX5590rP3bt3Hy1Zskj79++TVDpC8v33myRJu3btVJs2bZWefosGDrxDmzZtqHD8qlUrgvf6hCouLl4XXHCRPvlkoSRp584d2rbtB/3iF5eoc+eu2rr1B+3cuUOS9MknC3XhhZ0UGxsnqfReo2++WSdJ+vzzJerY8YIqR4Xi4uLKTfHr2fNKrV69Sj/+uC24rbLPVqawsDD4XS9cOL9cEDr13Cfr0aOXFi9eIK/XK6/Xq08+Waju3Xuesa+1iZGhMLmYJgcAABB1Pp9Pr7/+V2VnZykmppECAb/uvfd+XXTRxZKk22+/U8OH36eYmEaaOvWveuSRkXr22ac1ePDtCgQCuu++B9Wx4/mVnvuyy7pp6NBhGjPmv+Tz+eX1lujaa/vq4otTNGfO+1q79mu5XLZcLrdGjHi0wvHLl/9DaWm/OWP/n356otzumOD7Z599SRkZk/Xss09r1qx3ZVmWxo9/QklJSZKk8eOf0KRJ4+Tz+ZSYmKQJE54MHnvhhRfp888/1UsvPSfLMjV+/KQqv79rrrlW48eP0t13DwouoDBhwpP605+e1LFjx+T1lqhLl0uVktK50uOHD/8vjR07Ui1aeHTZZd3UtOmJqYm33nq7nn76CTVq1EgZGZPLHXfTTTdr9+6f9PvfD5Ik9ex5lQYMuLnK/tYWI3C6MbmTbN++XWPGjFF+fr4SExOVmZmp9u3bl2uTm5urxx57TFlZWSopKdGVV16p8ePHy7Zt+Xw+TZ48WcuXL5dhGBo6dKgGDhwYVkdzcwvl90dveprHk6CcnAKN/dtXatcyXvenVz4/FCirFaAq1ArCQb0gVLVZK9nZO3XOOefVyrnri5KSEt1xx2/0/vtza2yJ7jNZu3aNpk176YwLJpyObZuOXSX51Fo0TUPNm4d+j1TwuFAaZWRkaNCgQfr00081aNAgTZgwoUKb6dOn6/zzz9eCBQu0YMECbdiwQZ999pkkacGCBdq1a5c+++wzffDBB5o6dap2794ddmfrApfNyBAAAAAq53K5NGfOgrMShFB9VYah3Nxcbdy4Uamppeuwp6amauPGjTp48GC5doZhqKioSH6/X8XFxSopKVGrVq0kSYsXL9bAgQNlmqaaNWumvn37asmSJbXwcWqfbXHPEAAAAOqGbt26RzQqhFJVhqGsrCy1atUq+JRZy7LUsmVLZWWVX1d+2LBh2r59u66++urgzxVXXBE8R+vWrYNtk5OTlZ2dXZOf46xxWYZjhxMBAAAAnFBj43dLlixRp06d9NZbb6moqEhDhgzRkiVL1L9/xSfyRiKSOYA1zeNJUGxjt46V+OTxJES7O6jDqA+EilpBOKgXhKq2amX/flOWZVRYGhnOZdvOW1w6EAjINM0aqfMqw1BycrL27dsnn88nyypd63v//v1KTi6/rvw777yjp59+WqZpKiEhQb/85S+1evVq9e/fX8nJydq7d6+6di19UNOpI0WhqCsLKPj9fh0+WsJNrDgtbnJGqKgVhIN6Qahqs1ZM09ahQ/mKi2tCIKoHnLiAQiAQUFHRzzJNu1ydR7qAQpVhqHnz5kpJSdHChQuVlpamhQsXKiUlRc2aNSvXrm3btvriiy/UtWtXFRcXa9WqVbr++uslSf3799fs2bN1ww03KD8/X0uXLtXf//73sDtbF7C0NgAAaKiSkjzKy8tRYWF+tLuCGmCapvx+5/291rbdSkry1My5Qmk0ceJEjRkzRq+88oqaNGmizMxMSdKQIUM0fPhwdenSRWPHjlVGRoYGDBggn8+nXr166bbbbpMkpaWl6ZtvvtENN9wgSXrggQfUrl27GvkAZ5ttmypxWIIGAACoCZZlq0WL5KobwhEYcQ7xOUN1QV2ZJvf6oo3atDNPfx7WO2p9Qd3G/7AgVNQKwkG9IFTUCkJVn2qlVp8zhBNctuW4uZUAAAAAKiIMhcm2DJX4HDGYBgAAAOAMCENhYgEFAAAAoH4gDIXJtkqXIHTIrVYAAAAAToMwFCbbNhWQ5IviYg4AAAAAqo8wFCaXVfqVMVUOAAAAcDbCUJhsq/RpyzxrCAAAAHA2wlCYbLtsZIhpcgAAAICTEYbCVDZNroRpcgAAAICjEYbC5CobGWKaHAAAAOBohKEw2SygAAAAANQLhKEw2UyTAwAAAOoFwlCYXMdXk2OaHAAAAOBshKEwsZocAAAAUD8QhsIUnCbHyBAAAADgaIShMLlYQAEAAACoFwhDYSpbWpsFFAAAAABnIwyFKbi0NtPkAAAAAEcjDIXpxAIKhCEAAADAyexQGm3fvl1jxoxRfn6+EhMTlZmZqfbt25drM2rUKG3evDn4fvPmzZo2bZquu+46TZ06Ve+++65atmwpSerWrZsyMjJq7lOcRWVLa5ewmhwAAADgaCGFoYyMDA0aNEhpaWmaP3++JkyYoLfffrtcmylTpgRff//99xo8eLD69OkT3Jaenq7Ro0fXULejx2YBBQAAAKBeqHKaXG5urjZu3KjU1FRJUmpqqjZu3KiDBw+e9pg5c+ZowIABcrvdNdfTOqJsmhxLawMAAADOVmUYysrKUqtWrWRZliTJsiy1bNlSWVlZlbYvLi7WggULdMstt5TbvmjRIg0YMED33HOP1q1bVwNdjw7TMGSZBiNDAAAAgMOFNE0uHEuXLlXr1q2VkpIS3Hb77bfrvvvuk8vl0sqVKzVs2DAtXrxYSUlJIZ+3efP4mu5q2DyeBEmly2u73HbwPXAqagOholYQDuoFoaJWEKqGXitVhqHk5GTt27dPPp9PlmXJ5/Np//79Sk5OrrT9hx9+WGFUyOPxBF/37t1bycnJ2rJli3r27BlyR3NzC+X3R2/RAo8nQTk5BZJK7xv6ueBo8D1wspNrBTgTagXhoF4QKmoFoapPtWKaRkSDJ1VOk2vevLlSUlK0cOFCSdLChQuVkpKiZs2aVWibnZ2tr7/+Onh/UZl9+/YFX2/atEl79uxRhw4dwu5sXWFbTJMDAAAAnC6kaXITJ07UmDFj9Morr6hJkybKzMyUJA0ZMkTDhw9Xly5dJElz587Vtddeq8TExHLHP//889qwYYNM05TL5dKUKVPKjRY5jW2ZKvGytDYAAADgZEYgEHDE3+rr0jS5cTO+UltPvO5PvyRq/UHdVZ+GnFG7qBWEg3pBqKgVhKo+1UqtTZNDRbZlMk0OAAAAcDjCUARKp8kRhgAAAAAnIwxFwMUCCgAAAIDjEYYiYNumSghDAAAAgKMRhiLgskx5WU0OAAAAcDTCUARsmwUUAAAAAKcjDEXAZTFNDgAAAHA6wlAEWFobAAAAcD7CUARs25SXpbUBAAAARyMMRcC2DKbJAQAAAA5HGIqAyzJVwmpyAAAAgKMRhiLgOr6aXCBAIAIAAACcijAUAdsq/dp8fsIQAAAA4FSEoQiUhaESFlEAAAAAHIswFAGXXfq1sbw2AAAA4FyEoQjYliFJ8vqYJgcAAAA4FWEoAiemyfmi3BMAAAAAkSIMRaBsmlwJI0MAAACAYxGGIlA2MuRlAQUAAADAsexQGm3fvl1jxoxRfn6+EhMTlZmZqfbt25drM2rUKG3evDn4fvPmzZo2bZquu+46+Xw+TZ48WcuXL5dhGBo6dKgGDhxYox/kbGIBBQAAAMD5QgpDGRkZGjRokNLS0jR//nxNmDBBb7/9drk2U6ZMCb7+/vvvNXjwYPXp00eStGDBAu3atUufffaZ8vPzlZ6erquuukpt27atwY9y9gRHhghDAAAAgGNVOU0uNzdXGzduVGpqqiQpNTVVGzdu1MGDB097zJw5czRgwAC53W5J0uLFizVw4ECZpqlmzZqpb9++WrJkSQ19hLPPVbaAAmEIAAAAcKwqw1BWVpZatWoly7IkSZZlqWXLlsrKyqq0fXFxsRYsWKBbbrml3Dlat24dfJ+cnKzs7Ozq9j1qbPv40tpeFlAAAAAAnCqkaXLhWLp0qVq3bq2UlJQaPW/z5vE1er5IeDwJkqTDx0NQbFxMcBtwMuoCoaJWEA7qBaGiVhCqhl4rVYah5ORk7du3Tz6fT5Zlyefzaf/+/UpOTq60/YcfflhuVKjsHHv37lXXrl0lVRwpCkVubqH8/uiNxHg8CcrJKZAkFfx8pLRPB4uC24AyJ9cKcCbUCsJBvSBU1ApCVZ9qxTSNiAZPqpwm17x5c6WkpGjhwoWSpIULFyolJUXNmjWr0DY7O1tff/118P6iMv3799fs2bPl9/t18OBBLV26VP369Qu7s3WFzT1DAAAAgOOF9JyhiRMn6p133lG/fv30zjvvaNKkSZKkIUOGaP369cF2c+fO1bXXXqvExMRyx6elpalt27a64YYbdNttt+mBBx5Qu3btavBjnF02S2sDAAAAjhfSPUPnn3++Zs+eXWH7jBkzyr2///77Kz3esqxggKoPXDx0FQAAAHC8kEaGUJ7r+GpyTJMDAAAAnIswFAEr+NBVltYGAAAAnIowFAHTMGSZBvcMAQAAAA5GGIqQbZsq4Z4hAAAAwLEIQxFyWSb3DAEAAAAORhiKkG0ZrCYHAAAAOBhhKEK2ZXLPEAAAAOBghKEIuWxTJawmBwAAADgWYShCLstkmhwAAADgYIShCNk20+QAAAAAJyMMRYh7hgAAAABnIwxFyGUZLK0NAAAAOBhhKEK2xUNXAQAAACcjDEWo9J4hVpMDAAAAnIowFCFWkwMAAACcjTAUIds2uWcIAAAAcDDCUIRcrCYHAAAAOBphKEIsrQ0AAAA4G2EoQrZtqMTLAgoAAACAU9mhNNq+fbvGjBmj/Px8JSYmKjMzU+3bt6/QbvHixXr11VcVCARkGIbefPNNtWjRQlOnTtW7776rli1bSpK6deumjIyMGv0gZ1vZNLmyzwoAAADAWUIKQxkZGRo0aJDS0tI0f/58TZgwQW+//Xa5NuvXr9df/vIXvfXWW/J4PCooKJDb7Q7uT09P1+jRo2u291FkW6WDal5fQC6bMAQAAAA4TZXT5HJzc7Vx40alpqZKklJTU7Vx40YdPHiwXLuZM2fqnnvukcfjkSQlJCQoJiamFrpcN5wIQ9w3BAAAADhRlWEoKytLrVq1kmVZkiTLstSyZUtlZWWVa7dt2zb99NNPuvPOO3XzzTfrlVdeUSBw4p6aRYsWacCAAbrnnnu0bt26Gv4YZ5/LLv3qWF4bAAAAcKaQpsmFwufzafPmzXrzzTdVXFyse++9V61bt1Z6erpuv/123XfffXK5XFq5cqWGDRumxYsXKykpKeTzN28eX1NdjZjHkxB8nZQYK0lq0iRWnqTG0eoS6qiTawU4E2oF4aBeECpqBaFq6LVSZRhKTk7Wvn375PP5ZFmWfD6f9u/fr+Tk5HLtWrdurf79+8vtdsvtduu6667Tt99+q/T09ODUOUnq3bu3kpOTtWXLFvXs2TPkjubmFsrvj97qbR5PgnJyCoLv/SVeSdJPe/Mlrzda3UIddGqtAKdDrSAc1AtCRa0gVPWpVkzTiGjwpMppcs2bN1dKSooWLlwoSVq4cKFSUlLUrFmzcu1SU1O1YsUKBQIBlZSU6KuvvtLFF18sSdq3b1+w3aZNm7Rnzx516NAh7M7WJQmxLklSweHiKPcEAAAAQCRCmiY3ceJEjRkzRq+88oqaNGmizMxMSdKQIUM0fPhwdenSRb/+9a/13Xff6cYbb5Rpmrr66qt16623SpKef/55bdiwQaZpyuVyacqUKeVGi5woIbZ0pbyCwyVR7gkAAACASBiBk1c5qMPq2jS5gsPFevjlFbqj74W6vnu7qPULdU99GnJG7aJWEA7qBaGiVhCq+lQrtTZNDpWLa+ySYTAyBAAAADgVYShCpmEoobGLe4YAAAAAhyIMVUNCrJuRIQAAAMChCEPVkBDLyBAAAADgVIShakiIdetnRoYAAAAARyIMVUNCrEuFjAwBAAAAjkQYqoYmsW4VHfXK6/NHuysAAAAAwkQYqoaEWJckqfAIU+UAAAAApyEMVUNCrFsSzxoCAAAAnIgwVA1lI0M/c98QAAAA4DiEoWo4MTJEGAIAAACchjBUDU3ijoehIqbJAQAAAE5DGKqG2Ea2TMNQwZETI0Mbth/Uj3t/jmKvAAAAAISCMFQNpmEovrFdbgGFmZ9s0uz/2RrFXgEAAAAIhR3tDjhdQpxbPxeVjgwdPupV7s/HdKyE5w4BAAAAdR0jQ9WU0NilguPPGdp7oEhS6XOHygISAAAAgLqJMFRNTeLcKjgefHbnFAa37znpNQAAAIC6hzBUTQmN3cF7hnbnFMoyDUnSnuOjRAAAAADqJsJQNSXEunT4mFden197corU/pwExcbYwSlzUum0ub99vIGpcwAAAEAdElIY2r59u37729+qX79++u1vf6sdO3ZU2m7x4sUaMGCAUlNTNWDAAB04cECS5PP5NGnSJPXt21fXX3+9Zs+eXWMfINoSyp41dLhEu3MK1cYTr9aeuHIjQ2s279dXG/fp6837o9VNAAAAAKcIaTW5jIwMDRo0SGlpaZo/f74mTJigt99+u1yb9evX6y9/+YveeusteTweFRQUyO0uDQoLFizQrl279Nlnnyk/P1/p6em66qqr1LZt25r/RGdZQmOXJOmn/YUqOupVW0+cTEP61/f7FQgEZBiGvt+ZJ0natCtf13Zz/mcGAAAA6oMqR4Zyc3O1ceNGpaamSpJSU1O1ceNGHTx4sFy7mTNn6p577pHH45EkJSQkKCYmRlLpiNHAgQNlmqaaNWumvn37asmSJTX9WaIiIbY0DJUFnraeeLVuEaeio14dKiqWPxDQpuP7Nu/KUyAQCB77+Zqf9P7/3XL2Ow0AAACg6jCUlZWlVq1aybIsSZJlWWrZsqWysrLKtdu2bZt++ukn3Xnnnbr55pv1yiuvBP/in5WVpdatWwfbJicnKzs7uyY/R9Q0OT5NbtOu0sDTxhOnNi3iJJUuorA3p0gFh0t0UbtEFRwuCU6f8/n9WvTlDn3+/9u79/CoynvR49+5XzIzyUwySSYkIdwJKGKDou3uVoENtIKye9Fqy+7ZFfv09KrnafeR+rTaY30q9bDQ0OQAABo6SURBVNnbdler1vr0nL1p9dTqsRar2yoUiwJyiRBiCCEh98wkmWQmM5n7zHv+GBgMYBLwEmB+n38ga61Z77ve9Zt55zfvWu/a20XwlHuJ4sn0R3gEQgghhBBC5KcP7KGr6XSa5uZmfvOb35BIJNiwYQMVFRWsW7fuA9l/cbHtA9nP++F2209bZrJmR786fSGcdhMzpxfjdGWToZFoimA0BcA/r13Ixl++Qbc/yuULPNQ39zNyfBa65p4Rrv/EDAD6hyLc8W/b+do/LmLFldUfxWGJD8GZYkWIM5FYEWdD4kVMlsSKmKx8j5UJkyGPx4PP5yOdTqPT6Uin0/T39+PxeMZsV1FRwerVqzEajRiNRpYvX87BgwdZt24dHo+H3t5eFi1aBJw+UjQZfn+YTEZNvOGHxO22MzAQOm15Rim0Gg0ZpagotjIwEEIphc1ioLl9iGA4TqnTQpnDRLHDzL53vFw1380ru9oxG3UU2kxs3dPJlXNLAHhm21HiiTR/eO0Ii2qK0GiyU3Vv29/NG4e8/M9bP4ZBf3JAL5lKo9dpc9uJqfdesSLEqSRWxNmQeBGTJbEiJutiihWtVnNOgycTXiZXXFxMbW0tW7ZsAWDLli3U1tbicrnGbLdmzRp27NiBUopkMsmuXbuYP38+AKtXr+aZZ54hk8kwNDTEq6++yqpVq866sucjrUaD7fh9Q9Pc2ROg0WioKCmgqz9Mc1eABdOdAMyfXsThzmESyTT7mgf42Fw3Vy8oo6UrwHAoTjyR5vW3e7FbDfQMjnK4MwBAJJbk2e1ttPWO8PqB3lzZI5EE//LYTv7v1qNj6hSOJnnzUB8ZNTZ5VGrqkkkhhBBCCCHON5OaWvvee+9l8+bNrFq1is2bN/OjH/0IgNtvv52GhgYArr/+eoqLi/n0pz/NunXrmD17Np/73OcAuPHGG6msrGTlypXcdNNNfOMb36CqqupDOqSPnj2XDBXklk0rKeBY3wixRJr5J5KhaiejsRQvv9VJNJ7iytoyrqgtRQF7D/ez8x0vkXiKr65diM1i4LV93QC8sqeLSDxFmcvKlp3tJI7fU/SHv7YSDCf4y54uOrzZrF4pxeMvNPLrLU28urc7V59EMs0Dv93P4y80jkmSgqMJnt3eetozkILhOP2B6GnHemqCJYQQQgghxIVqUvcMzZo164zPBnriiSdy/9dqtWzcuJGNGzeetp1Op8slUBcjh9VID6NUuk8Ozb07MTqRDNUe//fFnR3YLAYW1DjR67RUum281eQjlkhTXWZjQY2TT17m4eXdnXR4Q7yyp4sl89ws+1glP32qnr++3cvMCgc7DvZx7eIK9h8ZYPNfmtn4pTq21/fQeGyIYoeJZ7e3culMF57iAjb/5Qgt3UEgSHWpjU9dNZ14Ms2//+Egx/pGONwxzPduuRyjQUd/IMpPNu8jlkjzvS9czswKBwCHjvl57PlGViyp5Ma/m4FGo0Epxat7uzncOcx/+9R87NbshBLJVJoXd3Ywp6qIhTUnRxGHQ3Eajw1x1cIy9LqTuXjP4CgWow6Xw5xbppTCPxKj2GEecxlgJqPIKDXm9UIIIYQQQpwt3b333nvvVFdiMqLRBFM5KFFQYCISSZxxXUObn97BUb6wYk7uC3oimeaNBi+Vbhurl2YnQrCY9Ow85CUUSfLxS8q5fG52GvLRWJI3jy//7DWzmF5up8xp4dW93ew7MkA0luK//+OlzKxwcKQrwP7mfo50BdBqNXz7c4sospnZur+HjIIX3jhG7XQn3/n8Zbz+di/NXUGUUvzpzXbWfLwGR4GRrft6mFtVxLPbW2lqH2bFkkr2Nw/QH4gys8LBT39XTyKZpsBs4G8He1k0q5i2vhEefq4BrVZL47EhkukMtdOd/H7rUf644xjeoQj1LYMsnlVMMq146JkD7Gr0savRS4FZzwyPg8OdAf716Xp2vePjcOcwl84sxqjX8eLODh57vpG/HuilxGGmstTGyGiCJ/70Dv/5X0fo9Ueone7EaNDR0h3god8f4OXdnZS5rJS7rGQyitf2dfOLZxsYCEaZM60Qg17HyGiCp7e28MpbnbjsJtxFFgBae4I89VoLPQOj1JTbMei1pDMZdhzs49W9XTgKjLmkLDia4KVdHXT6QlS5bbnz29ob5K/1PVhNBops2Uk0UukMu5t8dPrCuGxGdMe3DYbj7Gz0YdBpc7MPnthHW88I7iIzOm1222QqQ0ObH6VULrGEbBLZ2hvEaTej02YTQ6UUXf1h4qkMBWZDbttkKk2HN4zdakCrPZlEBsJxIrEUFtPJ30CUUvQHopiNujHbxpNpIrEUJqNuTKyHo0m0Ws2YbTNKEY2nx9zLduJYIHtP3bul0pkxrz9RDwV5d+/beJ8rQpxK4kVMlsSKmKyLKVY0Gg3Wd313mvTr1AVyI8n5OoECwBsNfRzuGOa2NQtyy0KRBN/59x2svKKKLyyfk1v+v19q4vUDffzLLZfnRox8wxE2Pr4Lm8XAv37j4xj02S+gv3j2IPUtg1y9sIzb1y4EoKU7wE827wfgazcu5MraMjJK8ZP/3Edr7whWk577NizFaTfxVpOPx/7YCMDCGid33rSYeDLN//o/exkMRElnFDcvm82qK6t5aXcHz2xrxWLSo5Tie7dcjs1i4Ceb95HOKCKxFFWlNv7HzYt57vU2/lrfg6fYSp8/woolldTNdfOLZxsw6LXodRpCkST/tHoe+5oHqG8ZZEGNk6aOYcpdVq65rILn/taGxain3GWluSvAkvmlBMJxjnYHqZvr5kh3gGg8xRXzy3iryYfNYuCSGS7ePOTF5TBjNuroGRzlytpSfMNROrwhqkttdA2EsVuNXL2wjNcP9JFIprFbDQTCCZbMc6PRaNhzuB+rSU8knsJRYOTaxRXsburHNxRBr9OSSme4fE4JpU4L2/b3kExlUIDDamB5XSVHugI0tg/nzumiWcXMqSxkW30PQyNxIHvp5PKPVTI4EmNXo5dUOhu7C2e4uGxWMTsbfRzrG8lte83iaSileP1AL6HjswzWTndyRW0ph9qGeLtlkMzxiTn+bpGHogIjf2voo2cgO1X7/OoirlpYToc3xO53fETiKWwWA1ctLMuNPDa1D6OAuZWFXLWwnKFQnN3veBkIxLBZDFwxv5TZ0wppaPNT3zJIPJlm9rRCli4oI51R7Dnso7VnhAKznrp5bhbUuGjpDrL/yADDoTizKhzUzSulwKynvmWQxvYhjHoti2eXcOmsYnoHR6lvGaS7P0yNx87iOW5KiywcavNzsM1PJqO4dFYxi2YWExxNcLDVT0t3kEp3AYtmFVNVaudIV4CGNj/haJIFNU4unVlMOqM41ObncGcAl8PEpTOLmVnhoMMborF9iIFAjLmVhSyc4cJs1PNO+xCHO4exGPUsqHExp7IQ33CUw53D9AyEqS6zs6DGhctuorkrQHNnAKUUc6uLmFflJBRJ0NwVoN0bwuOyMq+6CE+xlbbeEY50BYjEUsyuLGRuVRHptKKlO0Bb7whFdhNzq4qoKrXRMzBKS3eAkWiSqpIC5lQWYTToaO0J0tobxGTUMWdaITUeB4PBGEe7g/iGI1S6C5hVUUihzcixvhCtvUFQMLPCwQyPg3A0SVvvCD0DYdxOC7MqCnEXWejqD9PWGySaSFNTbmemx0EqozjWN0KnL0RhgYkZHjuekgK8/gjt3hGC4QRVpTZmeBzo9Vo6vCE6vCHMRh015XamldoYGonR4Q0xGIxR7rJSU27HZjXQ1R+m0xdGKcX0cjvVpXbC0SSdvhDeoQjFhWaml9lx2U30+iN0+kLEk2kq3Taqy2yk0tlEv2dwFIfVQHWZndIiC/2BKF2+MP6R2Gn3QE6USE+UZ5/6+tM214z752kFnLr+gyjfbDIQiyfPvP6UJaeWN97xaCY4uImP5WzLfn9tfbb1PX39+PU97eUTvP6jLv+09j7D9lariUgkPv6Oc9tP8N6ZcAfva/WEDTCZn8je7+9oH34bvL8KThgj72MHNpuJ0fAEsTJOATqthitry7BZDO+90UfkXCdQkGRoks5lto36IwPMmlY4ZjSgwxtiR0MftyyfM+bX8d/8uYkZHgfXXj4tt6y1N8ivtzRx5+cXUeq05pY/8adGUmnF125cmHsDd3hDPPT7t/niynlcMb80t+2TW96hpTvI9/+pDsfxbLm7P8xPfruPjy/0cOs/zMld7vYf/9XMzkYvd37+MuZVZxO1nsFRNv12P2UuC3d+fjFWczZZeurVFl7d183nr53F6qXVaDQaegbC/NvvDwDwrc9eSk25g4xSPLu9lZd2dbJknpt//nQtFpOe7v4wD/+/BgKhOLf+w1w+uchDRin+uKOdF99sp6rMxoY1C6h02+j0hXhiyzv0DoyyrK6Sz14zE71Oy5Y327OXHFoN3LJ8DlfML6XDF+I/Xm6m3Rvikhkublkxh2KHmZff6uTPOzsAWL20mtVLq+kdjPD01haOdgepKCngs38/k9oaJ3/Z283LuzuIxdMsXVjGDZ+YQSiS4Pm/HaOpYxiH1cCqpdVctaCcHQ19/GVPF+Fo9llSn76qmlK3nadePkxDmx+jXssnFnn45CIPjceGeHVvN8HRBGVOC8vrKikvtrJ1Xw8Hjg6CBi6bVcLfL66gZyCcS65OJECzKgrZ1eil/nhiNMPj4JOLPISjSf52sJeBQAyDXkvdPDcLa1wcaPXzdssAqbSipNDMxy8pR6fTsvOQF+9QBI0mm3BdNruE1p4gb7cMkkhlKDDrWTK/FKfdxN7D/XQfT7iqS21cPteNbzg7ChhPZEeDLpnhYprbRkOrnw5f9j1S7DCxeI6bSCzFgaODROIpNBqYPa2QWRWFNHcFcslggVnPpTOL0Wo1HGzNJjqQve9ublURXf1hWnuDKAV6nZb51UU4Cow0HhvKPaPLaTcxv9qJPxjlaM8IGaXQaGCGx0Gp00JzZ3aSEsiO0M6vLiIaT3G0J5hLVMtdVipLbbT3jTAYjAHZ8mZPc6DRaDjaEzw+2gWFNiMzyh30+kfpH87eW6cBqspsFJgNtPWO5J4XZjHpmOFxMByK0+eP5N6bpU4L5cUFtHQNE42nj5enYXq5nVg8nXsmGWSfZ1bustIzEGY0lp2uX6OBaSW24+/TcG7k3GzUMa2kAN9wNNeWACWFZiwmPT0Do7l7//S67GQvgXBizH2Ddmt2xLN3cJT08c9dDVBebCWWSOfaEsBk1OEuNOMbjuba50QMAPhHTm6r02oodVrwj8RIJE9ua7MYMBt1uXY/UZ67yMJIJEEscfLZayaDjlKnZcyIY3Zc8V3G//MMVxmocf6azP7G75tOXX36/idXvk6nJZ3OnHa8E/XiZ3u84+3v1GM927aa6FgnPpb32dYTtN3Ex/MBx9pZnruzLV+Ij8KJH+enmiRDH7LzaepBpdQZf8U4Mc33qdtmlMpdhnVCMpXOjUC9e9tEMnPapVHReAqTQXfapU2hSGLMpVwAsUT2i5rZOPZ2tOFQnCKbcUy948k08UR6TLII4A/GKLQZx9wTlEpnCITilBy/1O2EoZEYVrN+THmZjGI4FMflMI0p78QXw3f/enHiMjF3oWXM8UViSeLJDE67aUx5vuEIRTYTJsPJNoolUgTDCcpc2YT1RKz0B6JYTfox5SVTGbxDEaa5C8acK38whlarGVNeOpOhu3+UipKCMZegBcJxYok05a6TCXJGKbp8YdxFFqzmk20RjiYZGolRVWrLtYVSip6BUWzWk5f4QfY89/pHmV5mH9P2ff7R419iT5aXSKbp7A9T6S4Y0/YDgSixRJpKd0GuvFQ6Q7s3RKnTkkvIIRsTQ6EYNeX2XHxmMooOXwi7xTDmXIciCfr8EaaX23Ntn1GK7v4wOm32C/2J8qLxFJ2+ENPctlzbK6Xo80eIJ7P35Z0oL55M0+ENUVJoHnO/2kAgSiAcP34Zpe74uUvT7g1htxopc1py5Q2H4viGIlSX2XNtn0pn6OoPo9VoqCq15WJrZDRBz0CYipICCm0m3G47Pt8I3QNhEqkM08tsufJGY0k6fWFKCs2UFGbvm8sohW8owshoguoye+6SxxPHbLMY8BQXoNVmf+AYCMYYDESpdNty77N4Mk2XL4xOp6HSbcOg16JU9j3TNxSh3GnNvXeSqQzdA2GSqQzVZbbcuQ6G43QPjuKymyhzWdFqNKTSGfr8EcKRBFVl9lzbh6NJunwhCiwGKkoK0Ou0ZDIK71CE4VCcipKC3GdDJJaie+B43UpsmIw6MkoxGIhm36dFFtxFltM+5/LF+dQPifPbZGNlwuRywh1MtPrsktdzMfE+3l8dzvc2eL/HX1xsY9AfPuf9azVgNU/9qBBIMvShk05ITJbEipgsiRVxNiRexGRJrIjJuphi5UN7zpAQQgghhBBCXIwkGRJCCCGEEELkJUmGhBBCCCGEEHlpUg9dPR+cevN+vtZBXBgkVsRkSayIsyHxIiZLYkVM1sUSK+d6HBfMBApCCCGEEEII8UGSy+SEEEIIIYQQeUmSISGEEEIIIURekmRICCGEEEIIkZckGRJCCCGEEELkJUmGhBBCCCGEEHlJkiEhhBBCCCFEXpJkSAghhBBCCJGXJBkSQgghhBBC5CVJhoQQQgghhBB5SZKhCRw7doybb76ZVatWcfPNN9Pe3j7VVRJTZHh4mNtvv51Vq1axdu1avvnNbzI0NATA22+/zQ033MCqVav4yle+gt/vz71uvHXi4vfwww8zb948jhw5AkisiDOLx+Pcc889rFy5krVr1/KDH/wAGL8Pkv4pP23bto1169Zx4403snbtWl555RVAYkXApk2bWLZs2Zg+B849NvImbpQY1/r169Xzzz+vlFLq+eefV+vXr5/iGompMjw8rHbt2pX7+4EHHlAbN25UmUxGrVixQu3Zs0cppdQjjzyi7rrrLqWUGneduPgdOnRI3Xbbberaa69Vzc3NEiviPd13333q/vvvV5lMRiml1MDAgFJq/D5I+qf8k8lk1JIlS1Rzc7NSSqmmpia1ePFilU6nJVaE2rNnj+rt7VXXXXddLkaUOvfPkXyJG0mGxjE4OKjq6upUKpVSSimVSqVUXV2d8vv9U1wzcT54+eWX1Ze//GV14MABdf311+eW+/1+tXjxYqWUGneduLjF43F10003qc7OzlzHJLEiziQcDqu6ujoVDofHLB+vD5L+KT9lMhl15ZVXqr179yqllHrrrbfUypUrJVbEGO9Ohs41NvIpbvRTPTJ1Puvr66OsrAydTgeATqejtLSUvr4+XC7XFNdOTKVMJsNTTz3FsmXL6Ovro6KiIrfO5XKRyWQIBALjrisqKpqKqouPyM9//nNuuOEGqqqqcsskVsSZdHV1UVRUxMMPP8zu3bspKCjgO9/5Dmaz+T37IKWU9E95SKPR8LOf/Yyvf/3rWK1WRkdHefzxx8f9viKxkt/ONTbyKW7kniEhzsF9992H1WrlS1/60lRXRZyH6uvraWho4NZbb53qqogLQCqVoquriwULFvDcc8/x3e9+l29961tEIpGprpo4z6RSKR5//HF++ctfsm3bNh599FHuvPNOiRUh3gcZGRqHx+PB5/ORTqfR6XSk02n6+/vxeDxTXTUxhTZt2kRHRwePPfYYWq0Wj8dDb29vbv3Q0BAajYaioqJx14mL1549e2hra2P58uUAeL1ebrvtNtavXy+xIk5TUVGBXq9nzZo1AFx22WU4nU7MZvN79kFKKemf8lBTUxP9/f3U1dUBUFdXh8ViwWQySayIMxrvu+x4sZFPcSMjQ+MoLi6mtraWLVu2ALBlyxZqa2svuuFBMXkPPfQQhw4d4pFHHsFoNAJwySWXEIvF2Lt3LwBPP/00n/rUpyZcJy5eX/3qV9mxYwdbt25l69atlJeX8+STT7JhwwaJFXEal8vF0qVLeeONN4DsDE5+v5+ampr37IOkf8pP5eXleL1e2traAGhtbWVwcJDp06dLrIgzGu/8n+u6i41GKaWmuhLns9bWVu666y5GRkZwOBxs2rSJmTNnTnW1xBRoaWlhzZo11NTUYDabAaisrOSRRx5h//793HPPPcTjcaZNm8aDDz5ISUkJwLjrRH5YtmwZjz32GHPnzpVYEWfU1dXF97//fQKBAHq9njvuuINrrrlm3D5I+qf89MILL/DEE0+g0WgA+Pa3v82KFSskVgQ//vGPeeWVVxgcHMTpdFJUVMSLL754zrGRL3EjyZAQQgghhBAiL8llckIIIYQQQoi8JMmQEEIIIYQQIi9JMiSEEEIIIYTIS5IMCSGEEEIIIfKSJENCCCGEEEKIvCTJkBBCCCGEECIvSTIkhBBCCCGEyEuSDAkhhBBCCCHykiRDQgghzlvLli3jySefZO3atdTV1XHHHXcQj8f51a9+xU033UQqlQLgd7/7Hddffz3xeHyKayyEEOJCIsmQEEKI89pLL73Er3/9a1577TWam5t57rnn2LBhAwaDgUcffZT29nYeeughHnzwQUwm01RXVwghxAVEP9UVEEIIIcazfv16ysrKALjuuutoampCq9WyadMmPvOZz/DnP/+ZDRs2sGDBgimuqRBCiAuNjAwJIYQ4r7nd7tz/LRYLkUgEgMrKSpYuXUpPTw9f/OIXp6p6QgghLmCSDAkhhLggbd++nfr6eq6++mp++tOfTnV1hBBCXIAkGRJCCHHBGRoa4u677+b+++/ngQceYOvWrWzfvn2qqyWEEOICI8mQEEKIC84Pf/hDli1bxjXXXIPT6eT+++/n7rvvZnh4eKqrJoQQ4gKiUUqpqa6EEEIIIYQQQnzUZGRICCGEEEIIkZckGRJCCCGEEELkJUmGhBBCCCGEEHlJkiEhhBBCCCFEXpJkSAghhBBCCJGXJBkSQgghhBBC5CVJhoQQQgghhBB5SZIhIYQQQgghRF6SZEgIIYQQQgiRl/4/PkImeef1mHAAAAAASUVORK5CYII=\n",
- "text/plain": [
- "<Figure size 1008x432 with 2 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "fig, (ax1, ax2) = plt.subplots(nrows=2, sharex=True)\n",
- "df_ldst.set_index(\"nx\")[\"Loads / Loop Iteration\"].plot(ax=ax1, legend=True);\n",
- "df_ldst.set_index(\"nx\")[\"Stores / Loop Iteration\"].plot(ax=ax2, legend=True);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Did you expect more?\n",
- "\n",
- "The reason is simple: Among the load and store instructions counted by `PM_LD_CMPL` and `PM_ST_CMPL` are vector instructions which can load and store multiple (two) values at a time. To see how many *bytes* are loaded and stored, we need to measure counters for vectorized loads and stores as well.\n",
- "\n",
- "<a name=\"task2-b\"></a>**TASK B**: Please measure counters for _vectorized_ loads and _vectorized_ stores. See the TODOs in [`poisson2d.vld.c`](/edit/Tasks/poisson2d.vld.c) and [`poisson2d.vst.c`](/edit/Tasks/poisson2d.vst.c) (*Note: These vector counters can not be measured together and need separate files and runs*). Can you find out the name of the counters yourself, using `papi_native_avail | grep VECTOR_`?\n",
- "\n",
- "Compile, test, and bench-run your program again.\n",
- "\n",
- "[Back to top](#toc)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "| PM_VECTOR_FLOP_CMPL |\r\n",
- "| PM_VECTOR_LD_CMPL |\r\n",
- "| PM_VECTOR_ST_CMPL |\r\n"
- ]
- }
- ],
- "source": [
- "!papi_native_avail | grep VECTOR_"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "`make bench_task3` will submit benchmark runs of both vectorized counters to the batch system (as two subsequent runs of the individual files)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "bsub -W 60 -nnodes 1 -Is jsrun -n 1 -c 1 -g ALL_GPUS ./bench.sh poisson2d.vld.bin /gpfs/wolf/gen110/scratch/aherten//poisson2d.vld.bin.csv\n",
- "Job <4097> is submitted to default queue <batch>.\n",
- "<<Waiting for dispatch ...>>\n",
- "<<Starting on login1>>\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,4,0.0010,0,0,0\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,8,0.0011,114000,570,570\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,12,0.0012,174000,870,870\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,16,0.0013,234000,1170,1170\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,20,0.0014,294000,1470,1470\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,24,0.0014,354000,1770,1770\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,28,0.0014,414000,2070,2070\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,32,0.0015,474000,2370,2370\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,36,0.0016,534000,2670,2670\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,40,0.0016,594000,2970,2970\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,44,0.0017,654000,3270,3270\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,48,0.0017,714000,3570,3570\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,52,0.0018,774000,3870,3870\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,56,0.0020,834000,4170,4170\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,60,0.0020,894000,4470,4470\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,64,0.0021,954000,4770,4770\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,68,0.0022,1014000,5070,5070\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,72,0.0022,1074000,5370,5370\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,76,0.0023,1134000,5670,5670\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,80,0.0023,1194000,5970,5970\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,84,0.0023,1254000,6270,6270\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,88,0.0024,1314000,6570,6570\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,92,0.0025,1374000,6870,6870\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,96,0.0025,1434000,7170,7170\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,100,0.0026,1494000,7470,7470\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,104,0.0027,1554000,7770,7770\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,108,0.0027,1614000,8070,8070\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,112,0.0028,1674000,8370,8370\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,116,0.0028,1734000,8670,8670\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,120,0.0029,1794000,8970,8970\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,124,0.0030,1854000,9270,9270\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,128,0.0030,1914000,9570,9570\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,132,0.0031,1974000,9870,9870\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,136,0.0032,2034000,10170,10170\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,140,0.0032,2094000,10470,10470\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,144,0.0033,2154000,10770,10770\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,148,0.0034,2214000,11070,11070\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,152,0.0035,2274000,11370,11370\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,156,0.0035,2334000,11670,11670\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,160,0.0036,2394000,11970,11970\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,164,0.0036,2454000,12270,12270\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,168,0.0037,2514000,12570,12570\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,172,0.0037,2574000,12870,12870\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,176,0.0038,2634000,13170,13170\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,180,0.0039,2694000,13470,13470\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,184,0.0041,2754000,13770,13770\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,188,0.0040,2814000,14070,14070\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,192,0.0041,2874000,14370,14370\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,196,0.0041,2934000,14670,14670\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,200,0.0042,2994000,14970,14970\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,204,0.0043,3054000,15270,15270\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,208,0.0044,3114000,15570,15570\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,212,0.0044,3174000,15870,15870\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,216,0.0044,3234000,16170,16170\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,220,0.0045,3294000,16470,16470\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,224,0.0046,3354000,16770,16770\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,228,0.0047,3414000,17070,17070\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,232,0.0047,3474000,17370,17370\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,236,0.0048,3534000,17670,17670\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,240,0.0048,3594000,17970,17970\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,244,0.0049,3654000,18270,18270\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,248,0.0049,3714000,18570,18570\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,252,0.0050,3774000,18870,18870\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,256,0.0051,3834000,19170,19170\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,260,0.0052,3894000,19470,19470\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,264,0.0052,3954000,19770,19770\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,268,0.0053,4014000,20070,20070\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,272,0.0053,4074000,20370,20370\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,276,0.0055,4134000,20670,20670\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,280,0.0055,4194000,20970,20970\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,284,0.0055,4254000,21270,21270\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,288,0.0057,4314000,21570,21570\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,292,0.0056,4374000,21870,21870\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,296,0.0057,4434000,22170,22170\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,300,0.0059,4494000,22470,22470\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,304,0.0059,4554000,22770,22770\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,308,0.0060,4614000,23070,23070\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,312,0.0060,4674000,23370,23370\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,316,0.0061,4734000,23670,23670\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,320,0.0061,4794000,23970,23970\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,324,0.0062,4854000,24270,24270\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,328,0.0062,4914000,24570,24570\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,332,0.0063,4974000,24870,24870\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,336,0.0063,5034000,25170,25170\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,340,0.0066,5094000,25470,25470\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,344,0.0065,5154000,25770,25770\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,348,0.0067,5214000,26070,26070\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,352,0.0068,5274000,26370,26370\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,356,0.0067,5334000,26670,26670\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,360,0.0067,5394000,26970,26970\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,364,0.0068,5454000,27270,27270\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,368,0.0069,5514000,27570,27570\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,372,0.0069,5574000,27870,27870\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,376,0.0070,5634000,28170,28170\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,380,0.0071,5694000,28470,28470\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,384,0.0071,5754000,28770,28770\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,388,0.0073,5814000,29070,29070\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,392,0.0074,5874000,29370,29370\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,396,0.0073,5934000,29670,29670\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,400,0.0074,5994000,29970,29970\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,404,0.0074,6054000,30270,30270\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,408,0.0075,6114000,30570,30570\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,412,0.0076,6174000,30870,30870\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,416,0.0076,6234000,31170,31170\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,420,0.0080,6294000,31470,31470\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,424,0.0079,6354000,31770,31770\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,428,0.0078,6414000,32070,32070\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,432,0.0079,6474000,32370,32370\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,436,0.0080,6534000,32670,32670\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,440,0.0080,6594000,32970,32970\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,444,0.0083,6654000,33270,33270\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,448,0.0082,6714000,33570,33570\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,452,0.0082,6774000,33870,33870\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,456,0.0083,6834000,34170,34170\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,460,0.0086,6894000,34470,34470\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,464,0.0084,6954000,34770,34770\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,468,0.0085,7014000,35070,35070\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,472,0.0086,7074000,35370,35370\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,476,0.0086,7134000,35670,35670\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,480,0.0087,7194000,35970,35970\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,484,0.0088,7254000,36270,36270\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,488,0.0088,7314000,36570,36570\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,492,0.0089,7374000,36870,36870\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,496,0.0091,7434000,37170,37170\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,500,0.0092,7494000,37470,37470\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,504,0.0091,7554000,37770,37770\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,508,0.0092,7614000,38070,38070\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,512,0.0092,7674000,38370,38370\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,516,0.0093,7734000,38670,38670\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,520,0.0093,7794000,38970,38970\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,524,0.0094,7854000,39270,39270\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,528,0.0097,7914000,39570,39570\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,532,0.0095,7974000,39870,39870\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,536,0.0096,8034000,40170,40170\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,540,0.0097,8094000,40470,40470\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,544,0.0097,8154000,40770,40770\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,548,0.0099,8214000,41070,41070\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,552,0.0099,8274000,41370,41370\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,556,0.0100,8334000,41670,41670\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,560,0.0100,8394000,41970,41970\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,564,0.0101,8454000,42270,42270\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,568,0.0102,8514000,42570,42570\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,572,0.0103,8574000,42870,42870\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,576,0.0103,8634000,43170,43170\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,580,0.0104,8694000,43470,43470\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,584,0.0104,8754000,43770,43770\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,588,0.0106,8814000,44070,44070\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,592,0.0106,8874000,44370,44370\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,596,0.0107,8934000,44670,44670\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,600,0.0107,8994000,44970,44970\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,604,0.0109,9054000,45270,45270\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,608,0.0109,9114000,45570,45570\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,612,0.0110,9174000,45870,45870\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,616,0.0110,9234000,46170,46170\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,620,0.0111,9294000,46470,46470\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,624,0.0112,9354000,46770,46770\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,628,0.0112,9414000,47070,47070\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,632,0.0113,9474000,47370,47370\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,636,0.0114,9534000,47670,47670\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,640,0.0115,9594000,47970,47970\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,644,0.0115,9654000,48270,48270\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,648,0.0115,9714000,48570,48570\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,652,0.0116,9774000,48870,48870\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,656,0.0118,9834000,49170,49170\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,660,0.0117,9894000,49470,49470\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,664,0.0118,9954000,49770,49770\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,668,0.0118,10014000,50070,50070\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,672,0.0120,10074000,50370,50370\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,676,0.0121,10134000,50670,50670\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,680,0.0120,10194000,50970,50970\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,684,0.0121,10254000,51270,51270\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,688,0.0123,10314000,51570,51570\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,692,0.0122,10374000,51870,51870\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,696,0.0123,10434000,52170,52170\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,700,0.0124,10494000,52470,52470\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,704,0.0124,10554000,52770,52770\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,708,0.0125,10614000,53070,53070\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,712,0.0126,10674000,53370,53370\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,716,0.0126,10734000,53670,53670\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,720,0.0126,10794000,53970,53970\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,724,0.0128,10854000,54270,54270\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,728,0.0128,10914000,54570,54570\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,732,0.0129,10974000,54870,54870\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,736,0.0130,11034000,55170,55170\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,740,0.0130,11094000,55470,55470\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,744,0.0130,11154000,55770,55770\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,748,0.0131,11214000,56070,56070\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,752,0.0132,11274000,56370,56370\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,756,0.0133,11334000,56670,56670\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,760,0.0134,11394000,56970,56970\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,764,0.0134,11454000,57270,57270\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,768,0.0135,11514000,57570,57570\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,772,0.0135,11574000,57870,57870\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,776,0.0136,11634000,58170,58170\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,780,0.0138,11694000,58470,58470\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,784,0.0138,11754000,58770,58770\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,788,0.0139,11814000,59070,59070\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,792,0.0139,11874000,59370,59370\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,796,0.0141,11934000,59670,59670\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,800,0.0140,11994000,59970,59970\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,804,0.0141,12054000,60270,60270\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,808,0.0142,12114000,60570,60570\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,812,0.0143,12174000,60870,60870\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,816,0.0143,12234000,61170,61170\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,820,0.0143,12294000,61470,61470\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,824,0.0144,12354000,61770,61770\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,828,0.0145,12414000,62070,62070\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,832,0.0145,12474000,62370,62370\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,836,0.0146,12534000,62670,62670\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,840,0.0146,12594000,62970,62970\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,844,0.0147,12654000,63270,63270\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,848,0.0148,12714000,63570,63570\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,852,0.0149,12774000,63870,63870\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,856,0.0150,12834000,64170,64170\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,860,0.0150,12894000,64470,64470\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,864,0.0151,12954000,64770,64770\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,868,0.0152,13014000,65070,65070\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,872,0.0151,13074000,65370,65370\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,876,0.0152,13134000,65670,65670\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,880,0.0154,13194000,65970,65970\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,884,0.0154,13254000,66270,66270\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,888,0.0154,13314000,66570,66570\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,892,0.0155,13374000,66870,66870\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,896,0.0156,13434000,67170,67170\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,900,0.0158,13494000,67470,67470\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,904,0.0158,13554000,67770,67770\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,908,0.0159,13614000,68070,68070\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,912,0.0161,13674000,68370,68370\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,916,0.0162,13734000,68670,68670\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,920,0.0162,13794000,68970,68970\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,924,0.0163,13854000,69270,69270\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,928,0.0162,13914000,69570,69570\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,932,0.0164,13974000,69870,69870\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,936,0.0163,14034000,70170,70170\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,940,0.0164,14094000,70470,70470\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,944,0.0165,14154000,70770,70770\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,948,0.0166,14214000,71070,71070\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,952,0.0166,14274000,71370,71370\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,956,0.0170,14334000,71670,71670\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,960,0.0168,14394000,71970,71970\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,964,0.0174,14454000,72270,72270\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,968,0.0172,14514000,72570,72570\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,972,0.0173,14574000,72870,72870\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,976,0.0173,14634000,73170,73170\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,980,0.0175,14694000,73470,73470\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,984,0.0175,14754000,73770,73770\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,988,0.0176,14814000,74070,74070\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,992,0.0176,14874000,74370,74370\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,996,0.0178,14934000,74670,74670\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,1000,0.0179,14994000,74970,74970\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,1004,0.0178,15054000,75270,75270\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,1008,0.0179,15114000,75570,75570\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,1012,0.0179,15174000,75870,75870\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,1016,0.0181,15234000,76170,76170\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,1020,0.0181,15294000,76470,76470\n",
- "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n",
- "200,32,1024,0.0179,15354000,76770,76770\n",
- "mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.vld.bin.csv .\n",
- "bsub -W 60 -nnodes 1 -Is jsrun -n 1 -c 1 -g ALL_GPUS ./bench.sh poisson2d.vst.bin /gpfs/wolf/gen110/scratch/aherten//poisson2d.vst.bin.csv\n",
- "Job <4098> is submitted to default queue <batch>.\n",
- "<<Waiting for dispatch ...>>\n",
- "<<Starting on login1>>\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,4,0.0010,200,1,1\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,8,0.0011,18200,91,91\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,12,0.0012,30200,151,151\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,16,0.0012,42200,211,211\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,20,0.0013,54200,271,271\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,24,0.0014,66200,331,331\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,28,0.0014,78200,391,391\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,32,0.0016,90200,451,451\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,36,0.0015,102200,511,511\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,40,0.0016,114200,571,571\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,44,0.0017,126200,631,631\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,48,0.0017,138200,691,691\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,52,0.0018,150200,751,751\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,56,0.0019,162200,811,811\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,60,0.0020,174200,871,871\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,64,0.0022,186200,931,931\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,68,0.0022,198200,991,991\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,72,0.0021,210200,1051,1051\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,76,0.0023,222200,1111,1111\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,80,0.0023,234200,1171,1171\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,84,0.0023,246200,1231,1231\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,88,0.0024,258200,1291,1291\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,92,0.0025,270200,1351,1351\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,96,0.0027,282200,1411,1411\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,100,0.0026,294200,1471,1471\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,104,0.0027,306200,1531,1531\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,108,0.0027,318200,1591,1591\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,112,0.0028,330200,1651,1651\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,116,0.0028,342200,1711,1711\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,120,0.0030,354200,1771,1771\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,124,0.0030,366200,1831,1831\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,128,0.0030,378200,1891,1891\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,132,0.0032,390200,1951,1951\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,136,0.0032,402200,2011,2011\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,140,0.0032,414200,2071,2071\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,144,0.0033,426200,2131,2131\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,148,0.0033,438200,2191,2191\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,152,0.0034,450200,2251,2251\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,156,0.0035,462200,2311,2311\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,160,0.0036,474200,2371,2371\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,164,0.0036,486200,2431,2431\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,168,0.0037,498200,2491,2491\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,172,0.0037,510200,2551,2551\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,176,0.0039,522200,2611,2611\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,180,0.0039,534200,2671,2671\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,184,0.0039,546200,2731,2731\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,188,0.0040,558200,2791,2791\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,192,0.0040,570200,2851,2851\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,196,0.0041,582200,2911,2911\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,200,0.0042,594200,2971,2971\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,204,0.0042,606200,3031,3031\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,208,0.0043,618200,3091,3091\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,212,0.0044,630200,3151,3151\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,216,0.0044,642200,3211,3211\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,220,0.0046,654200,3271,3271\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,224,0.0046,666200,3331,3331\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,228,0.0046,678200,3391,3391\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,232,0.0047,690200,3451,3451\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,236,0.0047,702200,3511,3511\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,240,0.0048,714200,3571,3571\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,244,0.0049,726200,3631,3631\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,248,0.0049,738200,3691,3691\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,252,0.0050,750200,3751,3751\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,256,0.0051,762200,3811,3811\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,260,0.0051,774200,3871,3871\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,264,0.0053,786200,3931,3931\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,268,0.0053,798200,3991,3991\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,272,0.0054,810200,4051,4051\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,276,0.0055,822200,4111,4111\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,280,0.0055,834200,4171,4171\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,284,0.0055,846200,4231,4231\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,288,0.0056,858200,4291,4291\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,292,0.0057,870200,4351,4351\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,296,0.0057,882200,4411,4411\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,300,0.0058,894200,4471,4471\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,304,0.0058,906200,4531,4531\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,308,0.0059,918200,4591,4591\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,312,0.0060,930200,4651,4651\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,316,0.0060,942200,4711,4711\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,320,0.0061,954200,4771,4771\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,324,0.0061,966200,4831,4831\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,328,0.0062,978200,4891,4891\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,332,0.0063,990200,4951,4951\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,336,0.0063,1002200,5011,5011\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,340,0.0064,1014200,5071,5071\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,344,0.0065,1026200,5131,5131\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,348,0.0066,1038200,5191,5191\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,352,0.0066,1050200,5251,5251\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,356,0.0067,1062200,5311,5311\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,360,0.0067,1074200,5371,5371\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,364,0.0068,1086200,5431,5431\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,368,0.0068,1098200,5491,5491\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,372,0.0069,1110200,5551,5551\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,376,0.0070,1122200,5611,5611\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,380,0.0071,1134200,5671,5671\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,384,0.0072,1146200,5731,5731\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,388,0.0072,1158200,5791,5791\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,392,0.0072,1170200,5851,5851\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,396,0.0073,1182200,5911,5911\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,400,0.0074,1194200,5971,5971\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,404,0.0074,1206200,6031,6031\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,408,0.0076,1218200,6091,6091\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,412,0.0076,1230200,6151,6151\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,416,0.0077,1242200,6211,6211\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,420,0.0077,1254200,6271,6271\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,424,0.0078,1266200,6331,6331\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,428,0.0078,1278200,6391,6391\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,432,0.0080,1290200,6451,6451\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,436,0.0079,1302200,6511,6511\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,440,0.0081,1314200,6571,6571\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,444,0.0081,1326200,6631,6631\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,448,0.0082,1338200,6691,6691\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,452,0.0082,1350200,6751,6751\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,456,0.0084,1362200,6811,6811\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,460,0.0084,1374200,6871,6871\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,464,0.0084,1386200,6931,6931\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,468,0.0085,1398200,6991,6991\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,472,0.0085,1410200,7051,7051\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,476,0.0086,1422200,7111,7111\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,480,0.0087,1434200,7171,7171\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,484,0.0088,1446200,7231,7231\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,488,0.0088,1458200,7291,7291\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,492,0.0089,1470200,7351,7351\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,496,0.0089,1482200,7411,7411\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,500,0.0090,1494200,7471,7471\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,504,0.0092,1506200,7531,7531\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,508,0.0093,1518200,7591,7591\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,512,0.0092,1530200,7651,7651\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,516,0.0093,1542200,7711,7711\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,520,0.0094,1554200,7771,7771\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,524,0.0094,1566200,7831,7831\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,528,0.0094,1578200,7891,7891\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,532,0.0097,1590200,7951,7951\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,536,0.0096,1602200,8011,8011\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,540,0.0097,1614200,8071,8071\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,544,0.0097,1626200,8131,8131\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,548,0.0099,1638200,8191,8191\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,552,0.0099,1650200,8251,8251\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,556,0.0101,1662200,8311,8311\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,560,0.0100,1674200,8371,8371\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,564,0.0101,1686200,8431,8431\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,568,0.0102,1698200,8491,8491\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,572,0.0103,1710200,8551,8551\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,576,0.0103,1722200,8611,8611\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,580,0.0104,1734200,8671,8671\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,584,0.0104,1746200,8731,8731\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,588,0.0105,1758200,8791,8791\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,592,0.0107,1770200,8851,8851\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,596,0.0108,1782200,8911,8911\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,600,0.0107,1794200,8971,8971\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,604,0.0109,1806200,9031,9031\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,608,0.0109,1818200,9091,9091\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,612,0.0109,1830200,9151,9151\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,616,0.0110,1842200,9211,9211\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,620,0.0111,1854200,9271,9271\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,624,0.0112,1866200,9331,9331\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,628,0.0111,1878200,9391,9391\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,632,0.0112,1890200,9451,9451\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,636,0.0113,1902200,9511,9511\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,640,0.0116,1914200,9571,9571\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,644,0.0114,1926200,9631,9631\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,648,0.0115,1938200,9691,9691\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,652,0.0117,1950200,9751,9751\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,656,0.0117,1962200,9811,9811\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,660,0.0117,1974200,9871,9871\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,664,0.0118,1986200,9931,9931\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,668,0.0119,1998200,9991,9991\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,672,0.0120,2010200,10051,10051\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,676,0.0120,2022200,10111,10111\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,680,0.0120,2034200,10171,10171\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,684,0.0121,2046200,10231,10231\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,688,0.0122,2058200,10291,10291\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,692,0.0123,2070200,10351,10351\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,696,0.0124,2082200,10411,10411\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,700,0.0124,2094200,10471,10471\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,704,0.0125,2106200,10531,10531\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,708,0.0125,2118200,10591,10591\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,712,0.0125,2130200,10651,10651\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,716,0.0125,2142200,10711,10711\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,720,0.0126,2154200,10771,10771\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,724,0.0127,2166200,10831,10831\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,728,0.0128,2178200,10891,10891\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,732,0.0128,2190200,10951,10951\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,736,0.0130,2202200,11011,11011\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,740,0.0130,2214200,11071,11071\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,744,0.0130,2226200,11131,11131\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,748,0.0131,2238200,11191,11191\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,752,0.0133,2250200,11251,11251\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,756,0.0133,2262200,11311,11311\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,760,0.0133,2274200,11371,11371\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,764,0.0134,2286200,11431,11431\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,768,0.0135,2298200,11491,11491\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,772,0.0137,2310200,11551,11551\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,776,0.0136,2322200,11611,11611\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,780,0.0137,2334200,11671,11671\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,784,0.0137,2346200,11731,11731\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,788,0.0138,2358200,11791,11791\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,792,0.0139,2370200,11851,11851\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,796,0.0140,2382200,11911,11911\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,800,0.0140,2394200,11971,11971\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,804,0.0141,2406200,12031,12031\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,808,0.0143,2418200,12091,12091\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,812,0.0142,2430200,12151,12151\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,816,0.0143,2442200,12211,12211\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,820,0.0144,2454200,12271,12271\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,824,0.0144,2466200,12331,12331\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,828,0.0145,2478200,12391,12391\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,832,0.0146,2490200,12451,12451\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,836,0.0146,2502200,12511,12511\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,840,0.0147,2514200,12571,12571\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,844,0.0148,2526200,12631,12631\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,848,0.0149,2538200,12691,12691\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,852,0.0149,2550200,12751,12751\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,856,0.0150,2562200,12811,12811\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,860,0.0152,2574200,12871,12871\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,864,0.0151,2586200,12931,12931\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,868,0.0151,2598200,12991,12991\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,872,0.0151,2610200,13051,13051\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,876,0.0152,2622200,13111,13111\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,880,0.0155,2634200,13171,13171\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,884,0.0154,2646200,13231,13231\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,888,0.0155,2658200,13291,13291\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,892,0.0155,2670200,13351,13351\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,896,0.0156,2682200,13411,13411\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,900,0.0157,2694200,13471,13471\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,904,0.0159,2706200,13531,13531\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,908,0.0160,2718200,13591,13591\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,912,0.0161,2730200,13651,13651\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,916,0.0162,2742200,13711,13711\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,920,0.0161,2754200,13771,13771\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,924,0.0162,2766200,13831,13831\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,928,0.0163,2778200,13891,13891\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,932,0.0165,2790200,13951,13951\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,936,0.0165,2802200,14011,14011\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,940,0.0165,2814200,14071,14071\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,944,0.0166,2826200,14131,14131\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,948,0.0166,2838200,14191,14191\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,952,0.0168,2850200,14251,14251\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,956,0.0167,2862200,14311,14311\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,960,0.0168,2874200,14371,14371\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,964,0.0173,2886200,14431,14431\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,968,0.0172,2898200,14491,14491\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,972,0.0172,2910200,14551,14551\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,976,0.0173,2922200,14611,14611\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,980,0.0175,2934200,14671,14671\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,984,0.0176,2946200,14731,14731\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,988,0.0176,2958200,14791,14791\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,992,0.0177,2970200,14851,14851\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,996,0.0178,2982200,14911,14911\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,1000,0.0177,2994200,14971,14971\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,1004,0.0179,3006200,15031,15031\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,1008,0.0179,3018200,15091,15091\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,1012,0.0180,3030200,15151,15151\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,1016,0.0180,3042200,15211,15211\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,1020,0.0182,3054200,15271,15271\n",
- "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n",
- "200,32,1024,0.0178,3066200,15331,15331\n",
- "mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.vst.bin.csv .\n"
- ]
- }
- ],
- "source": [
- "!make bench_task3"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Let's plot it again, as soon as the run finishes! Non-interactively, call `graph_task2b`.\n",
- "\n",
- "*We need to read in two CSV files now, which we combine to one common dataframe `df_vldvst`.*"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [],
- "source": [
- "df_vld = pd.read_csv(\"poisson2d.vld.bin.csv\", skiprows=range(2, 50000, 2))\n",
- "df_vst = pd.read_csv(\"poisson2d.vst.bin.csv\", skiprows=range(2, 50000, 2))\n",
- "df_vldvst = pd.concat([df_vld.set_index(\"nx\"), df_vst.set_index(\"nx\")[['PM_VECTOR_ST_CMPL (total)', 'PM_VECTOR_ST_CMPL (min)', ' PM_VECTOR_ST_CMPL (max)']]], axis=1).reset_index()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>nx</th>\n",
- " <th>iter</th>\n",
- " <th>ny</th>\n",
- " <th>Runtime</th>\n",
- " <th>PM_VECTOR_LD_CMPL (total)</th>\n",
- " <th>PM_VECTOR_LD_CMPL (min)</th>\n",
- " <th>PM_VECTOR_LD_CMPL (max)</th>\n",
- " <th>PM_VECTOR_ST_CMPL (total)</th>\n",
- " <th>PM_VECTOR_ST_CMPL (min)</th>\n",
- " <th>PM_VECTOR_ST_CMPL (max)</th>\n",
- " <th>Vector Loads / Loop Iteration</th>\n",
- " <th>Vector Stores / Loop Iteration</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>4</td>\n",
- " <td>200</td>\n",
- " <td>32</td>\n",
- " <td>0.0010</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>200</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>0.000000</td>\n",
- " <td>0.007812</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>8</td>\n",
- " <td>200</td>\n",
- " <td>32</td>\n",
- " <td>0.0011</td>\n",
- " <td>114000</td>\n",
- " <td>570</td>\n",
- " <td>570</td>\n",
- " <td>18200</td>\n",
- " <td>91</td>\n",
- " <td>91</td>\n",
- " <td>2.226562</td>\n",
- " <td>0.355469</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>12</td>\n",
- " <td>200</td>\n",
- " <td>32</td>\n",
- " <td>0.0012</td>\n",
- " <td>174000</td>\n",
- " <td>870</td>\n",
- " <td>870</td>\n",
- " <td>30200</td>\n",
- " <td>151</td>\n",
- " <td>151</td>\n",
- " <td>2.265625</td>\n",
- " <td>0.393229</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>16</td>\n",
- " <td>200</td>\n",
- " <td>32</td>\n",
- " <td>0.0013</td>\n",
- " <td>234000</td>\n",
- " <td>1170</td>\n",
- " <td>1170</td>\n",
- " <td>42200</td>\n",
- " <td>211</td>\n",
- " <td>211</td>\n",
- " <td>2.285156</td>\n",
- " <td>0.412109</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>20</td>\n",
- " <td>200</td>\n",
- " <td>32</td>\n",
- " <td>0.0014</td>\n",
- " <td>294000</td>\n",
- " <td>1470</td>\n",
- " <td>1470</td>\n",
- " <td>54200</td>\n",
- " <td>271</td>\n",
- " <td>271</td>\n",
- " <td>2.296875</td>\n",
- " <td>0.423438</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " nx iter ny Runtime PM_VECTOR_LD_CMPL (total) PM_VECTOR_LD_CMPL (min) \\\n",
- "0 4 200 32 0.0010 0 0 \n",
- "1 8 200 32 0.0011 114000 570 \n",
- "2 12 200 32 0.0012 174000 870 \n",
- "3 16 200 32 0.0013 234000 1170 \n",
- "4 20 200 32 0.0014 294000 1470 \n",
- "\n",
- " PM_VECTOR_LD_CMPL (max) PM_VECTOR_ST_CMPL (total) \\\n",
- "0 0 200 \n",
- "1 570 18200 \n",
- "2 870 30200 \n",
- "3 1170 42200 \n",
- "4 1470 54200 \n",
- "\n",
- " PM_VECTOR_ST_CMPL (min) PM_VECTOR_ST_CMPL (max) \\\n",
- "0 1 1 \n",
- "1 91 91 \n",
- "2 151 151 \n",
- "3 211 211 \n",
- "4 271 271 \n",
- "\n",
- " Vector Loads / Loop Iteration Vector Stores / Loop Iteration \n",
- "0 0.000000 0.007812 \n",
- "1 2.226562 0.355469 \n",
- "2 2.265625 0.393229 \n",
- "3 2.285156 0.412109 \n",
- "4 2.296875 0.423438 "
- ]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "common.normalize(df_vldvst, \"PM_VECTOR_LD_CMPL (min)\", \"Vector Loads / Loop Iteration\")\n",
- "common.normalize(df_vldvst, \"PM_VECTOR_ST_CMPL (min)\", \"Vector Stores / Loop Iteration\")\n",
- "df_vldvst.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 1008x432 with 2 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "fig, (ax1, ax2) = plt.subplots(nrows=2, sharex=True)\n",
- "df_vldvst.set_index(\"nx\")[\"Vector Loads / Loop Iteration\"].plot(ax=ax1, legend=True);\n",
- "df_vldvst.set_index(\"nx\")[\"Vector Stores / Loop Iteration\"].plot(ax=ax2, legend=True);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Let's try to make sense of those numbers.\n",
- "\n",
- "Vector loads and vector stores use two 8 Byte values at a time. When we measured loads and stores with `LD_CMPL` and `ST_CMPL` in part A of this task, we measured total number of stores and loads; that is: vector and scalar versions of the instructions. In order to convert the load and store instructions into **bytes** loaded and stored, we need to separate them. The difference of total instructions and vector instructions yield scalar instructions. We multiply the scalar instructions by 8 Byte (double precision) and the vector instructions by 16 Byte (two loads or stores of double precision). That yields the loaded or stored data (or, more precisely, the instruction-equivalent data).\n",
- "\n",
- "To formualize it, see the following equations, as an example for load ($ld$), with $b$ denoting data loaded in bytes and $n$ denoting the number of instructions.\n",
- "\n",
- "\\begin{align}\n",
- "b_\\text{ld} &= b_\\text{ld}^\\text{scalar} + b_\\text{ld}^\\text{vector}\\\\\n",
- "b_\\text{ld}^\\text{scalar} &= n_\\text{ld}^\\text{scalar} * 8\\,\\text{Byte} \\\\\n",
- "b_\\text{ld}^\\text{vector} &= n_\\text{ld}^\\text{vector} * 16\\,\\text{Byte} \\\\\n",
- "n_\\text{ld}^\\text{scalar} &= n_\\text{ld}^\\text{total} - n_\\text{ld}^\\text{vector}\\\\\n",
- "\\Rightarrow b_\\text{ld} &= n_\\text{ld}^\\text{scalar}* 8 \\,\\text{Byte} + n_\\text{ld}^\\text{vector} * 16\\,\\text{Byte} \\\\\n",
- "& = (n_\\text{ld}^\\text{scalar}+2 n_\\text{ld}^\\text{vector}) * 8\\,Byte \\\\\n",
- "& = (n_\\text{ld}^\\text{total} - n_\\text{ld}^\\text{vector} + 2 n_\\text{ld}^\\text{vector}) * 8\\,Byte \\\\\n",
- "& = (n_\\text{ld}^\\text{total} + n_\\text{ld}^\\text{vector}) *8\\,Byte \n",
- "\\end{align}\n",
- "\n",
- "We are going to print this in the next cell. In case you look at this Notebook non-interactively, call `graph_task2b-2`."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 83,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 1008x432 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "df_byte = pd.DataFrame()\n",
- "df_byte[\"Loads / Loop Iteration\"] = (df_vldvst.set_index(\"nx\")[\"Vector Loads / Loop Iteration\"] + df_ldst.set_index(\"nx\")[\"Loads / Loop Iteration\"])*8\n",
- "df_byte[\"Stores / Loop Iteration\"] = (df_vldvst.set_index(\"nx\")[\"Vector Stores / Loop Iteration\"] + df_ldst.set_index(\"nx\")[\"Stores / Loop Iteration\"])*8\n",
- "ax = df_byte.plot()\n",
- "ax.set_ylabel(\"Bytes / Loop Iteration\");"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Mean byte loaded: 37.52662546714877\tMean byte stored: 8.428951320998907\n"
- ]
- }
- ],
- "source": [
- "import numpy as np\n",
- "mean_byte_ld = np.polyfit(df_byte[df_byte.index > 200].index, df_byte[df_byte.index > 200][\"Loads / Loop Iteration\"], 0)[0]\n",
- "mean_byte_st = np.polyfit(df_byte[df_byte.index > 200].index, df_byte[df_byte.index > 200][\"Stores / Loop Iteration\"], 0)[0]\n",
- "print(\"Mean byte loaded: {}\\tMean byte stored: {}\".format(mean_byte_ld, mean_byte_st))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "*Not really a* <a name=\"task2-c\"></a>**TASK C**: We can combine this information with the cycles measured in Task 1 to create a bandwidth of exchanged bytes per cycle."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [],
- "source": [
- "df_bandwidth = pd.DataFrame()\n",
- "df_bandwidth[\"Bandwidth / Byte/Cycle\"] = (df_byte[\"Loads / Loop Iteration\"] + df_byte[\"Stores / Loop Iteration\"]) / df.set_index(\"nx\")[\"Cycles / Loop Iteration\"]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Let's display it as a function of `nx`. And also compare it to the available L1 cache bandwidth in a second (sub-)plot. Non-interactive users, call `make graph_task2c`."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 1008x432 with 2 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "fig, (ax1, ax2) = plt.subplots(nrows=2, sharex=True)\n",
- "for ax in [ax1, ax2]:\n",
- " df_bandwidth[\"Bandwidth / Byte/Cycle\"].plot(ax=ax, legend=True, label=\"Jacobi Bandwidth\")\n",
- " ax.set_ylabel(\"Byte/Cycle\")\n",
- "ax2.axhline(2*16, color=sns.color_palette()[1], label=\"L1 Bandwidth\");\n",
- "ax2.legend();"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "As you can see, we are quite a bit away from the available L1 cache bandwidth. Can you think of reasons why?"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Task E1: Measuring FlOps\n",
- "<a name=\"taske1\"></a>\n",
- "\n",
- "If you still have time, feel free to work on the following extended task.\n",
- "\n",
- "\n",
- "**TASK**: Please measure counters for _vectorized_ floating point operations and _scalar_ floating point operations. The two counters can also not be measured during the same run. So please see the TODOs in [`poisson2d.sflops.c`](/edit/Tasks/poisson2d.sflops.c) and [`poisson2d.vflops.c`](/edit/Tasks/poisson2d.vflops.c). By now you should be able to find out the names of the counters by yourself (*Hint: they include the words scalar and vector…*).\n",
- "\n",
- "As usual, compile, test, and bench-run your program.\n",
- "\n",
- "[Back to top](#toc)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 42,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "bsub -W 60 -nnodes 1 -Is -P GEN110 jsrun -n 1 -c 1 -g ALL_GPUS ./bench.sh poisson2d.sflop.bin /gpfs/wolf/gen110/scratch/aherten//poisson2d.sflop.bin.csv\n",
- "Job <4299> is submitted to default queue <batch>.\n",
- "<<Waiting for dispatch ...>>\n",
- "<<Starting on login1>>\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,4,0.0010,96000,480,480\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,8,0.0011,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,12,0.0012,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,16,0.0012,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,20,0.0013,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,24,0.0014,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,28,0.0014,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,32,0.0015,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,36,0.0015,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,40,0.0016,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,44,0.0017,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,48,0.0017,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,52,0.0018,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,56,0.0019,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,60,0.0020,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,64,0.0021,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,68,0.0022,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,72,0.0022,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,76,0.0022,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,80,0.0023,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,84,0.0024,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,88,0.0024,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,92,0.0025,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,96,0.0025,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,100,0.0028,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,104,0.0027,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,108,0.0027,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,112,0.0029,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,116,0.0028,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,120,0.0029,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,124,0.0030,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,128,0.0031,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,132,0.0031,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,136,0.0032,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,140,0.0033,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,144,0.0034,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,148,0.0034,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,152,0.0034,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,156,0.0035,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,160,0.0036,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,164,0.0037,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,168,0.0037,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,172,0.0038,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,176,0.0038,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,180,0.0039,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,184,0.0039,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,188,0.0040,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,192,0.0041,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,196,0.0041,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,200,0.0042,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,204,0.0043,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,208,0.0043,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,212,0.0044,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,216,0.0045,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,220,0.0046,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,224,0.0047,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,228,0.0047,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,232,0.0047,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,236,0.0048,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,240,0.0049,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,244,0.0049,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,248,0.0050,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,252,0.0050,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,256,0.0051,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,260,0.0052,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,264,0.0053,0,0,0\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,268,0.0054,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,272,0.0055,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,276,0.0055,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,280,0.0055,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,284,0.0056,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,288,0.0057,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,292,0.0057,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,296,0.0058,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,300,0.0059,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,304,0.0059,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,308,0.0059,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,312,0.0060,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,316,0.0061,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,320,0.0061,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,324,0.0062,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,328,0.0063,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,332,0.0065,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,336,0.0064,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,340,0.0065,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,344,0.0065,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,348,0.0066,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,352,0.0067,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,356,0.0067,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,360,0.0068,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,364,0.0069,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,368,0.0070,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,372,0.0070,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,376,0.0071,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,380,0.0072,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,384,0.0072,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,388,0.0072,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,392,0.0075,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,396,0.0074,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,400,0.0075,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,404,0.0075,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,408,0.0076,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,412,0.0077,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,416,0.0077,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,420,0.0078,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,424,0.0079,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,428,0.0079,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,432,0.0080,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,436,0.0080,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,440,0.0081,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,444,0.0083,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,448,0.0084,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,452,0.0084,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,456,0.0084,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,460,0.0085,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,464,0.0086,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,468,0.0086,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,472,0.0088,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,476,0.0087,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,480,0.0088,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,484,0.0089,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,488,0.0089,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,492,0.0090,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,496,0.0090,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,500,0.0092,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,504,0.0092,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,508,0.0093,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,512,0.0092,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,516,0.0093,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,520,0.0094,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,524,0.0094,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,528,0.0094,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,532,0.0095,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,536,0.0096,0,0,0\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,540,0.0098,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,544,0.0097,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,548,0.0098,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,552,0.0099,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,556,0.0099,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,560,0.0100,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,564,0.0102,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,568,0.0102,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,572,0.0103,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,576,0.0103,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,580,0.0105,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,584,0.0104,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,588,0.0106,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,592,0.0107,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,596,0.0106,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,600,0.0107,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,604,0.0109,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,608,0.0109,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,612,0.0109,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,616,0.0110,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,620,0.0117,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,624,0.0112,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,628,0.0111,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,632,0.0112,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,636,0.0113,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,640,0.0115,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,644,0.0114,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,648,0.0115,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,652,0.0116,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,656,0.0117,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,660,0.0117,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,664,0.0118,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,668,0.0119,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,672,0.0119,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,676,0.0119,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,680,0.0120,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,684,0.0121,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,688,0.0122,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,692,0.0122,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,696,0.0123,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,700,0.0124,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,704,0.0124,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,708,0.0125,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,712,0.0125,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,716,0.0126,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,720,0.0126,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,724,0.0127,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,728,0.0128,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,732,0.0128,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,736,0.0129,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,740,0.0130,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,744,0.0130,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,748,0.0131,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,752,0.0131,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,756,0.0132,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,760,0.0133,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,764,0.0134,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,768,0.0134,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,772,0.0136,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,776,0.0136,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,780,0.0136,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,784,0.0137,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,788,0.0138,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,792,0.0139,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,796,0.0139,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,800,0.0140,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,804,0.0141,0,0,0\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,808,0.0142,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,812,0.0142,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,816,0.0143,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,820,0.0143,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,824,0.0144,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,828,0.0145,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,832,0.0145,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,836,0.0146,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,840,0.0147,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,844,0.0147,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,848,0.0148,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,852,0.0149,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,856,0.0149,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,860,0.0150,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,864,0.0150,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,868,0.0152,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,872,0.0151,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,876,0.0153,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,880,0.0153,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,884,0.0153,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,888,0.0155,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,892,0.0156,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,896,0.0156,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,900,0.0158,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,904,0.0158,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,908,0.0159,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,912,0.0159,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,916,0.0162,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,920,0.0162,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,924,0.0162,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,928,0.0162,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,932,0.0163,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,936,0.0164,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,940,0.0165,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,944,0.0165,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,948,0.0166,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,952,0.0167,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,956,0.0168,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,960,0.0168,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,964,0.0172,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,968,0.0173,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,972,0.0173,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,976,0.0173,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,980,0.0175,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,984,0.0176,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,988,0.0175,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,992,0.0176,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,996,0.0178,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,1000,0.0177,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,1004,0.0178,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,1008,0.0178,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,1012,0.0181,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,1016,0.0180,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,1020,0.0182,0,0,0\n",
- "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n",
- "200,32,1024,0.0179,0,0,0\n",
- "mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.sflop.bin.csv .\n",
- "bsub -W 60 -nnodes 1 -Is -P GEN110 jsrun -n 1 -c 1 -g ALL_GPUS ./bench.sh poisson2d.vflop.bin /gpfs/wolf/gen110/scratch/aherten//poisson2d.vflop.bin.csv\n",
- "Job <4300> is submitted to default queue <batch>.\n",
- "<<Waiting for dispatch ...>>\n",
- "<<Starting on login1>>\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,4,0.0010,0,0,0\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,8,0.0011,150000,750,750\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,12,0.0012,246000,1230,1230\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,16,0.0012,342000,1710,1710\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,20,0.0013,438000,2190,2190\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,24,0.0014,534000,2670,2670\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,28,0.0014,630000,3150,3150\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,32,0.0015,726000,3630,3630\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,36,0.0016,822000,4110,4110\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,40,0.0016,918000,4590,4590\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,44,0.0017,1014000,5070,5070\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,48,0.0018,1110000,5550,5550\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,52,0.0018,1206000,6030,6030\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,56,0.0020,1302000,6510,6510\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,60,0.0020,1398000,6990,6990\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,64,0.0021,1494000,7470,7470\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,68,0.0022,1590000,7950,7950\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,72,0.0022,1686000,8430,8430\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,76,0.0022,1782000,8910,8910\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,80,0.0023,1878000,9390,9390\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,84,0.0024,1974000,9870,9870\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,88,0.0024,2070000,10350,10350\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,92,0.0025,2166000,10830,10830\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,96,0.0025,2262000,11310,11310\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,100,0.0026,2358000,11790,11790\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,104,0.0027,2454000,12270,12270\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,108,0.0028,2550000,12750,12750\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,112,0.0028,2646000,13230,13230\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,116,0.0029,2742000,13710,13710\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,120,0.0032,2838000,14190,14190\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,124,0.0030,2934000,14670,14670\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,128,0.0031,3030000,15150,15150\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,132,0.0031,3126000,15630,15630\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,136,0.0032,3222000,16110,16110\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,140,0.0033,3318000,16590,16590\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,144,0.0033,3414000,17070,17070\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,148,0.0034,3510000,17550,17550\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,152,0.0034,3606000,18030,18030\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,156,0.0036,3702000,18510,18510\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,160,0.0036,3798000,18990,18990\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,164,0.0036,3894000,19470,19470\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,168,0.0037,3990000,19950,19950\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,172,0.0038,4086000,20430,20430\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,176,0.0039,4182000,20910,20910\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,180,0.0039,4278000,21390,21390\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,184,0.0040,4374000,21870,21870\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,188,0.0040,4470000,22350,22350\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,192,0.0041,4566000,22830,22830\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,196,0.0042,4662000,23310,23310\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,200,0.0042,4758000,23790,23790\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,204,0.0043,4854000,24270,24270\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,208,0.0043,4950000,24750,24750\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,212,0.0044,5046000,25230,25230\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,216,0.0045,5142000,25710,25710\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,220,0.0047,5238000,26190,26190\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,224,0.0046,5334000,26670,26670\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,228,0.0047,5430000,27150,27150\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,232,0.0047,5526000,27630,27630\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,236,0.0048,5622000,28110,28110\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,240,0.0049,5718000,28590,28590\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,244,0.0050,5814000,29070,29070\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,248,0.0050,5910000,29550,29550\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,252,0.0051,6006000,30030,30030\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,256,0.0051,6102000,30510,30510\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,260,0.0052,6198000,30990,30990\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,264,0.0052,6294000,31470,31470\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,268,0.0053,6390000,31950,31950\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,272,0.0054,6486000,32430,32430\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,276,0.0058,6582000,32910,32910\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,280,0.0055,6678000,33390,33390\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,284,0.0056,6774000,33870,33870\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,288,0.0056,6870000,34350,34350\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,292,0.0057,6966000,34830,34830\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,296,0.0058,7062000,35310,35310\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,300,0.0059,7158000,35790,35790\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,304,0.0060,7254000,36270,36270\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,308,0.0060,7350000,36750,36750\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,312,0.0061,7446000,37230,37230\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,316,0.0061,7542000,37710,37710\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,320,0.0062,7638000,38190,38190\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,324,0.0063,7734000,38670,38670\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,328,0.0064,7830000,39150,39150\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,332,0.0064,7926000,39630,39630\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,336,0.0064,8022000,40110,40110\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,340,0.0065,8118000,40590,40590\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,344,0.0066,8214000,41070,41070\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,348,0.0066,8310000,41550,41550\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,352,0.0068,8406000,42030,42030\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,356,0.0069,8502000,42510,42510\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,360,0.0068,8598000,42990,42990\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,364,0.0069,8694000,43470,43470\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,368,0.0069,8790000,43950,43950\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,372,0.0070,8886000,44430,44430\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,376,0.0071,8982000,44910,44910\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,380,0.0071,9078000,45390,45390\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,384,0.0072,9174000,45870,45870\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,388,0.0073,9270000,46350,46350\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,392,0.0074,9366000,46830,46830\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,396,0.0074,9462000,47310,47310\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,400,0.0075,9558000,47790,47790\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,404,0.0075,9654000,48270,48270\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,408,0.0076,9750000,48750,48750\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,412,0.0077,9846000,49230,49230\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,416,0.0077,9942000,49710,49710\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,420,0.0078,10038000,50190,50190\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,424,0.0079,10134000,50670,50670\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,428,0.0079,10230000,51150,51150\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,432,0.0080,10326000,51630,51630\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,436,0.0080,10422000,52110,52110\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,440,0.0081,10518000,52590,52590\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,444,0.0082,10614000,53070,53070\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,448,0.0082,10710000,53550,53550\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,452,0.0083,10806000,54030,54030\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,456,0.0084,10902000,54510,54510\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,460,0.0085,10998000,54990,54990\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,464,0.0085,11094000,55470,55470\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,468,0.0086,11190000,55950,55950\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,472,0.0088,11286000,56430,56430\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,476,0.0089,11382000,56910,56910\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,480,0.0088,11478000,57390,57390\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,484,0.0088,11574000,57870,57870\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,488,0.0089,11670000,58350,58350\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,492,0.0090,11766000,58830,58830\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,496,0.0090,11862000,59310,59310\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,500,0.0091,11958000,59790,59790\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,504,0.0092,12054000,60270,60270\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,508,0.0094,12150000,60750,60750\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,512,0.0092,12246000,61230,61230\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,516,0.0093,12342000,61710,61710\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,520,0.0093,12438000,62190,62190\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,524,0.0094,12534000,62670,62670\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,528,0.0094,12630000,63150,63150\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,532,0.0095,12726000,63630,63630\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,536,0.0096,12822000,64110,64110\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,540,0.0100,12918000,64590,64590\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,544,0.0097,13014000,65070,65070\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,548,0.0098,13110000,65550,65550\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,552,0.0099,13206000,66030,66030\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,556,0.0100,13302000,66510,66510\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,560,0.0101,13398000,66990,66990\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,564,0.0102,13494000,67470,67470\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,568,0.0103,13590000,67950,67950\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,572,0.0103,13686000,68430,68430\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,576,0.0103,13782000,68910,68910\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,580,0.0105,13878000,69390,69390\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,584,0.0105,13974000,69870,69870\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,588,0.0106,14070000,70350,70350\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,592,0.0106,14166000,70830,70830\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,596,0.0106,14262000,71310,71310\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,600,0.0108,14358000,71790,71790\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,604,0.0109,14454000,72270,72270\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,608,0.0109,14550000,72750,72750\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,612,0.0109,14646000,73230,73230\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,616,0.0111,14742000,73710,73710\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,620,0.0111,14838000,74190,74190\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,624,0.0112,14934000,74670,74670\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,628,0.0112,15030000,75150,75150\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,632,0.0112,15126000,75630,75630\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,636,0.0114,15222000,76110,76110\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,640,0.0114,15318000,76590,76590\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,644,0.0114,15414000,77070,77070\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,648,0.0115,15510000,77550,77550\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,652,0.0117,15606000,78030,78030\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,656,0.0117,15702000,78510,78510\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,660,0.0117,15798000,78990,78990\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,664,0.0118,15894000,79470,79470\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,668,0.0120,15990000,79950,79950\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,672,0.0120,16086000,80430,80430\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,676,0.0121,16182000,80910,80910\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,680,0.0120,16278000,81390,81390\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,684,0.0121,16374000,81870,81870\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,688,0.0122,16470000,82350,82350\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,692,0.0122,16566000,82830,82830\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,696,0.0124,16662000,83310,83310\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,700,0.0124,16758000,83790,83790\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,704,0.0124,16854000,84270,84270\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,708,0.0125,16950000,84750,84750\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,712,0.0125,17046000,85230,85230\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,716,0.0126,17142000,85710,85710\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,720,0.0126,17238000,86190,86190\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,724,0.0127,17334000,86670,86670\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,728,0.0128,17430000,87150,87150\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,732,0.0130,17526000,87630,87630\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,736,0.0129,17622000,88110,88110\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,740,0.0129,17718000,88590,88590\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,744,0.0130,17814000,89070,89070\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,748,0.0131,17910000,89550,89550\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,752,0.0132,18006000,90030,90030\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,756,0.0132,18102000,90510,90510\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,760,0.0133,18198000,90990,90990\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,764,0.0134,18294000,91470,91470\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,768,0.0135,18390000,91950,91950\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,772,0.0136,18486000,92430,92430\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,776,0.0136,18582000,92910,92910\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,780,0.0137,18678000,93390,93390\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,784,0.0137,18774000,93870,93870\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,788,0.0138,18870000,94350,94350\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,792,0.0138,18966000,94830,94830\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,796,0.0140,19062000,95310,95310\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,800,0.0140,19158000,95790,95790\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,804,0.0140,19254000,96270,96270\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,808,0.0141,19350000,96750,96750\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,812,0.0142,19446000,97230,97230\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,816,0.0143,19542000,97710,97710\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,820,0.0143,19638000,98190,98190\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,824,0.0144,19734000,98670,98670\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,828,0.0146,19830000,99150,99150\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,832,0.0146,19926000,99630,99630\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,836,0.0146,20022000,100110,100110\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,840,0.0147,20118000,100590,100590\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,844,0.0147,20214000,101070,101070\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,848,0.0148,20310000,101550,101550\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,852,0.0148,20406000,102030,102030\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,856,0.0150,20502000,102510,102510\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,860,0.0150,20598000,102990,102990\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,864,0.0151,20694000,103470,103470\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,868,0.0151,20790000,103950,103950\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,872,0.0152,20886000,104430,104430\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,876,0.0153,20982000,104910,104910\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,880,0.0154,21078000,105390,105390\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,884,0.0154,21174000,105870,105870\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,888,0.0154,21270000,106350,106350\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,892,0.0155,21366000,106830,106830\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,896,0.0157,21462000,107310,107310\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,900,0.0156,21558000,107790,107790\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,904,0.0158,21654000,108270,108270\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,908,0.0159,21750000,108750,108750\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,912,0.0159,21846000,109230,109230\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,916,0.0161,21942000,109710,109710\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,920,0.0161,22038000,110190,110190\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,924,0.0162,22134000,110670,110670\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,928,0.0164,22230000,111150,111150\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,932,0.0164,22326000,111630,111630\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,936,0.0164,22422000,112110,112110\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,940,0.0164,22518000,112590,112590\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,944,0.0165,22614000,113070,113070\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,948,0.0167,22710000,113550,113550\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,952,0.0168,22806000,114030,114030\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,956,0.0168,22902000,114510,114510\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,960,0.0168,22998000,114990,114990\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,964,0.0174,23094000,115470,115470\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,968,0.0172,23190000,115950,115950\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,972,0.0173,23286000,116430,116430\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,976,0.0172,23382000,116910,116910\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,980,0.0174,23478000,117390,117390\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,984,0.0174,23574000,117870,117870\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,988,0.0176,23670000,118350,118350\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,992,0.0176,23766000,118830,118830\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,996,0.0179,23862000,119310,119310\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,1000,0.0177,23958000,119790,119790\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,1004,0.0178,24054000,120270,120270\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,1008,0.0178,24150000,120750,120750\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,1012,0.0180,24246000,121230,121230\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,1016,0.0180,24342000,121710,121710\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,1020,0.0181,24438000,122190,122190\n",
- "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n",
- "200,32,1024,0.0178,24534000,122670,122670\n",
- "mv /gpfs/wolf/gen110/scratch/aherten//poisson2d.vflop.bin.csv .\n"
- ]
- }
- ],
- "source": [
- "!make bench_task4"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 47,
- "metadata": {},
- "outputs": [],
- "source": [
- "df_sflop = pd.read_csv(\"poisson2d.sflop.bin.csv\", skiprows=range(2, 50000, 2))\n",
- "df_vflop = pd.read_csv(\"poisson2d.vflop.bin.csv\", skiprows=range(2, 50000, 2))\n",
- "df_flop = pd.concat([df_sflop.set_index(\"nx\"), df_vflop.set_index(\"nx\")[['PM_VECTOR_FLOP_CMPL (total)', 'PM_VECTOR_FLOP_CMPL (min)', ' PM_VECTOR_FLOP_CMPL (max)']]], axis=1).reset_index()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The name of the vector counter is a bit misleading; not floating point operations are measured but floating point instructions. To get *real* floating point operations, each value needs to be multiplied by the vector width (2). We can plot the values afterwards (non-interactive: `make graph_task4`)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 49,
- "metadata": {},
- "outputs": [],
- "source": [
- "common.normalize(df_flop, \"PM_SCALAR_FLOP_CMPL (min)\", \"Scalar FlOps / Loop Iteration\")\n",
- "common.normalize(df_flop, \"PM_VECTOR_FLOP_CMPL (min)\", \"Vector Instructions / Loop Iteration\")\n",
- "df_flop[\"Vector FlOps / Loop Iteration\"] = df_flop[\"Vector Instructions / Loop Iteration\"] * 2"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 50,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 1008x432 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "df_flop.set_index(\"nx\")[[\"Scalar FlOps / Loop Iteration\", \"Vector FlOps / Loop Iteration\"]].plot();"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "With that measured, we can determine the Arithmetic Intensity; the balance of floating point operations to bytes transmitted:\n",
- "\n",
- "\\begin{align}\n",
- "\\text{AI}^\\text{emp} = I_\\text{flop} / I_\\text{mem} \\text{,}\n",
- "\\end{align}\n",
- "\n",
- "with $I$ denoting the respective amount. This is the emperically determined Arithmetic Intensity.\n",
- "\n",
- "In the non-interactive version of the Notebook, please plot the graph calling `make graph_task4-2` in the terminal."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 66,
- "metadata": {},
- "outputs": [],
- "source": [
- "I_flop_scalar = df_flop.set_index(\"nx\")[\"Scalar FlOps / Loop Iteration\"]\n",
- "I_flop_vector = df_flop.set_index(\"nx\")[\"Vector FlOps / Loop Iteration\"]\n",
- "I_mem_load = df_byte[\"Loads / Loop Iteration\"]\n",
- "I_mem_store = df_byte[\"Stores / Loop Iteration\"]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 75,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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- "text/plain": [
- "<Figure size 1008x432 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "df_ai = pd.DataFrame()\n",
- "df_ai[\"Arithmetic Intensity\"] = (I_flop_scalar + I_flop_vector) / (I_mem_load + I_mem_store)\n",
- "ax = df_ai.plot();\n",
- "ax.set_ylabel(\"Byte/FlOp\");"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Thinking back to the first lecture of the tutorial, what Arithemtic Intensity did you expect?"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Task E2: Measuring a Larger Range\n",
- "<a name=\"taske2\"></a>\n",
- "\n",
- "If you still still have time, you might venture into your own benchmarking adventure.\n",
- "\n",
- "\n",
- "**TASK**: Revisit the counters measured above for a larger range of `nx`. Right now, we only studied `nx` until 1000. New effects appear above that value – partly only well above, though ($nx > 15000$).\n",
- "\n",
- "You're on your own here. Edit the `bench.sh` script to change the range and the stepping increments.\n",
- "\n",
- "**Good luck!**\n",
- "\n",
- "[Back to top](#toc)"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
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+{"cells": [{"cell_type": "markdown", "metadata": {}, "source": ["# Hands-On: Performance Counters\n", "\n", "This Notebook is part of the exercises for the SC19 Tutorial \u00bbApplication Porting and Optimization on GPU-accelerated POWER Architectures\u00ab. It is to be run on a POWER9 machine; in the tutorial: on Ascent, the POWER9 training cluster of Oak Ridge National Lab.\n", "\n", "This Notebook can be run interactively on Ascent. If this capability is unavailable to you, use it as a description for executing the tasks on Ascent via a shell access. During data evaluation, the Notebook mentions the corresponding commands to execute in case you are not able to run the Notebook interactively directly on Ascent.\n", "\n", "## Table of Contents\n", "<a name=\"toc\"></a>\n", "\n", "* [Task 1: Measuring Cycles and Instructions](#task1)\n", "* [Task 2: Measuring Loads and Stores](#task2)\n", " - [A: Loads and Stores](#task2-a)\n", " - [B: More Loads and Stores](#task2-b)\n", " - [C: Bandwidth](#task2-c)\n", "* [Task E1: Measuring FLOP](#taske1)\n", "* [Task E2: Measuring a Greater Range](#taske2)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Task 1: Measuring Cycles and Instructions\n", "<a name=\"task1\"></a>\n", "\n", "Throughout this exercise, the core loop of the Jacobi algorithm is instrumented and analyzed. The part in question is\n", "\n", "```c\n", "for (int iy = iy_start; iy < iy_end; iy++)\n", "{\n", " for( int ix = ix_start; ix < ix_end; ix++ )\n", " {\n", " Anew[iy*nx+ix] = -0.25 * (rhs[iy*nx+ix] - (A[ iy *nx+ix+1] + A[ iy *nx+ix-1]\n", " + A[(iy-1)*nx+ix ] + A[(iy+1)*nx+ix ]));\n", " error = fmaxr( error, fabsr(Anew[iy*nx+ix]-A[iy*nx+ix]));\n", " }\n", "}\n", "```\n", "\n", "The code is instrumented using PAPI. The API routine `PAPI_add_named_event()` is used to add *named* PMU events outside of the relaxation iteration. After that, calls to `PAPI_start()`\n", "and `PAPI_stop()` can be used to count how often a PMU event is incremented.\n", "\n", "For the first task, we will measure quantities often used to characterize an application: cycles and instructions.\n", "\n", "**TASK**: Please measure counters for completed instructions and run cycles. See the TODOs in file [`poisson2d.ins_cyc.c`](poisson2d.ins_cyc.c). You can either edit the files with Jupyter capabilities by clicking on the link of the file or selecting it in the file drawer on the left; or use a dedicated editor on the system(`vim` is available). The names of the counters to be implemented are `PM_INST_CMPL` and `PM_RUN_CYC`.\n", "\n", "After changing the source code, compile it with `make task1` or by executing the following cell (we need to change directories first, though). \n", "*(Using the `Makefile` we have hidden quite a few intricacies from you in order to focus on the relevant content at hand. Don't worry too much about it right now\u00a0\u2013 we'll un-hide it gradually during the course of the tutorial.)*\n", "\n", "[Back to top](#toc)"]}, {"cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["/autofs/nccsopen-svm1_home/aherten/OpenPOWER-SC19/Prototyping/2-Performance_Counters/Handson/Solutions\n"]}], "source": ["!pwd"]}, {"cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["/autofs/nccsopen-svm1_home/aherten/OpenPOWER-SC18/2-PAPI/Compiling/Solutions\n"]}], "source": ["%cd Tasks/\n", "# Use `%cd Solutions` to look at the solutions for each task"]}, {"cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["gcc -DUSE_DOUBLE -Ofast -std=c99 -lm -lpapi poisson2d.ins_cyc.c -o poisson2d.ins_cyc.bin\n"]}], "source": ["!make task1"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Before we launch our measurement campaign we should make sure that the program is measuring correctly. Let's invoking it, for instance, with these arguments: `./poisson2d.ins_cyc.bin 100 64 32` \u2013 see the next cell. The `100` specifies the number of iterations to perform, `64` and `32` are the size of the grid in y and x direction, respectively."]}, {"cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "100,64,32,0.0011,3324225,33235,33960,1859440,18357,25033\n"]}], "source": ["!./poisson2d.ins_cyc.bin 100 64 32\n", "# alternatively call !make run_task1"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Alright! That should return a comma-seperated list of measurements.\n", "\n", "For the following runs, we are going to use Ascent's compute backend nodes which are not shared amongst users and also have six GPUs available (each!). We use the available batch scheduler *IBM Spectrum LSF* for this. For convenience, a call to the batch submission system is stored in the environment variable `$SC19_SUBMIT_CMD`. You are welcome to adapt it once you get more familiar with the system.\n", "\n", "For now, we want to run our first benchmarking run and measure cycles and instructions for different data sizes, as a function of `nx`. The Makefile holds a target for this, call it with `make bench_task1`:"]}, {"cell_type": "code", "execution_count": 2, "metadata": {"scrolled": true}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["bsub -W 60 -nnodes 1 -Is -P TRN003 jsrun -n 1 -c 1 -g ALL_GPUS ./bench.sh poisson2d.ins_cyc.bin /gpfs/wolf/trn003/scratch/aherten//poisson2d.ins_cyc.bin.csv\n", "Job <24059> is submitted to default queue <batch>.\n", "<<Waiting for dispatch ...>>\n", "<<Starting on login1>>\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,4,0.0012,572978,2861,3639,261330,1235,4684\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,8,0.0014,1082978,5411,6189,601962,2914,5099\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,12,0.0014,1442978,7211,7989,811603,3992,5761\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,16,0.0014,1802978,9011,9789,1017305,4988,7017\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,20,0.0015,2162978,10811,11589,1221559,6002,7999\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,24,0.0016,2522978,12611,13389,1435167,7037,9259\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,28,0.0016,2882978,14411,15189,1633061,8054,9789\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,32,0.0017,3242978,16211,16989,1842895,9092,10889\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,36,0.0018,3602978,18011,18789,2042894,10108,12457\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,40,0.0019,3962978,19811,20589,2261332,11191,14233\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,44,0.0020,4322978,21611,22389,2458267,12112,14375\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,48,0.0020,4682978,23411,24189,2658621,13164,15613\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,52,0.0020,5042978,25211,25989,2866175,14190,16864\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,56,0.0021,5402978,27011,27789,3080357,15237,21565\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,60,0.0022,5762978,28811,29589,3283103,16278,18799\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,64,0.0022,6122978,30611,31389,3587582,17820,19681\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,68,0.0025,6482978,32411,33189,3893368,19284,20847\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,72,0.0025,6842978,34211,34989,4289441,21278,22715\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,76,0.0024,7202978,36011,36789,4208700,20936,22677\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,80,0.0025,7562978,37811,38589,4409613,21897,23855\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,84,0.0026,7922978,39611,40389,4611755,22921,24910\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,88,0.0026,8282978,41411,42189,4821904,23974,26087\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,92,0.0028,8642978,43211,43989,5104722,25036,38488\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,96,0.0028,9002978,45011,45789,5238952,26060,27927\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,100,0.0028,9362978,46811,47589,5441545,27049,29275\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,104,0.0030,9722978,48611,49389,5920763,28136,72679\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,108,0.0030,10082978,50411,51189,5853554,29106,31403\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,112,0.0030,10442978,52211,52989,6053498,30123,32279\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,116,0.0031,10802978,54011,54789,6296056,31338,33377\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,120,0.0033,11162978,55811,56589,6468115,32146,33869\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,124,0.0032,11522978,57611,58389,6675248,33233,35075\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,128,0.0033,11882978,59411,60189,6894325,34338,36207\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,132,0.0034,12242978,61211,61989,7093543,35299,37463\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,136,0.0034,12602978,63011,63789,7312105,36353,48105\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,140,0.0035,12962978,64811,65589,7503757,37375,39247\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,144,0.0036,13322978,66611,67389,7692611,38277,40419\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,148,0.0037,13682978,68411,69189,7968094,39656,42113\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,152,0.0037,14042978,70211,70989,8122466,40468,42706\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,156,0.0038,14402978,72011,72789,8328043,41484,45104\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,160,0.0040,14762978,73811,74589,8547674,42493,54216\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,164,0.0039,15122978,75611,76389,8738805,43542,45427\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,168,0.0040,15482978,77411,78189,8948025,44560,46819\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,172,0.0040,15842978,79211,79989,9186567,45735,47659\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,176,0.0041,16202978,81011,81789,9391949,46573,70131\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,180,0.0042,16562978,82811,83589,9549568,47559,54271\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,184,0.0042,16922978,84611,85389,9766306,48609,58645\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,188,0.0043,17282978,86411,87189,9974165,49613,56721\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,192,0.0044,17642978,88211,88989,10187263,50734,52953\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,196,0.0044,18002978,90011,90789,10386920,51763,53773\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,200,0.0045,18362978,91811,92589,10593326,52744,54962\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,204,0.0045,18722978,93611,94389,10791966,53796,55775\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,208,0.0046,19082978,95411,96189,10993938,54691,56692\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,212,0.0047,19442978,97211,97989,11183564,55716,57663\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,216,0.0047,19802978,99011,99789,11413409,56842,65317\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,220,0.0049,20162978,100811,101589,11747337,57952,85917\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,224,0.0049,20522978,102611,103389,11967444,58993,147575\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,228,0.0050,20882978,104411,105189,12176974,59986,107137\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,232,0.0051,21242978,106211,106989,12243039,61011,62843\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,236,0.0051,21602978,108011,108789,12454738,61985,74677\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,240,0.0051,21962978,109811,110589,12632612,62912,64911\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,244,0.0052,22322978,111611,112389,12844679,63954,74316\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,248,0.0053,22682978,113411,114189,13049050,65048,67067\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,252,0.0054,23042978,115211,115989,13274577,66113,68093\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,256,0.0054,23402978,117011,117789,13479975,67191,69232\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,260,0.0055,23762978,118811,119589,13702476,68321,70257\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,264,0.0055,24122978,120611,121389,13885554,69178,71473\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,268,0.0056,24482978,122411,123189,14091173,70236,72538\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,272,0.0057,24842978,124211,124989,14277355,71142,73153\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,276,0.0057,25202978,126011,126789,14477479,72149,74585\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,280,0.0058,25562978,127811,128589,14807542,73365,106386\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,284,0.0059,25922978,129611,130389,14919273,74349,83988\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,288,0.0060,26282978,131411,132189,15262342,75369,108903\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,292,0.0061,26642978,133211,133989,15457489,76550,112579\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,296,0.0061,27002978,135011,135789,15587890,77470,113796\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,300,0.0063,27362978,136811,137589,15736737,78474,80976\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,304,0.0062,27722978,138611,139389,15931699,79424,85309\n", 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(min), PM_RUN_CYC (max)\n", "200,32,344,0.0069,31322978,156611,157389,18045749,89720,131352\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,348,0.0069,31682978,158411,159189,18233228,90790,129666\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,352,0.0070,32042978,160211,160989,18429938,91908,93827\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,356,0.0071,32402978,162011,162789,18723870,92891,169000\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,360,0.0071,32762978,163811,164589,18839189,93872,104313\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,364,0.0072,33122978,165611,166389,19052230,94828,108456\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,368,0.0072,33482978,167411,168189,19224348,95828,106832\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,372,0.0073,33842978,169211,169989,19409746,96825,98825\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,376,0.0074,34202978,171011,171789,19635914,97934,100015\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,380,0.0075,34562978,172811,173589,19901265,99194,108856\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,384,0.0075,34922978,174611,175389,20087150,100132,113306\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,388,0.0076,35282978,176411,177189,20289560,101187,111225\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,392,0.0076,35642978,178211,178989,20478069,102158,104431\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,396,0.0077,36002978,180011,180789,20703541,103136,118462\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", 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(total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,572,0.0109,51842978,259211,259989,29760468,148529,150992\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,576,0.0108,52202978,261011,261789,30001693,149671,152448\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,580,0.0109,52562978,262811,263589,30194219,150474,161954\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,584,0.0110,52922978,264611,265389,30465237,151575,196784\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,588,0.0112,53282978,266411,267189,30866027,152658,345805\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,592,0.0112,53642978,268211,268989,30806266,153631,162459\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,596,0.0112,54002978,270011,270789,31013348,154624,161083\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,600,0.0113,54362978,271811,272589,31227644,155782,158034\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,604,0.0115,54722978,273611,274389,31534633,156837,219588\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,608,0.0114,55082978,275411,276189,31675474,157869,168332\n", 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"200,32,628,0.0117,56882978,284411,285189,32609412,162678,167394\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,632,0.0118,57242978,286211,286989,32869379,163975,168634\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,636,0.0119,57602978,288011,288789,33151217,165037,223167\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,640,0.0119,57962978,289811,290589,33341299,166215,181218\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,644,0.0121,58322978,291611,292389,33649260,167751,199967\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC 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(min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,668,0.0124,60482978,302411,303189,34905159,173832,222673\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,672,0.0126,60842978,304211,304989,34988298,174422,188003\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,676,0.0126,61202978,306011,306789,35263092,175911,185984\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,680,0.0127,61562978,307811,308589,35503073,176323,305860\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,684,0.0128,61922978,309611,310389,35672483,178036,180851\n", 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(min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,744,0.0136,67322978,336611,337389,38643004,192776,211409\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,748,0.0138,67682978,338411,339189,38834497,193752,204307\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,752,0.0139,68042978,340211,340989,39026422,194674,207102\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,756,0.0139,68402978,342011,342789,39292510,195755,242534\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,760,0.0140,68762978,343811,344589,39445808,196904,199749\n", 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(total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,800,0.0146,72362978,361811,362589,41586665,207290,248916\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,804,0.0147,72722978,363611,364389,41696398,208106,211465\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,808,0.0148,73082978,365411,366189,41978951,209272,255137\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,812,0.0148,73442978,367211,367989,42187366,209918,283393\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,816,0.0149,73802978,369011,369789,42482639,211214,322437\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,820,0.0149,74162978,370811,371589,42512865,212010,227823\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,824,0.0151,74522978,372611,373389,42861251,213412,278868\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,828,0.0151,74882978,374411,375189,42979335,214191,262439\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,832,0.0152,75242978,376211,376989,43402619,215543,296991\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,836,0.0152,75602978,378011,378789,43382253,216450,232179\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,840,0.0154,75962978,379811,380589,43665001,217538,261020\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,844,0.0154,76322978,381611,382389,43762162,218196,232967\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,848,0.0156,76682978,383411,384189,44077885,219619,233562\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,852,0.0155,77042978,385211,385989,44269902,220266,357562\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,856,0.0156,77402978,387011,387789,44458368,221658,275183\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,860,0.0156,77762978,388811,389589,44599845,222530,244104\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,864,0.0158,78122978,390611,391389,44856987,223898,229495\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,868,0.0157,78482978,392411,393189,45070339,224667,268426\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,872,0.0158,78842978,394211,394989,45243346,225686,238504\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,876,0.0160,79202978,396011,396789,45425044,226467,285843\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,880,0.0160,79562978,397811,398589,45637897,227585,255503\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,884,0.0163,79922978,399611,400389,45922301,228540,294854\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,888,0.0161,80282978,401411,402189,46210377,229936,317062\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,892,0.0161,80642978,403211,403989,46224897,230736,244030\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,896,0.0163,81002978,405011,405789,46706945,232252,393574\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,900,0.0163,81362978,406811,407589,46846573,233803,243774\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,904,0.0165,81722978,408611,409389,47211102,235424,247115\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,908,0.0165,82082978,410411,411189,47420647,236067,308146\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,912,0.0167,82442978,412211,412989,47664515,237299,252663\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,916,0.0166,82802978,414011,414789,47825500,238210,307878\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,920,0.0168,83162978,415811,416589,48024315,239591,249230\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,924,0.0168,83522978,417611,418389,48204506,240348,286103\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,928,0.0168,83882978,419411,420189,48474452,241766,272232\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,932,0.0169,84242978,421211,421989,48643328,242408,310910\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,936,0.0170,84602978,423011,423789,49041567,243670,350571\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,940,0.0171,84962978,424811,425589,49009612,244295,313509\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,944,0.0171,85322978,426611,427389,49257311,245620,259650\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,948,0.0172,85682978,428411,429189,49415667,246533,254714\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,952,0.0172,86042978,430211,430989,49711139,247671,319628\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,956,0.0174,86402978,432011,432789,49856592,248552,271876\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,960,0.0174,86762978,433811,434589,50136102,249978,265617\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,964,0.0176,87122978,435611,436389,50925446,253713,295499\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,968,0.0178,87482978,437411,438189,51035835,253858,318894\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,972,0.0177,87842978,439211,439989,51188317,255334,306288\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,976,0.0178,88202978,441011,441789,51436023,256205,289239\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,980,0.0179,88562978,442811,443589,51703656,257814,300077\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,984,0.0179,88922978,444611,445389,51801305,257947,349721\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,988,0.0181,89282978,446411,447189,52056854,259676,262216\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,992,0.0182,89642978,448211,448989,52237864,260535,269494\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,996,0.0183,90002978,450011,450789,52526126,262024,274178\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,1000,0.0182,90362978,451811,452589,52578843,262284,265526\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,1004,0.0183,90722978,453611,454389,52896370,263840,273834\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,1008,0.0183,91082978,455411,456189,53074476,264385,308471\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,1012,0.0184,91442978,457211,457989,53382079,266422,284446\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,1016,0.0186,91802978,459011,459789,53434221,266486,275700\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,1020,0.0186,92162978,460811,461589,53712164,268036,277528\n", "iter,ny,nx,Runtime,PM_INST_CMPL (total),PM_INST_CMPL (min), PM_INST_CMPL (max),PM_RUN_CYC (total),PM_RUN_CYC (min), PM_RUN_CYC (max)\n", "200,32,1024,0.0187,92522978,462611,463389,53754294,268076,276795\n", "mv /gpfs/wolf/trn003/scratch/aherten//poisson2d.ins_cyc.bin.csv .\n"]}], "source": ["!make bench_task1"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Once the run is completed, let's study the data!\n", "\n", "This can be done best in the interactive version of the Jupyter Notebook. In case this version of the description is unavailable to you, call the Makefile target `make graph_task1` (either with X forwarding, or download the resulting PDF)."]}, {"cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": ["import numpy as np\n", "import seaborn as sns\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import common\n", "%matplotlib inline\n", "sns.set()\n", "plt.rcParams['figure.figsize'] = [14, 6]"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Execute the following cell if you want to switch to color-blind-safer colors"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["sns.set_palette(\"colorblind\")"]}, {"cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>iter</th>\n", " <th>ny</th>\n", " <th>nx</th>\n", " <th>Runtime</th>\n", " <th>PM_INST_CMPL (total)</th>\n", " <th>PM_INST_CMPL (min)</th>\n", " <th>PM_INST_CMPL (max)</th>\n", " <th>PM_RUN_CYC (total)</th>\n", " <th>PM_RUN_CYC (min)</th>\n", " <th>PM_RUN_CYC (max)</th>\n", " <th>Grid Points</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>200</td>\n", " <td>32</td>\n", " <td>4</td>\n", " <td>0.0012</td>\n", " <td>572978</td>\n", " <td>2861</td>\n", " <td>3639</td>\n", " <td>261330</td>\n", " <td>1235</td>\n", " <td>4684</td>\n", " <td>128</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>200</td>\n", " <td>32</td>\n", " <td>8</td>\n", " <td>0.0014</td>\n", " <td>1082978</td>\n", " <td>5411</td>\n", " <td>6189</td>\n", " <td>601962</td>\n", " <td>2914</td>\n", " <td>5099</td>\n", " <td>256</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>200</td>\n", " <td>32</td>\n", " <td>12</td>\n", " <td>0.0014</td>\n", " <td>1442978</td>\n", " <td>7211</td>\n", " <td>7989</td>\n", " <td>811603</td>\n", " <td>3992</td>\n", " <td>5761</td>\n", " <td>384</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>200</td>\n", " <td>32</td>\n", " <td>16</td>\n", " <td>0.0014</td>\n", " <td>1802978</td>\n", " <td>9011</td>\n", " <td>9789</td>\n", " <td>1017305</td>\n", " <td>4988</td>\n", " <td>7017</td>\n", " <td>512</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>200</td>\n", " <td>32</td>\n", " <td>20</td>\n", " <td>0.0015</td>\n", " <td>2162978</td>\n", " <td>10811</td>\n", " <td>11589</td>\n", " <td>1221559</td>\n", " <td>6002</td>\n", " <td>7999</td>\n", " <td>640</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>"], "text/plain": [" iter ny nx Runtime PM_INST_CMPL (total) PM_INST_CMPL (min) \\\n", "0 200 32 4 0.0012 572978 2861 \n", "1 200 32 8 0.0014 1082978 5411 \n", "2 200 32 12 0.0014 1442978 7211 \n", "3 200 32 16 0.0014 1802978 9011 \n", "4 200 32 20 0.0015 2162978 10811 \n", "\n", " PM_INST_CMPL (max) PM_RUN_CYC (total) PM_RUN_CYC (min) \\\n", "0 3639 261330 1235 \n", "1 6189 601962 2914 \n", "2 7989 811603 3992 \n", "3 9789 1017305 4988 \n", "4 11589 1221559 6002 \n", "\n", " PM_RUN_CYC (max) Grid Points \n", "0 4684 128 \n", "1 5099 256 \n", "2 5761 384 \n", "3 7017 512 \n", "4 7999 640 "]}, "execution_count": 2, "metadata": {}, "output_type": "execute_result"}], "source": ["plt.rcParams['figure.figsize'] = [14, 6]\n", "df = pd.read_csv(\"poisson2d.ins_cyc.bin.csv\", skiprows=range(2, 50000, 2)) # Read in the CSV file from the bench run; parse with Pandas\n", "df[\"Grid Points\"] = df[\"nx\"] * df[\"ny\"] # Add a new column of the number of grid points (the product of nx and ny)\n", "df.head() # Display the head of the Pandas dataframe"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Let's have a look at the counters we've just measured and see how they scaling with increasing number of grid points.\n", "\n", "*In the following, we are always using the minimal value of the counter (indicated by \u00bb(min)\u00ab) as this should give us an estimate of the best achievable result of the architecture.*"]}, {"cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 1008x432 with 2 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["fig, (ax1, ax2) = plt.subplots(nrows=2, sharex=True)\n", "df.set_index(\"Grid Points\")[\"PM_RUN_CYC (min)\"].plot(ax=ax1, legend=True);\n", "df.set_index(\"Grid Points\")[\"PM_INST_CMPL (min)\"].plot(ax=ax2, legend=True);"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Although some slight variations can be seen for run cycles for many grid points, the correlation looks quite linear (as one would naively expect). Let's test that by fitting a linear function!\n", "\n", "*The details of the fitting have been extracted into dedicated function, `print_and_return_fit()`, of the `common.py` helper file. If you're interested, [go have a look at it](common.py).* "]}, {"cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": ["def linear_function(x, a, b):\n", " return a*x+b"]}, {"cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Counter PM_RUN_CYC (min) is proportional to the grid points (nx*ny) by a factor of 8.1021 (\u00b1 0.0057)\n", "Counter PM_INST_CMPL (min) is proportional to the grid points (nx*ny) by a factor of 14.0630 (\u00b1 0.0003)\n"]}], "source": ["fit_parameters, fit_covariance = common.print_and_return_fit(\n", " [\"PM_RUN_CYC (min)\", \"PM_INST_CMPL (min)\"], \n", " df.set_index(\"Grid Points\"), \n", " linear_function,\n", " format_uncertainty=\".4f\"\n", ")"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Let's overlay our fits to the graphs from before."]}, {"cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [{"data": {"image/png": 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StR+pQUnVfuvXv/4Fp5125pSfVbUrbreT7u4IL774PCee+IFprVtJ1czQLw/pRzFLP4pZelG8UsO2bbojAzRt2Ei4L0njcCaJaBuftH5DljMOgIWDflchG4LH45+9jNklWRTkOCipLFPMUmR3k6qpblQh+7ALLzx32+YKWy1YsJArr7xmxu750EO/5de/3nEW5rXXfo05c4wZu+87nX76zO2uX1BQMO0JlYiIiOw/bNumKzpCQ1sU18bHcQ+E8Y12ECTKPIdNx/hCotlHUxooIWovIVE+m6K6OWQEKslze6ja9S1kH6ekaj9y330/2ev3XLPmw6xZ8+G9fl8RERGRVIiNxtls1hPraMDZ10rmcDstI1n8anAZYHNT/pPYThcD3hK6CheSXz2Hk+rms8ZfNFHDslQ2X2aIkioRERERkZ2wkwmS0TA9HZ2sHw7xan2EE6M/o87dBYBlO4g68ikqMPjUEQZ1ZXkU+VeSkeWjLMVtl71LSZWIiIiIyITRzS8xYL6AFWkhc6QTFxZYHn7Z9zHKgz6Gy1cSDWRSVDMHX6iaPLeXmlQ3WlJOSZWIiIiIHDBs28Ye7sXqaSbR00SsowEr0sKjxRewqXOEJQOPc4hnM62JQnqcC7HyKwjUzuW2gwwK87KAlanuguyDlFSJiIiIyH7JthJYfe1YPc24KhYSjXuJvvQwJZt/B4BlQ6+VSzhRwPreFoKhEPFFp9BeVsDs0lwO8Xv1jEqZFCVV+5gzzliDx+PB4/ECsGzZci6//Mvce+/d1NbWcdxxJ/Lyyy+RSCQ49NDDJlXnM888xb33/ge2DbZtccEFF3PMMat3OO+FF/7O2rV3Ul+/idNP/xif+9wXt5Ulk0m+//3beP75Z3E4HJxzznk73aDCtm0cDgc33XQD1157w7af96aNG02+//3b6O/vA+Cyy77IqlVHsHGjyc0334hl2SQSCRYtWsKXvnQlHo+H//mfn/PHP/5+Wx1tbWHWrDmVL31px2dfvdfr+c74XXrp51m5ctVe6LWIiMiBbevvHMm+DobWPUS8q4mMoQ6cdgKAB+Mn8MJgKcVOMLwrSeZWkFNaQ1V5EXNKc7klkI1TCZTsJiVV+6BvfONW6upmv+3YRRddsu3fr7yyjpGRkUklVbZt8/Wvf5W77rqHurrZbNq0kUsvvZCjjjp2h2c6lZWVc9VV1/L4439lfHz8bWWPPvow4XALP//5/9Lf388FF3yCFSsOpbT07csw//CH3xGJ9DA2Nsbf//4sf/3rY1x11XW4XK6pvgw7OOOMNfzqVw+95zkjIyNcc82/8bWvfYOFCxeRSCQYHh4CoKqqmrVr7ycjIwPLsrj++qv43e9+w5lnnrXtD0AikeDDH/4gJ5yw4zbqk3k9dxY/ERERmR62bWPH+rAiTSS6mxgM12P3tvBq5nL+Pm6Q7Gvn056XCCcLaU0YRFxBxv3leMtK+URpPnVluVQEfWS4p/fZlnJgU1KVJm666QbmzZvP0qXL+d3vfoNlWbz00gscd9yJnHvuee95rdPpZGhoS2IxNDRIIFC004fkVlRUAvDUU0/sUPbXvz7GmjUfxul0UlBQwFFHHcPf/vZnPv7xT77tvDVrPsyzzz7Nj350D263m+uvv3GHkaqmpka+9KXLuOuuewmFSrnvvrU0Nzfy7/9+81Rekp167LE/sXjxEhYuXASA2+0mLy8fAK83c9t5iUSCsbExnM4dv5F65pknCQQCzJt30E7vMdnXU0RERPaMbSWx+juwIs1YGTm0uqupb+rksNe+ue2cwaSf1mQhm0acePJdBGfP5rWia6kK+Tmh2EdOZkYKeyAHCiVVOxF7aMdf7t11h+JZcBx2YoyRh7+7Q3nG3CPJMI7CGh1k9LE7diw/aDVuY3LTwK677qp3nT42a9ZsTj31I4yMjLxtet4VV1zORRddskMi4HA4uPHGm/nKV75MZmYWsViMb3/7+5Nqx/Y6OzsIhUq3/VxSEqKrq3OH8/7wh9/S09PDMces5sQTP8itt36DK6+85m0jVdXVNVx88Wf56le/wkUXXcKf//wI997731Nu0840Ntbjdru54orL6enpwTDmcdllXyQ3NxeAnp5urrjiC4TDraxadQSnnPKRHer44x9/z8knn7LT+ifzev77v18P2CxatJTPfOYy/H7/tPRNRERkf2ZbSRzOLb8vDDz5AKNhE89QO66J6XuvJaq4Z+BYANr8R4G/BG9xFbNqQiypzOfoHE+qmi6ipGpftDvTx2677fadHk8kEvzkJ/dz883fYfHipfzzn+v56le/wgMP/A/Z2dnT0dy3OfnkU7etqTrssMNZuXLVTtdUfeADJ7Nu3Yt85Stf5s477yUnx7fT+q666kt0dm5J3np6ujnvvI8D4HK5dvqw42Qyybp1L3L33T+isLCQH/7we9xxx/e45pqvAVBUFOT++3/KyMgIN954PU888VeOP/79267v6elh3boXueaaG3banl29nnfeeQ8lJSHGx8e5/fbv8L3vfYuvfvXrU3oNRURE9ndWrB+rp4lEpInRjkasSDOjeHg0cC71bQN8YOQtvI4E4cRcelxBYtml5FdX8dnqIuZW5pObs+PacJFUUlK1E9lrvvKuZQ639z3LnZn+9yzf2zZt2kAk0s3ixUsBWLx4KVlZWTQ1NTB//oJJ11NSEqKjo33bNe8cudpqawJ17bU3vO3nd4rH4zQ01OPz+YlGI+9631tv/d62f59xxhruv/+nu2hnKcuWraCoaMtTy0844f3cfPONO5yXlZXFccedwKOP/ultSdXDD/+BVauOID8/f6f17+r1LCkJAeDxeDjttDO5+ur/957tFRER2Z/ZloU10IHV08xopJ1NhUcS7h6mdtPPqBo1AYgmfYSThTQming50k1ViY/e+Rcxr7qAhcU+vBl7vi5bZKYpqUpDOTk59PR0T+rcYLCYrq4umpsbqaqqobGxgUgkQnl5xZTu+b73Hc9DD/2WY45ZTX9/P0899QR33PGfu9N8AO688wcYxjyuueZrXHHF5dx9939RXFyy2/VttXr1CVx55eXEYsNkZ+fw/PPPMXv2XADC4VaKi0vIyMggHo/z1FNPMGvW20cEH374IT7/+S+9a/3v9XqOjIyQTCbx+XzYts2f//zItnuLiIjs7+zEGDgzcDid9L76NInXHp2YvhcHIGE7ubcvkxHby8Lc+ZTkLSIjWEVxMEBpIJvFRTmck60pfJKelFSloaOPfh/XXnsl55338W0bVbzbmqpAoIgrrria6667Codjy2YK11zzNXJz84C3r8X6xz/Wc8MN1zA8PIxt2/zlL49y9dXXs3LlKt7//pN4443XOOus0wA477yLppyYbfXkk4/zyivr+M//vB+v18sFF3yaG264lttvvxu3e8/ekqFQiI9//JN85jPn43Q6KS0t49/+7VoAXnvtnzz44I9xOJxYVpKlS5dz3nkXbrv2n/9cTywW49BD37727a233uDee+/mtttuf8/XMxxu5brr/g3LskgmLWpqavnyl6/eo/6IiIjsi+zxGMnOzSR7mohNTN/LiHXzWPF5PN+eQeWIyRHeMcLJ2UScQeyCCoLVdXyhuojKYh9ZXv0KKvsXh23bqW7D3lIDNEQiQ1jWv/rc0dFEKFS9VxrgdjtJJKy9ci+ZHjMZs7353juQBIN+ursHU90MmQLFLP0oZullT+Jl2xZ2fxfJSDMjHQ1E8ubT4SjGan2VxU0PAhBJ5hBOFhJOFvCqYz4lFRXMq8pndkUeJQXZSqB2gz5jqeN0OggEfAC1QONkr9O7XERERESwE+OQGMfyZNPW3Ibnuf/EO9yOy9oyfc+yHfw5FuW5sbl4SbKoYA2+slpqqkKUFeWwuCCLM7My3nU9tcj+bJdJlWEYAeAnwCxgDNgEfMY0zW7DMA4D1gJZbMnkzjFNs2viur1aJiIiIiKTY9s2yfAbjHU2MNLRANFWPLEu3vAu4b8jBxMfH+dS/xhtyVn0e0pwBaooqKhlVUkeH8rLpNCfidejDSREtprMSJUNfMs0zccBDMP4NnCLYRgXAQ8A55mm+bRhGNcBtwAXGIbh2Jtl0/ViiIiIiOxPbNsiHu0gXv8Gw231DCdc1BccTnPnEO9rvAs/w8SS2bQmCwknF9FDFYctCDG3Mo9g0SoWFGRp9z2RSdhlUmWaZi/w+HaH/g5cCqwARk3TfHri+N1sGT26IAVle8S2bQ1Vy15l2xag95yIiEwfOzGONdjDUEaA7v5RPOsexN+1niFrfOIEB03xcn40VIg3w8V46FRKyssJlRVTkZfJ0rxMsjMzUtsJkTQ1pTVVhmE42ZJQ/R6oApq2lpmm2WMYhtMwjMK9XTaR+O0Wt9vD8PAAOTm5Sqxkxtm2TTKZYHAwiseTmermiIhIGkv0NNG/6Z/E2utx9ofJGe8hbru4OnoWNg5WZyYocNYynFWKO1BFQVUtxYE8binIIpDrxeV0proLIvuNqW5U8UNgCLgDOG36mzPzJnbz2CY/P5OWlha6u1tT1CI50LjdLgoKCigqKsKp/6HNiGDQn+omyBQpZulHMds7bNsm0d/FWEcD0caNDLZu4uXiU3mrbYSajr+yOmM9MSuLcDLAcPYyHIFqLjr6IEqLcinKP5ZgQTa+LI0+pSN9xtLLpJMqwzBuA+YAa0zTtAzDaAaqtysvAmzTNHv3dtlUOvzOLdUB/P4g/r3wvtX2mOlnJmMWiQzPSL0HOn3O0o9iln4Us5lhJxNY0TAOX4DeMRe9rz9H0Ru/IMMaA7bsvtdn5fLExtfxBsoZqTmKV0tOoqKylGVFObhdO35RNzI0ii8rQ/FKM/qMpc52W6pPyaSSKsMwbgKWAyebpjk2cXgdkGUYxpET65wuAX6ZojIRERGRtGKNDjLy5tMMtzdgR5rJHOnCicWDo8fyQqyKUleUI73VDGeV4irasvtedUUR/x7MIcOtzSNE9iWT2VJ9AXANsAF41jAMgAbTNE8zDONcYK1hGJlMbHEOMDGStdfKRERERPZFtm1jD0VI9jQx3N7ASEcjzd7ZvDQ2i/6uNj7v/AVxK5PWRCGdLGDMV0ZhxVw+WRqiIuijsvjD2rpcJA04bNve9Vn7hxqgYWfT//YWDeWmH8Us/Shm6UcxSz+K2c7ZVgIr2k4ykaDLGaS5vY+5L3wTrxUDwLKh28rlydH5bMw5mMpgDrMKoaSslIriHAK5mTOyaZbilX4Us9TZbvpfLVsGcSZlqhtViIiIiMiE4dceZ6j5TezeFjJjnbhIYsbLuGvweAA+lDMHV04+jsIq8sprKS8L8PFgDpke/Qomsj/RJ1pERETkXdi2jT0cJdHTxFC4ntHOBsZGx/i/zFNo6RrizOSfKHX10ZosoJuDiOeW466u5eKKaipL/IQKj9XW5SIHACVVIiIiIoBtJUlG2xhsayScPZ+2SIzQxl9TO/wPADKAvqSflkSAFs8gVSV+OoIX4C4pYE4ol0N8Hj3zUuQApaRKREREDkhd0RgbX3mZ3PYX8Y12kJ/swU0SN3Bf9Az67WwO9oVo8eXjDFThL6+jLBTgsKIcjtHmESKyHSVVIiIist+ybRs71kekaSP9zZtx9beQOdzOb+3jeLE7m+Week7PeYseRxFhzxKSuRV4Sqq5pKKG0qCf3GxPqrsgImlASZWIiIjsF2zLYrCzhVbzTSLOIvrcRbh6NnJUx4NkAplAd9JPvVWIy+fiY6tns3zOSgL5FxLStD0R2QNKqkRERCTtJC2LcPcw7R0RApv/QOZQG9mjXWSQoBJ4NbaER0aXkJcB7uJjyaucTflcg9JAAXVeN0ekugMisl9RUiUiIiL7tNH+Xto2vkm8uxlXfyuZw+28OVbCzwYOwYHFDflv0JnMZcCzgKxQDeVz53FyRQ2nZ3pxu7TznojMPCVVIiIisk+wLYtYdxt9LZuIxUZozFrIG429nBq5h6BzCIBI0kebI4AjUM2FR82nriwXX9bRlGdm4HRqCp+IpIaSKhEREdnrrESc6HCS3sFRePMxMtv/QfZoJx7i5AJjST8/6/dSlJfJprIPEi8vIr9qFuWFhdS4NfokIvsWJVUiIiIyY2zbprsrwmDrRqxIM66+VjIGw2RtPt56AAAgAElEQVQlBrg6ehYWTtZkNVKbEacx8yA8wWp8ZXVkh6r4bl4OeTl69pOI7PuUVImIiMi0sG0Lq7+TvpbNDLRu5nl7Ca80x1g6+gIfyl4PQG8yh05nEXH/PM5dVkthIJ8C/6EE87Lw6tlPIpKmlFSJiIjIlNmJcQAGxqD19X/gN39PzkgHHuJ4gALbQWMsi/LKeVSWvY+erMPxFlcTKCykOisjtY0XEZlmSqpERETkPdmJcRIdGxkK1zPa2UDXQBveWCd/sI/lL9FKyly9nJEzRkPGPKz8CrJLaympncUVoQLtviciBwQlVSIiIgJsWf9kD3Yz3N7AQMtm2u0A/xivpK+ri4vj9+MGElY2TYkCelxLoLCcjy6dzeyKPKqKT8OToel7InJgUlIlIiJyALKTcZKxQXrimbR0DhJcdzf+WBgPW6b1+WwHraPzedWRTXlRLs8GPk5OaQ3FoWKOnVfC6PBYinsgIrLvUFIlIiJyAIi1mkQb3mS8qxH3QBh/PEJDopgfDpwIwDk5TlzeOSRyy/EU11BYWcf7Sgv4sM+7Q13+bI+SKhGR7UwqqTIM4zbgdKAGWGSa5msTxxuB0Yk/AFeZpvnIRNlhwFogC2gEzjFNs2umykRERA50tm1jD0WItdfT37yJ4f4of8s4lqaOQT48/nvme9rot7LosAPUZy4jUVLL+UfOo6rYT1nRMWS4NX1PRGR3THak6rfAD4CndlJ2xtYkayvDMBzAA8B5pmk+bRjGdcAtwAUzUTbVTouIiKQ7O5kgGQ0zkBGkuSsGr/+Jiu6n8NpbRpB8Ngwn86hnMRUlefQXnE5jSSHllaUs93v17CcRkWk0qaTKNM2nAQzDmGy9K4DRrdcBd7NlZOmCGSoTERHZrw12hhnYuI5EdxPugVZyxrpxYXFz32lELD9LM+Is8c8i7i/HW1JDoKqOyrIAt2Z7Ut10EZH93nSsqXpwYhTpaeAa0zT7gCqgaesJpmn2GIbhNAyjcCbKTNPsnYZ+iIiIpJRt29jDvcTaG4g2byLe3cTfnctZ151F1egGLvA/waCVSThZyIB3KVZ+OScvXUBFWZCK4qPJ9GiptIhIKuzpf32PMk2zxTAML/B94A7gnD1v1swJBHwpvX8w6E/p/WXqFLP0o5ilnwMxZnYywVhPmN4RaBrMoGPzRua98Z947S3LlHNt6LH8jLjrWDS7krmllUQLP0BpRRkLCrNT/vynAzFm6UzxSj+KWXrZo6TKNM2Wib/HDMO4C/j9RFEzUL31PMMwigDbNM1ewzCmvWwqbY5EhrAse6pdnRbBoJ/u7sGU3Ft2j2KWfhSz9HOgxGxkZJTelx9jrHNi973xbtwkeXRkEX8cORgvcc4urGHcV463uJpAzWyqK4JcmJnxjppsor3DKenDVgdKzPYXilf6UcxSx+l07NYgzG4nVYZh5ABu0zT7J6b/nQWsnyheB2QZhnHkxBqoS4BfzmCZiIhIytm2jTUcpa95EwPheqyeZjoSfh4aXkp3X4yb83+PEyftdoBG72Ks/ErKyudwXWU15cEcvBnvT3UXRERkN0x2S/XbgY8AIeDPhmFEgDXArw3DcAEu4A3gswCmaVqGYZwLrDUMI5OJ7c9nqkxERGRvs60kVl870e5u6q0yGtsHObj+XkJWBx6gCOhO+hl21lFV4uPwRSHa86+lvLyYg/OytPueiMh+xGHbqZkKlwI1QIOm/8lUKGbpRzFLP/tizF54s5PX6ns5qKaAJbOLyPK6SVoW/W+9QGzzKxBtIWe0EzdJBqxMru/7KG6Xg5MCTRT5M/CGaghUzqasLLBfbh6xL8ZM3p3ilX4Us9TZbvpfLVsGcSZl//svvYiIyG6KjSb41Z/W0bl5A1UZUcbqI7Q+2cd99hn0DCY4NesFDvFs3rL7nmchVn4FOWWz+FrdbMqLfSnfPEJERFJDSZWIiByQbMtipCdMf8tmGqigIWKRsflvnOZ6DiY23UpkBehxhpif48VXUEauv5ahYD4LQ368Ga7UdkBERPYZSqpEROSAYFk24fp6RtY/jLO/lYJENx5HEh/w7OBqNjuqWR6sY7C2lOJZBq7CChzeHAqAOaluvIiI7NOUVImIyH7FGhlgpL2BvpZNjHc14hls41l7CX+JVhGwI1zu/yc9ziIafQdjF1TgLa7hnJo6igN+nNo8QkREdoOSKhERSUu2bWH1dzLY1kB3zMXGRIiO9i7O7L4TgFygN5lDKwEsn49jDy6nusTAW30yC/yZqW28iIjsV5RUiYjIPs+2LZIWdERijL/wC1y9DfhHO8kgjgvoGK/iV0PHUpSXyXO5x5MZLKOgcjYVFSVU+Tys1AiUiIjMICVVIiKyT7FHhxhqq6eveSPx7iY8g20MJjL4Xv+JJJI2n/Vvwu2waPLMI5FXgbekhuKqOn4YyicnMwM4PNVdEBGRA4ySKhERSQnbtkj2d9O0+UXaNtfzD+8KWrqGOCLyG+Y5m8gHosls2iliILuCE1ZUUlnso7RkJaHCLFxObV8uIiL7BiVVIiIy4+xknJG4TWt3jOG3niM//Az+8U68xAEosh38rb+AwkAeLcEjSOQdS37lLMorQlTleFLcehERkfempEpERKaVNRajr2kjfS0bSfQ04x1qIy8R4db+NXRa+RziaeXIrHHaM+dj51cQqDPIDlbwveICMtwafRIRkfSjpEpERHaLbdskB3voa97IQGs9pqOWN6LZZHW/zrnePxME+qwsepxBwjmzWG3UUVxRQWXx4RT4vTgmNo8IBv10dw+mtjMiIiJ7QEmViIjskp1MYMXH6BiCtqZWgq8/iG+0Ay/jeIGA7aB1ZBVDeQcTqlvAhuwKCmpmU15eSqXHlermi4iIzCglVSIi8jaWZTHY+DoDLZuJdzeRMRjGH+/hqbH5/O/wcjJI8LncEcLeuVj5FWSFaglWz+b80gLcLk3fExGRA4+SKhGRA5Rt28QHeuht2shgaz09MXhi7CDC3YNck/lT8p1jDFiZtFsBNmcejLd0PhfVzqe6xE8ocLx23xMREZmgpEpE5ABgWwmGI120xrJo6RoiaP6KsthbZDFGDpBlQ2+yEss/nxXzSmjIOo/CUCmh8lKW53i2rX8SERGRHSmpEhHZz9i2TXfTZgY2v0qipwnvUBv5iR7GbDff6vsY4OCUXDfj2XOw8irJDNVQXDuHQ4oLWOlU8iQiIjJVu0yqDMO4DTgdqAEWmab52sTxucCPgQAQAT5pmubGVJSJiByIbNsmOdRLtHkTQ+F6rEgzD7uOw2yLsdrxPMdnvc6Q5aXbGaTLtwJnYRX/7/hFVIbyyPOtTnXzRURE9huTGan6LfAD4Kl3HL8buNM0zQcMwzgHWAusTlGZiMh+zbaSDLa3EB7x0BJNYjevY2nkT2QzSiaQCXQn/Yy5IyybW0VJcA29wbMoLS+lNEOTEkRERGbSLv9Pa5rm0wCGYWw7ZhhGMbAMOGHi0M+AOwzDCAKOvVlmmmb3FPssIrJPG4snaQ93MrLhOaxIC5nDbRQkI2Q4kjwyeCz/jFcxN8emwDeLRG453pJaCqtmUR4K8GVtXy4iIrLX7e7Xl5VA2DTNJIBpmknDMNomjjv2cpmSKhFJS7ZtM9bfS/smk4HwZpzRFtaPVfJ4tJwi5wDX5T/EsOWlxxUk6l+Go7CK99cu4vyKMvJyPKluvoiIiEw44OaEBAK+lN4/GPSn9P4ydYpZ+tkXY2ZbSaLhZsId/WyO+Whq7WV14w/JIUYRUAREyaUiv4azDjGoDvnw5B5DVVXFAfHsp30xZvLeFLP0onilH8UsvexuUtUClBuG4ZoYNXIBZRPHHXu5bEoikSEsy97Nbu+ZYNBPd/dgSu4tu0cxSz/7Qsws26a7b4T+fzxOsmsTnsE2ChLdZDiSNI6Xc9/QceT5PFT75+PJKyKvoo7SOfOoysuj6h11RXuHU9KHvWlfiJlMjWKWXhSv9KOYpY7T6ditQZjdSqpM0+wyDGM9cDbwwMTfr2xd37S3y0REUmV0oJfu+o0MtdVDbzMjo3HuGTiKsfEkl/sfp9TVR7eziF7fUpyF1RRUzOb7dbPJzfEAR6a6+SIiIjINJrOl+u3AR4AQ8GfDMCKmaS4ALgF+bBjGV4Eo8MntLtvbZSIiM8qykgx0hIk01/OWXUNL1xALOv/AUt4iH8gHei0fIxmlHLkwRFWJn4LCBRSEApRo9z0REZH9msO2UzMVLgVqgAZN/5OpUMzSz3TELGlZdERi9Gx6HVfzC3iHtkzf8zoSAFwbPZPM3AJW5XdRlR0jO1RHUe1sioIBHA49PHeq9DlLP4pZelG80o9iljrbTf+rBRone52+PhWRA9pwX5Tuhg0Mt9VDbws5I+3cP3QUzeP5HOrZxEdy/knEGaTZtwRnURX+8jpurptDTnZmqpsuIiIi+wglVSJyQLCsJL1trfQ2baJpNBezPxNX11uc4/wjASAA9Fk59HmKWTk/yPsr51BZtJyCogspduvZTyIiIvLulFSJyH5nPJ6kPRIj3NaDb8MfyBxqozC5ZfpeKbAudjDh7JXMKq5hs/tEsktrCdbOpSJQSKXDwaJUd0BERETSipIqEUlrg9Feuuo3EGtvgGgLvtEOXhsJ8b+xFTix+Eb+a/S7Cmn1L8ZVVE1uxSw+XDOLs7OzJmo4JKXtFxERkfSnpEpE0kLSsoiEW4k0baS3b4gXR7bswPcF54MUu2IA9NvZDHpDBIvruGTuAqpK/ATzVhM6AB6eKyIiIqmjpEpE9jmjY+OEIyO0dA3h2fhXCvvfpDDZTZYjTgXgSeYS8XyCeVX5dHrWEC/KJ1g7h4rCIu2YJCIiInudkioRSRnbtumP9k1M36uHaCu+0Q581iDf7PsYNg7O9LdT5LXoyF2Eq6iavIpZVNbO4uuZW6fvLUhpH0RERESUVInIXpFIJulqDdPbtJHxrkaeji9gc3ecI+0X+UDWPwEYtLPp9xTTnTuPzx0zj8rSQgJ579Ozn0RERGSfpqRKRKbd8PAo4e4hmntGiLW8xeyexwlY3fgd4/gBy4YX3cUsmT2bUO6xRLJWEaybS1lBgLJUN15ERERkipRUichus22bSHSQ7k1vEmtvwNHXgn+0gyKiPDJ8BK+M12JkD3JQTpIe/0JcRVXkVc6iqHo2n9k2fU9EREQkvSmpEpFJGY8n6WwN09u8kfGuJjbH8ni6N4g33scN+b8BYJhM+r0h2nPnccLKFZwzax75Pg8Ox0dS3HoRERGRmaOkSkR2MDgUIxzuprHPQUvnIId1/Iyg1U2+c4x8tkzfG3YfzKEHLaQqWEefo5hg7RxK8goJaf2TiIiIHGCUVIkcwCzbpjs6QtemNxhp24SzrxX/aAdBRy+DiRJ+OXgCBX4vh2ZnE80+iMHglt33AlWzODYze7uaKlLWBxEREZFUU1IlcoAYHUvQHm4j2rSJ8e5Gxgf7+VnfMsbiST7rf5T5GR3EyGQgs4SuPINA2Ry+v+hwcrM9wBGpbr6IiIjIPktJlch+aGBwhPaGejYP59DcNURp51McYv2DIucoRRPnRB0FHLXog1SGcinJqsRbnI8vt4gSTd8TERERmRIlVSJpzLJtevpGaG9sZqz5HzgmHp5b7OilzJFkbd/puP0BKvIKGXTPY7SohrzKWRRUzcLvzebjqe6AiIiIyH5gj5MqwzAagdGJPwBXmab5iGEYhwFrgSygETjHNM2uiWumvUxkfzcWT9LW0k5f80bGuhrxDIZ5aHABTaN5LPM08CnfU4zgZSCzhJ68Q8ksqeXGhSvx5ealuukiIiIi+7XpGqk6wzTN17b+YBiGA3gAOM80zacNw7gOuAW4YCbKpqkPIvuM/oER2poaCfcl2NTrYLSziTOth942fW/Akcthtcs5ttqgunAB3tw1+PKCFGv6noiIiMheNVPT/1YAo6ZpPj3x891sGVm6YIbKRNJS0rLoiMRo7ejD2vwMzr4t0/dKHFEqHAleHVnM5ozDmFUUZMg2GC2qnpi+Nxt/Zg7lqe6AiIiIiExbUvXgxEjS08A1QBXQtLXQNM0ewzCchmEUzkSZaZq909QPkRkTG03Q1tpOtGUT411NeAbDNMR8/Cm2CAcWtxY8hu1wMpBZQm/eIWSW1HDSnIV8NLg1dTo0pe0XERERkZ2bjqTqKNM0WwzD8ALfB+4A/nca6p0RgYAvpfcPBv0pvb9M3VRjZts2nb3DtGzcTEdrO68OFVIf7ufs+K+pzeimZOK8IaefrIrFzF++jLryPEqzj8aTW4hD0/f2mD5n6UcxSz+KWXpRvNKPYpZe9jipMk2zZeLvMcMw7gJ+D/wAqN56jmEYRYBtmmavYRjN0102lfZGIkNYlr07Xd1jwaCf7u7BlNxbds+uYhZPWLT1DNPcNchYwytkR0z8Yx2EnL0EHAmcyWx+5/okVcU+kq7lRHwe8itnk1teiz/LT+l2dQ2Me6BnaOY7tZ/T5yz9KGbpRzFLL4pX+lHMUsfpdOzWIMweJVWGYeQAbtM0+yem/50FrAfWAVmGYRw5sQbqEuCXE5fNRJnIjBuIjRNu7aCveTPx7kY8Q23kxSN8Z+AkLJx81PcqhmczA9kh+vMOYTxUQ7B6DjeX1k3UsDCl7RcRERGRmbGnI1UlwK8Nw3ABLuAN4LOmaVqGYZwLrDUMI5OJ7c8BZqJMZDrFE0k6IsN0t7aS6G3mxWghm7vGWRJfz0dyXqJi4rwhh4+RvFI+e9QsyspLCOYcjtPjIeBwprT9IiIiIrJ3OWw7NVPhUqAGaND0P9neyFiCTeF+Nrb2M9DRQlXfixTGuyh3R8l0xAH4pXMNVsl85vsHqSZMfuUscsrrcGZqrvO+SJ+z9KOYpR/FLL0oXulHMUud7ab/1bJlEGdSZmpLdZF9im3b9A+P09raxUB4M8nuJjxDbeSPd/Hk6AJeic9iccEwSx0mw7khYvkrsEpqqV28iAvcARxuT6q7ICIiIiL7KCVVst+xbJuu3hjtzc0MhOtp7YcXI7kkRwb5ZsG/luENO3KI5ZXywUPmcdHSVXjcDuAkCrebvpcT9BPTN0UiIiIi8h6UVElaiyeShLuHaO4apqlzkMrWR8gfbaPUGWG2c8v0vWzHbEZnfYSqkhr6Y3HyK2rILq3Fn5Wb4taLiIiIyP5ASZWkhURyy9bl7R09jLQ3QLQVz1CYgng3w7aX+wdPINPj4tDcdjJzXMTyDsYuraWwajYrglUc4vZO1FSZ0n6IiIiIyP5HSZXsc0bHE7R0DtLRGmaorZ7xaCd/jM4mkbS52PcXlnvCAIw4shnOK8UbnM0thxxGUX4WDo7Ww3NFREREZK9SUiUpNTA8TnN7H01dwzR3DZPTtZ4F8dcod/USco4DYOEgsewIyksLqXEUkJHlwFNcgz87P8WtFxERERFRUiV7iW3bdPeP0hKO0N+ykUR3E96hNoJ2D+WuPn7cfyoOf5Dj8hyEEi4ShctIltbiK6/DFaji9Iyt0/dCKe2HiIiIiMg7KamSaRdPWLR1D9HR2sZQWz12bzPP9JfSNOpnSUYTF/ifAGA0I4vRnDJGAkv42vLD8AVKgMNT23gRERERkSlSUiVTYtk2bd3DOJwOsr1b3j7hzn7CnX00ReL0dbazevwvlLt6Ocg5tu26jMqTsWcZ1OTPw20txVtSgy87X+ufRERERCTtKamSXbJtm3D3MM+/2cnfX2/HH2ulwtVLuStKubuXUlcfG0bnY7pXURvMIzQK8fzFjIVqyauYjbuokmM8WdvVWJayvoiIiIiITDclVbJTA0NjbNjYROfmDYx3NdI75uK58XksrC3gvMHHcSVHSbiyGM4uZaxwESfMWcZH6hZPXH1IStsuIiIiIrI3KakSxsbidITbaBxw09I1RE3z76hL1GM4RzEA3NBfZHDGyUeQl+Mh0fFlnL5CHDmFFGj6noiIiIgc4JRUHWD6h8fp2LyRWMub2L0tZI+0E7QjZNgZ/LjvTDI9bioKchjKnYejfBZFNXNwB6vwe7K31eEOzUlhD0RERERE9i1KqvZTlmXT1d5BT+NGRjsbcfWH+enQKiLDFh/Oeon3Zb3BiO2hL6OYDv8KMopruGXRYRQVZON0HJPq5ouIiIiIpA0lVfuBkbFxOhqaaBp00hSJ42lbz1HjT5DnHCFn4px+/Cyv9FBYXkmtvwYC2QSLSijW9D0RERERkT2ipCqN2LZNdHCMcGsnY/UvQW8zOSMdBIlQ5Ejwm8HVNLpqWB7IZSBrDsOBSvzlswjWzsGf7eesVHdARERERGQ/lHZJlWEYc4EfAwEgAnzSNM2NqW3V9EskLTrbOok0bWK0swF3f5gXhit4YbickKuPr+Q9xKjtoc9TTLd/GZ7iGs6fezAFJaGJZz+dlOouiIiIiIgcENIuqQLuBu40TfMBwzDOAdYCq1Pcpj0yPDJOR1Mz7T0DbBjIor0jyifHHyDfGSN34pwBfMwPVTOnbi5Vxdm4slZRFAgR1PQ9EREREZGUSqukyjCMYmAZcMLEoZ8BdxiGETRNszt1LZsa27YJv/AX+ptMHH0tBO0IxY447eNVrE+eQHWJj76cBYwWFOOvmEVRzWz82bmUp7rhIiIiIiKyg7RKqoBKIGyaZhLANM2kYRhtE8fTJql65tUO8tb9iZCrj6grSCR/KZ6SGhbUzOcHFbUT0/cOTnUzRURERERkEtItqdpjgYAvpfcPBv2sXunBzL+CynmVHJSTmdL2yK4Fg/5UN0GmSDFLP4pZ+lHM0ovilX4Us/SSbklVC1BuGIZrYpTKBZRNHJ+USGQIy7JnrIHvJRj00909CEBtVYjRWJzRWDwlbZHJ2T5mkh4Us/SjmKUfxSy9KF7pRzFLHafTsVuDMM4ZaMuMMU2zC1gPnD1x6GzglXRaTyUiIiIiIvuXdBupArgE+LFhGF8FosAnU9weERERERE5gKVdUmWa5lvAylS3Q0REROT/s3fn8XHV9f7HX2cmmayTZSYz2Zq1aU5XurIvZV+UCiqKICCXooIs1yvXDRTRHyggKigIXHFBuC6oXFwAkYuyyiKUHXqaNvs6yWSyZzKZmfP7oyG3QClJ02Yyzfv5ePRR5nzPnPM5+WRI3j3nfI+ICCRhqJoBJ2y/TjKREr1/mT71LPmoZ8lHPUs+6llyUb+Sj3qWGDt83Z3TeZ9h24mZtCEBDgOeSHQRIiIiIiIy5x0OPDnVledTqEoD9gc6gFiCaxERERERkbnHCRQD/wLGpvqm+RSqRERERERE9rikmlJdRERERERkrlGoEhERERERmQGFKhERERERkRlQqBIREREREZkBhSoREREREZEZUKgSERERERGZAYUqERERERGRGVCoEhERERERmQGFKhERERERkRlQqBIREREREZkBhSoREREREZEZUKgSERERERGZAYUqERERERGRGVCoEhERERERmQGFKhERERERkRlQqBIREREREZkBhSoREREREZEZUKgSERERERGZAYUqERERERGRGVCoEhERERERmQGFKhERERERkRlQqBIREREREZkBhSoREREREZEZUKgSERERERGZAYUqERERERGRGVCoEhERERERmYGURBcwi9KA/YEOIJbgWkREREREZO5xAsXAv4Cxqb5pPoWq/YEnEl2EiIiIiIjMeYcDT0515fkUqjoAQqFh4nE7IQV4vdkEg0MJ2bfsHvUs+ahnyUc9Sz7qWXJRv5KPepY4DodBfn4WTGSHqZpPoSoGEI/bCQtVb+1fkot6lnzUs+SjniUf9Sy5qF/JRz1LuGndLqSJKkRERERERGZAoUpERERERGQGFKpERERERERmYD7dU7VTsViUUKibaDSy1/cVCDiIx+N7fT+y58ylnjkcTjIyssnOzsUwjESXIyIiIjJtwwMDBOothtobaB9O4enBBUSicb618QBSnMl7vmfeh6pQqJv09Eyysor2+i+qKSkOotG58Qu6TM1c6Zlt28RiUQYH+wiFuvF4/IkuSUREROQ9xeNx+kMDNIeiNHcNUrrtXgpGG8k3BikACoChWCU5nmqWVObjcCT3PxjP+1AVjUZmJVCJzIRhGKSkpJKX56WrqzXR5YiIiIhMisdtAi1NhBo2E+luwjXQSn60m/5YOjcNfAiA8/Ij9KcXE8o7gMyiKnzVtRzs83NwgmvfU+Z9qAIUqCRpGIYD0BSrIiIikhhjoyN01W9joG0r48E2HogcSEvPMB93Pc7+afVEbQdBw0tXVi1GQSVfWbaGMn82GWlHJ7r0vUqhSkRERERE3mUw1EtLKEZz9yhG03PU9j2Fx+4j37DJB0ZtF9lZq1i/spQc94cY8GTgr6wiP9WV6NJnnULVHHPaaRtwuVykprqIx2N86lMbOfbYE9i06XkuvfQCzjjjbC666N8n17/44s/w0kub+NvfHiczM3On29y06XluueUmfvrTuwA47LB1HHjgIXzvez+cXOeww9ZNbmPTpue59dYfMT4+zvh4BK+3gBtv/DFXXPElOjraAdi6dQsLF9ZgGA48Hg/f//7N73lMzz77NL/4xU8IhUKkpKRQUlLKZz97MQsX1nDaaRsYH49w770P4HQ6Abj//j/xne98i//4jy/y0Y+ezgMP/Jkf/vB7FBWVEI2OU1FRyZe//DVycnK5+OLPcMYZZ3PooYfv8usaDoe56KJPc/PN/0VGRsbUmgF0d3dz5ZWX86Mf3b7L9SKRCBdeuJGbbrqV7OzsKW9fREREJNFs2yYYCBKse4WxQAPO/jZyI13kGsPc0/8BmmMFHJgTYUFGPgM5K0gvqsRbWUtBcQn/7kjeySX2JIWqOejqq6+jurqGLVs2c8EFG1m37kAAyssreOKJR7nggotxOp20t7cxNhberX00Nzfy4osvsHr12rctj0ajXHHFl/jRj26npmYRAFu2bMYwDL7znRsm1zvssHXceuvP3jPIvf/rgNsAACAASURBVOW5557h2mv/H9/5zg0sXrx0cnvBYA8LF9YA4PUW8NxzT3PwwYcB8OCDf8E0l7xtO+vWHcDVV19PPB7nyiu/wp13/pRLLvnClI/397//DUceefS0AhWAz+d730AF4HK5OOGEk/jtb/+bjRs/O619iIiIiMyW8fEIgcZ6+pu3Mt7dzMvj5TzXk0NJrJVLcv5G3DboNfIIZZTTm1/GJ45YR2n5AtyZ8+/s03QoVL3DU6928OQrHXtl2+tXl3DQ0qIpr19bu5jMzEw6OtoAyMjIpLKyajKAPPjgXzjxxA/y5ptvTLuW8877DLfffgu33fazty0fGRkhHB7F4/G8rY7d9fOf/4RPfWrjZKDa2fZOOmkDDzzwFw4++LDJoFhdvXCn23M4HKxZsz9PP/3ktOr405/+hx/+8LbJ16edtoHjjz+JF174F93dAS644BL6+np5+OG/MjAwwOWXf4OVK1fT3t7Ov/3bJ7n//keA7WHyM5/5HI8//ij9/f1cdNGlHHnkMQAce+wJbNx4tkKViIiIzAkjQ0N0dIVo7DPoau/kgI7f4LV7yTHi5AAR20lDaiYHLF1EpbeMUMYK/NU1VE3zH6FFoWpO27TpeSKRCAsWlFNXZwHwgQ9s4I9/vJeDDjqURx75G7fe+lN+8IPvTnvb69cfzb333sMTTzzK4YcfObk8JyeHDRtO5ROf+AirVq1hxYqVHH/8iRQWTj0M7mjLls184Qtf2uU6a9as43/+53cMDAxMBsXNm9/c6bqRSIQnn3ycxYuX7HR8Z7q6OgmHwxQVFb9t+fj4OLff/nPefPN1Lrnks1x44aX85Ce/5JFHHua2227m1lt/utPtZWVlcccdv+SVV17iyiu/OhmqPB4vKSmpNDU1UlFROeX6RERERGaqf2iMwOv/YrSzHqOvlZxwJx5jgLpwLfeMHIQ7w8l+7mya3YtI9VfgKV+Er6ySU1OciS59n6BQ9Q6Hrijm0BXF77/ibpjqM4++9rUv43KlkZWVxTXXXIfb7Z4cW7NmHd/73rU8/vijVFcvJDc3b7fr+exnL+amm27g0EOPeNvyL3zhy5x++ifZtOl5nnnmKe6+++fcccddlJWV7/a+dsUw4Oijj+ORR/42GRTfGaqef/45zj33TABWrFjJ2Wf/25S3390dID/f867lxxxzHLD9zFk4HOaYY44HYPHiJbS1vfe05ccccwIAy5atoKenm7GxMdLS0gDwer0EAl0KVSIiIrJXxOIxelqa6W2qI9LVSGg0zn39yxkYjvD13Hupcg4Rst0MpBXRn7eWygVL+Z65H3nZLgxjfaLL32cpVM1Bb91TtTOGYXD00cdx/fVXc/nlV81oP+vWHYDHU8BDDz3wrrHS0gWUli5gw4ZTueyyS3nqqcf5xCfOmvY+amsX88Ybr7NokbnL9U466WQ++9lzWbVqzU6D4lv3VO2OtLQ0IpGxdy13ubZfG/zWBBlvvXY4HMRi0ffc3jvfF4vFJscikf8LWCIiIiIzEQmH6WppomHETXPXINUtf6Y2uplMI0omELUd4FjAiuojKPe7iWddhGNBKeU5OYkufd5RqEpCp5zyETIyMjjwwJk/Lu3CCy/m61//6uTrkZERXnvtFfbf/0AMw2BwcJCOjjaKi0t3a/uf+tRGrr/+GhYvXoppbr+X6o03XqO/v5+DDz50cr3S0gV8+tOfY+nS5TM7oJ0oL68gGAwSiUQmA9HeEIvFaG9ve89ALCIiIvJehsPjtG/dQrjxFZz9baQPt+OxQ2Ti4JehM3C5UvHke2jLXUVKQQW5ZTX4K6tZmZbGykQXLwpVycjn8/PJT35qj2xre9gxJyfDAJt7772HH/zgelyuNGKxGMcffxLr1x+1W9s/6KBD+OIXv8r3v38d/f39E1Oql3DBBRe/a91TTvnIbu3j29++Cpfr/84Offe7N03OXAiQlpbOmjVrefHFF/ZIEH0vr776MkuXLteU6iIiIvKe4vE4oc5Ogo0W4c5GnP2t3DN6CC0DBselv8rJmS8yYGfS5yqkKWcpaf5Kvr30AAo8bhy6fG/OMmzbTnQNs6USaAgGh4jH/++YOzubKCqqmJUCpnpPlex5r776Mr/61S/5zne+N633TadnV111BR/84IfYf/8Dd6fEKZvN79lk5PO56e4eTHQZMg3qWfJRz5KL+pU40eg43U0NtAyk0BCKYbe9xlEjfyXT2H5bQtyGkJHHM55TyFlQTWU+lBZkUbO4Sj1LEIfDwOvNBqgCGqf6Pp2pknlhxYqVHHLI4YyOjk77WVVTEYlEWLVq9V4PVCIiIjI3jUVitLYHGN38T+LBZjJGOvDGe8g24rwwdCgvxhaxoiCLzuwlOL1luBfUUFi1iMqsTCoTXbzM2LRClWma3wCuAlZYlvWaaZoHAbcDGWxPcmdZlhWYWHdWxwQ2bjz7bZMmACxbtpwvfvHyvbrfujqLa6755ruWf/SjH2fDhlP36r6nY2/W4nK5OPXU0/ba9kVERGTuGAj2EKi3GOlowAi18PpYMQ/3lpNtjHJ1/p8ZsdPoTfHRkrs/qb5KTlm4gs8sKMHpcADHJ7p82QumHKpM01wDHAQ0T7w2gLuBcy3LetI0za8B1wLnzfbYnvhC7At++tO7ErLfRYtMfvGLXyVk3yIiIiJ7Syweo7ethc6uPupG3DR3DvLh0E/xGIMUTqwTst34swr50NIqyguziWSvxldYRKHDkdDaZXZNKVSZppkG3AKcCfxjYvE6IGxZ1pMTr29j+9mj8xIwJiIiIiKy26KxOG3dw/S/+U/sri2kD7XjifWQbowzPl7IA0MnUlKQSYd7OQM5uWSVLsRfvYjy3Dz2zpM8JZlM9UzVt4C7LctqMM3J5w2VA01vvbAsq8c0TYdpmp7ZHrMsq3eaxy0iIiIi89TwwACBeouhtgbsUDOx0SF+HFpPLG7zmezHqUntIuj00Z6zAmdBJb7yRdy6sJbUFCeg+6fl3d43VJmmeTCwP/CVvV/O3jcxm8ekQMBBSsrsnZ6dzX3JnjHXeuZwOPD53IkuY07T1yf5qGfJRz1LLvO1X/F4nGBHB63Wm2yJlrKtfZDKtr9yEC9TABQAg3YG/WlFfHh9NdWl+VT5DqKouIAlTmdCa5+vPUtWUzlTtR5YDLx1lmoB8BDwQ2ByXmfTNAsA27KsXtM0m2dzbDoH/M4p1ePx+KxNcz6V6blPO20DLpdr8rlLa9as5dJLL+OOO26jqqqaY445nk2bnicajXLAAQdNab8333wjjz32dzo62vnlL3+z04fT/uxn/8XPfvZf7zkeDof59re/iWW9idPp5KKLPs+hhx4+Of773/+Ge+/9HSkpKTidTn7+83ffY2XbNoZhcM01V3HFFVdNvp4tdXUW3//+dWzZYnHwwYdy9dXXT47deuuPePbZpydfNzc3cuGFl3LGGWcyMDDE9753LXV1FtFolJNPPpUzzzwb2P6w5Pca21FzcxPf/e63CQZ7cDqdLFmyjMsu+zJpaekA3HXXz3nooQdxOp1kZmbyxS9eTnX1wp0eRzwe1zSru6Cpg5OPepZ81LPkMl/6FY/bdPaO0FVv4Wj6F66BVvKj3WQZYXKBB/s+jDPXjy+vhoY0LxnF1fiqaynx+SkBluywrVDvSIKOYrv50rO5aIcp1aflfUOVZVnXsn1CCABM02wETgbeAD5jmuZhE/c5XQDcM7HaC0DGLI7tU66++rp3BZvzz79g8r9ffPEFRkdHpxyqDj/8SD72sU9w0UWf3um4ZW3m9ddfo7Cw6D238etf30VmZia//e19tLQ0c9FFn+Y3v/kfMjMzeeyxv/OPfzzCHXf8kszMLILBnp1u4667fk5mZiaxWIy//e1BNm9+g0svvWxKx/B+TjttA7///Z93uU5+voeLL/4CdXUWzz//7NvGLrzwEi688BIAQqEQp512Mkcffexk3ampqdx5528Ih8NccMF57LffKpYvX7HLsR2lpqZyySX/QW3tYuLxOFdddQW//vXdnHvu+dTVWdx33x+4++7fkZGRwe9+9xt+/OObuOGGH+6Rr42IiMi+Jjw6QqB+KwOt24gFm8kYbucPQ2upGytgZWoT52Q/S9Dw0pVVi8NbTnZpNd+sWUJm5p5/rIoIzOA5VZZlxU3TPBu43TTNdCamOE/E2HxwzTVXsXjxElatWssf/3gv8Xic559/jmOOOZ6zzz53l+9duXLVe45FIhG+//3r+MY3rubSSy94z/UeeeRhvva1qwAoKytn8eIlPPPMPzn66GP5zW/u5vzzLyQzMwsAr7dgp9s455zzuO++P/C3vz1IYWHRTgPVpk3Pc/3113DHHXeRnZ3NNddchcfjnQw8M1FQ4KOgwEdTU8Mu13vooftZt+6AyePYunULJ520AcMwyMjIYPXqNTz88IMsX75il2M7Ki4uobi4BNh++d6SJct2qMMgGo0SDofJyMhgeHgIn68QERERgcFQL4F6i9YhF1Z/OmNdDZwT/wP5hk0+MGq76HX6WFPj5YjKJZT71pDt/ST5qa5Ely7zyLRDlWVZlTv89z+BFe+x3qyO7Uu+9rUvT17+d+GFl3DggQdPji1cWMMpp3yE0dFRLr7485PL//M/L+X88y9g8eKl09rXHXfcxvHHn0RJSeku1+vq6qSwsHjytd9fRCDQCUBDQwOvv/4qP/nJrYyPj3PKKR/hQx/68Lu2cdddvyA9PZ3jjz+Jqqpqbr75xrcdA8CaNes48cQPcu213+LQQ4+gpaWZL3/5a9M6ppl64IE/c/75F06+Ns0lPProIxxxxJEMDQ3x7LNPU15e8b5j72VsLMz99/+JCy64CIBFi2o5/fRP8rGPbSA72012tptbbvmvvXeAIiIic5Bt2/T0h2np6MP5xgOkDLSRG+ki1xjGD7w8uhwr5WAW+otpdBxOelEl3spaCopL8DscLE70Aci8tttnqvZlI3/+zruWpVQfgGvZMdjRMUYf/P67xlNrDyPVPJx4eJDwwze/e3zp0aSYB79r+c7s7PK/97M7l4q99torbN78xozPAsXjcQKBLn784zvo7+/jwgs3Ul5ewapVa9623llnfWrynqrjjz+J4447cafbO+ec8/j85z/HLbfcyB133E1Kys6/TXd82HFPTzfnnnsmAIWFhVx33Q9261jeeOM1QqEQhxxy2A51n8stt9zExo1nk5eXz+rVa+nv73vfsZ2JRqN84xuXs3btOg47bD0AnZ0dPPnkY/z2t/fh9Rbwq1/9kmuuuYrrr79xt45BRERkrhsfjxBorKe/eSvj3U2kDbXTOJbLPYPrAJtr8p4j7MgklFFOb34ZmSXVHFdt8pH8/IktrE1k+SLvolA1j7344iaamhr52Mc+BEB3d4AvfOESLr/8G++6X6uwsIiurg7yJ/5nFgh0smbNuomxQo499gQcDgf5+R7WrTuQN954/V2h6q1JKa644qq3vX6noaEhuro6SU11MTDQR1HRzu/12vFhx6edtmGPPID4/vv/xAknfOBtQS49PZ3LLvvy5OsbbriWioqq9x17p1gsxre+9XXc7hw+//kvTi7/+9//l+rqmsnLDU888YP87Gc6UyUiIvuGkcFBAg11BLuDvDy2gJauIT4R/hUlzhA5QMR2EnQUkOfzcc4hJuWFbvK9h5Gelpbo0kWmTKFqJzI3fPU9x4yUtF2OO9LduxzfE7Kysujp6Z7xds4++9y33Y912mkbuP76H+z0LNlRRx3DH/94L4sXL6WlpZk333yDq666BoDjjjuRZ599mlWr1jA6Osorr7zI+vVH7nZd3/nONzn55FNZsmQpV111xeQEGHvb2FiYRx75G7fe+rO3LR8eHiIlJYW0tHS2bq3jiSf+wU9/evf7ju0oHo/z7W9fhcPh4Ctf+frbAmVJSQkPPXQ/o6OjZGRk8PTTT1FVtfOZ/0REROayvqExmruGGLX+SWb3q+SEO/EYA3gBVzydO8fOpKLQTcCznlheOp7yRfjKKvGmJHb6cpGZUqhKQkcccRRXXPFFzj33zMmJKnZ1T9WNN36Xxx77B729QT7/+YvIycnl7rvff+LEc889kxtuuImCAh9nnnkO11xzFaeffioOh4MvfenyyaBz+ulncv3113DWWR8H4MQTP8D++09tZsJ3uueeXzE2NjZ5qeBRRx3Lddddwze/+e3d2t6OOjra+dznziccDhOJjPHhD3+AjRs/w8knnwrAY4/9g/LySqqqqt/2vvb2Nr7+9a+SkuLE5XJx5ZVXU1Dge9+xJ598jCeffJyvfOXrPPPMP3nooQeprl7Ixo3bp1xfsWIll132ZdavP5o33niNjRvPIjXVhdvt5vLLvzHj4xUREdlbYvEYPS3N9DbVEelqJHWwjZzxbr4V+jBRnJyS8Sar0zsYSCuiP28t6UVV+KpqudHnx+FwAKsTfQgie5Rh2/b7r7VvqAQa3vmcqs7OJoqKdj2xwJ4yledUydwyF3s2m9+zyUjP9kg+6lnyUc+Sy0z7FQmH6WrYPn35a9FytvXEqQw9w4b0fwEQtR0EDQ8jmUUEF55MSWkhC3xZZGVo9r3dpc9Y4uzwnKoqts82PiU6UyUiIiIiAAyNRmgJDNPdVE9+0yNkjXbisUPkGTZ5wCOjx+LwLiFr4Wqa08rJLavBX7mQ/DQFKJnfFKpERERE5pl4PE6os4Ng4xbCnY04+1vJiXTx4MgKnhlbRKGjj4tzG+lPLaQpdylp/iq8lTVcVLoAp0P3P4m8k0KViIiIyD4sOj5O+7Yt9DVvo2PYwaZBP91dPXw9878pBuI2hIw8+tIXsLx6EYfUrKTcl01O9kfY9VMsReQtClUiIiIi+4hweJzW4AjNXYPkbb4P93Az3ngPbiOOG+gdryCcfTJLzTIanB8ht6ScwqpaKrMyE126SFJTqGL7E7zf65lJInOJbccBfa+KiAgMBHsI1FuMdDRghFpwj3XRP57KTYMnAfC53E6iLhcd3oMw8svIL6/hoIpKDktJndjC4sQVL7KPmfehKiXFxfDwAFlZOQpWMmfZtk0sFmVwMITLlZ7ockREZBbF4jF621oINm5lpKedx6Mrae4a5KM8xH6uFgBCtpsBl5+4v4pLlq2g3O/Gk3MUhmFoJjmRWTDvQ1V+vo9QqJuhob69vi+Hw0E8Prem55Zdm0s9czicZGRkk52dm+hSRERkLxmPjNEeDNPcPcx4/b8o7XkGT6yHdGOcUiBmGzzgrGJJhRcj+0S681z4qxdRnpuX6NJF5rV5H6qczhQKCopnZV/6l6Lko56JiMjeMjw4RGDbmwy1NWCHmska6cBr93Jb/wa64nkclBGkNAvac1bgLKgkt6yawsoavpauKxZE5pp5H6pERERE9qZ4PE5/d4Duhi2EO+p5bbyMl3sz8A9v5TPuv1MADNoZ9KX6aXKbfOyApZRUlOPPPwqHbk0QSQoKVSIiIiJ7SCwao6u7j+bQOF1tHdQ230t+tJssI8xb18W8aR9ORdGBLCzYn07XQvxViyjx+SlJaOUiMhMKVSIiIiK7IRyJEqh7k4HWrcSCzWQMt+ON9/B82OS+0XWkO+MszRsjkFWL4S3HXVqNv7qW07OzE126iOxhClUiIiIi72Mw1EtX/RZG2+vpGYrxyHAtHcEh/l/u76hwhBm1XfSm+GjNWUtFyXK+aa6h2JtJivPYRJcuIrNAoUpERERkQjweJ9gVoHnASXPXIMUNf2ZBeAu5xjCFk+uU4C9YyTrTRyjtPFylJRQUl+B3OBJau4gkjkKViIiIzEvRWJyupib6G15nvLuJtKF2PLFubBtu6TsdwzD4eL6DrIxyevPLyCqpxl+1iJUeLysnt1KdwCMQkblCoUpERET2eSNDQ3Rt28JQez12sIk/Rw6mKTjGia4XOC7jNSK2k6CjgA73MpwF5Xxt6RoWFObgSj060aWLSBJQqBIREZF9Sl93gJZQlKaeCNGWV1jd9794jAEKgAJg2E6nKHsli9ZVUpFbzHD+h/GVVeJNcSa6dBFJUlMKVaZp3gdUAXFgCLjEsqyXTNOsBe4EvEAQOMeyrLqJ98zqmIiIiMwvcdsmEOglZL1ApKuR1ME28sYDuI1RHhlczyvjFeyXZ1OTXkh/3lrSi6rwVdXi9/nZqPufRGQPmuqZqk9ZltUPYJrmKcDPgDXAbcAtlmXdbZrmWcDtwFvnyWd7TERERPZRkXCYroatDLRuI9rTxOaxQh4NFuGOhfh63n1EbQe9hoeerIUE88s5eeFqPlNZRWZ6CnBKossXkX3clELVW4FqQi4QN03Tz/ZgddzE8l8DN5um6QOM2RyzLKt76ocsIiIic9lQfx/t7T00DLpo6eznqM5f4LV7yTNs8oCwnUqnK53D91tNmX8hA+m1+CpryE9zJbp0EZmnpnxPlWmadwDHsz3cnAiUAW2WZcUALMuKmabZPrHcmOWxKYcqrzexD9zz+dwJ3b9Mn3qWfNSz5KOeJZ890TPbtunuG6X1xacZatqMHWwmJ9xJrjFEb6SU3w4dgycnnTXuUsZyl5NdWkPRosVUVFaw1Kn7n6ZDn7Hko54llymHKsuyzgcwTfNs4LvA1/dWUXtTMDhEPG4nZN8+n5vu7sGE7Ft2j3qWfNSz5KOeJZ/d6Vk0Ok6gqYG+5m2MBxoJjwxzd/8BDIejXOJ+iOqULnqNPELpC+jNX4B3gcmN5kpyslzAIW/bVm/vyB48mn2fPmPJRz1LHIfD2K2TMNOe/c+yrLtM0/wvoBUoNU3TOXHWyAmUAC1sP6s0m2MiIiIyR4SHR+hsqKM+4qW5a4iytr+xOvYSbiOOGxi3nQQcftbW+qgocpPrriKtpJCqrMxEly4islveN1SZppkN5FuW1TLxegPQCwSAl4AzgLsn/n7xrfubTNOc1TERERGZfQPDEdrrtxJp2IQRasE91kW+3YfXgB+EPkY8zU2Op5CW9P1J9VWSX16Dr6IST0oqixNdvIjIHjKVM1VZwO9M08wCYmwPVBssy7JN07wAuNM0zSuBEHDODu+b7TERERHZS2LxGMG2Fnobt/JGbzMEW/jj6GrqBrPZ37WNs7KfImS7GXD56c9bRXpRJVfWrsHjcWMYRqLLFxHZqwzbTsz9RQlQCTToniqZDvUs+ahnyUc9m3siY2MEGrfR2g/b+lMIt9fxofB9pBvjAMRsg14jn1c8J5BZvoRyr4sFBelk5+YluHLZGX3Gko96ljg73FNVBTRO9X3TvqdKRERE9h0j4SitHUHG3ngUu7eFrNEOvHYvuYbNEyOreTK2ikW+XNpzVpBSUEFO2UKWrVtF3lCU6kQXLyIyRyhUiYiIzAPxeJy+QBc9jXWMdtTj7G+jPpzLfX1LcRDn+vyHCJNGX6qfJreJq7CSo6uXcXppCQ7DAA6b3FZaRgYM6V/RRUTeolAlIiKyj4lFY3Q3NxLoDLB5zEdz1yAb+u6ixNFL8cQ6QTsXT3YBH11ZTZnfjTNvNSVeLyUJrVxEJDkpVImIiCSxsfEYrd1DDGx+Dmfn66QPd+CN95BlxHDHsvnfoY9SWpBNIH81kZwM3KXV+KtrqczOpjLRxYuI7CMUqkRERJLEYG+QroY6Rtq3YYRaSQ93c13oROK2g49nPseatCZ6U3y05qwlxVdBXlkNP65eRIrTAeyf6PJFRPZZClUiIiJzTDwep7e9jWDjFqzxEhp6IizoeYrjnc9ROLFOv51Fv6uQUw8sprjYT4V3LR6PG7/DkdDaRUTmI4UqERGRBIrG4rT3DNPV1ICr/gnShtrxxLrJMCKUAH8YPJ6R3IWU+k0aXHlklVTjr1rEAo+XBcCyRB+AiIgoVImIiMyWkaEhurZtYahtG3ZvM5kjHfx1eBmbxiqocHZzcc6LBB1eOtzLcBaUk1Naw2XVi0jLSE906SIisgsKVSIiIntBX3eA7nqL9kGD1wdz6Q108e/G3RQABcCwnU4oxc+yRcWsq15KhT+LvLzT8KY4E126iIhMk0KViIjIDMRtm0BolOauQVyv/xnXQAt54wHcxihFQPNYNU2px1Lu97PNcQyZRRX4qmrx+/wUORwsSfQBiIjIjClUiYiITFEkHKarYSsDrduI9jSRPtxB71gqPxk8EoAv5ryBK8VBT9ZCgp5yskuqOai6lmNycia2sF/CahcRkb1HoUpERGQnBvv66K7fQn9XO89Ha2kODPKh8B9ZnNpOHhC2U+l1+nD5yvi3wxdTUeimyHMErlT9aBURmW/0f34REZnX4vE4vQNhWgLDDG/9F/ldL5AT6SLPGMIHeGyDX49/ipLCfEaK19PmduKtXISndAE+h+5/EhERhSoREZlHotFxAk0N9DVvYzzQiGuoDU+0m+v7P0gons36tAbWZwXpS19AKH8BmSXV+Ktqud5bMLGFlQmtX0RE5iaFKhER2SeNDg8RmLj/afN4CW8GUyjoe5WzMx/HDYzbToIOL13Zi/nIflUUlZdTWnAE6Wn60SgiItOjnxwiIpL0BgZHae4eobO1jcKG+3GPdZFv9+ExwANsGjuUjIJ1lCxdRUtqMfnlNfgqKvGkpCa6dBER2QcoVImISNKIxeME25rpbdzKWFcDqQNt5I4H+Ge4hgdHV5FuRPhKXjsDLj/9eatIL6qkoLKWswuLcDgciS5fRET2UQpVIiIyJ0XGxgg0bKO/ZSuBoRjPDJfTGhjkm1l3U2pEidkGvUY+vZlVVFYv50u1qykrzCYr/cREly4iIvOMQpWIiCTc8PAorcEwzV1D5Nbdj3eoDq/dS65hkwtEY4XY7nIOXl5Mq+sMvEWFFFbWkJeenujSRUREFKpERGT22LZNKBCgp8FitKMeZ38bOZFOiMW4rv80AD6Z20d2WjZNOYtx+SvwVixieVk5KyenLzcTdwAiIiI78b6hyjRNL3AXsBAYA7YCn7Usq9s0zYOA24EMoBE4y7KsCF0/gAAAGvBJREFUwMT7ZnVMRETmllg0RndzI6HmOiLdTTwSXUtjYJQTjCc5It0CIGjnMpRZSjSnhM8ft4KKohxys49OcOUiIiLTM5UzVTZwvWVZjwKYpvld4FrTNM8H7gbOtSzrSdM0vwZcC5xnmqYxm2N76oshIiK7Jzw6QltwlJbuMOHGl6nueQxvvIcsI0YWELUdOFOrWb1oAXk5JxDMOQF/dS2V2dn4fG66uwcTfQgiIiK77X1DlWVZvcCjOyx6BrgQWAeELct6cmL5bWw/e3ReAsZERGSWDA4M0lX3OiPt2zBCrWSHO/HYffx+8BisaAnLMwaozE6h1b2WFH8FeWU1+Cur+PdUV6JLFxER2SumdU+VaZoOtgeqPwHlQNNbY5Zl9Zim6TBN0zPbYxPBT0RE9qB4PE5vexvBxi2Euxqxxvw8G/KQOdLBl3L/AkC/nUW/q5CB3BWcdMBqNlZV481NxzA+keDqRUREZs90J6r4ETAE3Ax8eM+Xs/d5vdkJ3b/P507o/mX61LPko55N33gkQktrgIZgnMaWbsy6X5I/HiDDiFACxG2DNucBrFxkUl1UyYhrIaWLl1Dt8+2R/atnyUc9Sy7qV/JRz5LLlEOVaZo3AIuADZZlxU3TbAYqdhgvAGzLsnpne2w6BxwMDhGP29N5yx6j+waSj3qWfNSz9zc6FqXTepWh1q3Yvc1kjnTitYO8EinjzuH1uFIdVOY66XAvw1lQTk5pDYXVNXwgI2OHrZQShT3ytVbPko96llzUr+SjniWOw2Hs1kmYKYUq0zSvAdYCH7Qsa2xi8QtAhmmah03c53QBcE+CxkRE5B1s26a/p5vueovRjgYGh0b48/BKAqFR/jPnL1Sm9DJspxNK8dOccyDeYpNrluxPYX4mDseRiS5fREQkaUxlSvVlwOXAFuCfpmkCNFiW9WHTNM8GbjdNM52JKc4BJs5kzdqYiMh8F4vH6G5ppXkknaauQXxND1MbfgW3MUrRxDqdtpcy/6EcurwII+tcosV+/D4/RQ5HQmsXERFJdoZtJ+ZSuASoBBp0+Z9Mh3qWfOZDz8ajMdobmxisf4VYsJmM4XY8sR7SjChf6j2DqMPFSZ4GatJ7MTzlZJdU46+uJSsnJ9Gl79R86Nm+Rj1LLupX8lHPEmeHy/+q2H4SZ0qmO1GFiIjMosG+Prq3WQx11GOEWvjr2GqsYAqHuN7kY1nPEbZT6XX6aMtdRUpBBV/94FpKijykOI9KdOkiIiLzhkKViMgcEI/H6e1sp603SkMoTrhtC4f2/4U8Ywgf4AMG7EwWuJexsLaaqvxKwnkfxFO6AJ/DmejyRURE5jWFKhGRWRaLx+no6mNg8zOMB5pIG2ojPxog04jw4vD+PDG2hFqPk2XpCwjll5FZUoW/qpZSbwFnJLp4EREReReFKhGRvWh0eJhAfR0Dbduwg83Uh/N4oLcKR2yM6/LvIYaToMNLV/YSHN5yjqzajzOqqklzOYHjEl2+iIiITIFClYjIHtIf7KGzrZOtw9m0BIZY33UXJXYXHgM8wIidRihtBcesLaW80M1I5iJ8ZWV4UlITXbqIiIjMgEKViMg0xW2b7r5RejZvItr2JqkDbeSOB8gxRrCj+fxhYAMFueksz65kPHsxaUVV+Cpr8RUWUajpy0VERPY5ClUiIrsQGRsj0LiN/uatRHuaMYZ7uG3gSMKROGdnPcFqVyO9Rj69mVX05peRVVrDj5asJCs9FTgk0eWLiIjILFCoEhGZMDzQT6C+jq1jXpq7R/F3PMHh8WfINWxygTE7haDTxxFLPZQUF1CRZ5JZ6CUvPT3RpYuIiEgCKVSJyLxj2zahwTHaGxuJbXsGZ38bOWOd5BuDFAA/7/8gg+nFHOgpoSntYFz+SrwVi/CWlVPgcGIm+gBERERkTlGoEpF9Wiwao7u5kVBzHZFAE6mDbTw8spSXh/0sSung4pxHCdq5DKQX05d3AJnFVVy2aAW5+bmJLl1ERESShEKViOwzwqMjbHnBYnPrCHUDGfR1tvPJsf8my4iRBURtB0GHF7Mki+WVtZT79sPpOYXK7OxEly4iIiJJTKFKRJLS4EiE5s5BYq8/hBFqITvcicfuI8Ww6QvX8lzsMCr8ObRmrCPFV05+WQ2+yiryU13UJLp4ERER2acoVInInBaPxwm2t9HbuIVwVyMpA60Ewmnc1X8AAFfmPkWKA/pdRQzkrsBTZbJ/YRUnFhdjGAawLrEHICIiIvs8hSoRmTPGxyMEGrYR7OzgtcgCmruGOH7g9yxytlMCxG2DXiOP7JyFfHxNDeWF2Xg9B+DOyWbBxDZ8Pjfd3YOJPAwRERGZZxSqRCQhRseitASG6N/yAmkdL5E50oHX7iXHiJNuO7l16JMs8OXQV7iOpmyD3AU1+KtqqMrIoCrRxYuIiIjsQKFKRPYq27bp7+mmu95itKMBo7+VnHAn3+07kRE7nePTX+XIjDpCqX6a3Ytw+SvJL6/h5vJKnE4nunxPRERE5jqFKhHZY2KxGN0tTYSat1IX8bE1aODp3sRHXY9TNLFOyHYzkFbEhw4soXDBAsp9B5HrTqfI4Uho7SIiIiK7S6FKRHbLeDRGa/cwHc0tZG17hIyRdjyxHrKMKFnAY8OHMZC7HyVli2lMzSa7pBp/dS3lOTkALE9s+SIiIiJ7jEKViLyvwb4+urdZDHXUY4RayBrt5KmRav4RXkqOMcIVea/S6/TRlruKlIIK8spr2Fi5kFSXK9Gli4iIiOx1ClUiMikej9Pb2UFv4xY6+8d5eaSI1q4+vuL8BT4jjg8YsDPpcxVSVVPBkprllPuz8OSdhM/hTHT5IiIiIgnxvqHKNM0bgI8ClcAKy7Jem1heC9wJeIEgcI5lWXWJGBOR6YvGYnT2jtLcNYhr81/J7t9GfjRAphGhGBgcL6LTdSpVpR4aUk8mx1+Ev6qWUm8BpYkuXkRERGQOmcqZqvuAm4An3rH8NuAWy7LuNk3zLOB24OgEjYnILowODxGo38pA2zbsYDMZIx3EolGu7z8ZgPPdDThdEbqyl+DwluNeUMOyqkWszcqc2ILugBIRERF5L+8bqizLehLANM3JZaZp+oE1wHETi34N3Gyapg8wZnPMsqzuaR6zyD6tv6eH7gaL4Y5Gno7vR1NgmGPGHubAtG14gBE7jd4UP2Peaj59+BLKC90UetaT4tTleyIiIiK7Y3fvqSoD2izLigFYlhUzTbN9Yrkxy2MKVTIvxeIxevpGaQ6MMFT/Mv7Of5I33kWOMULhxDp/iRVQ4i8mxX0k7e7D8VXW4issolDTl4uIiIjsMfNuogqvNzuh+/f53Andv0zfXOhZZGyM5s2b6dlmEe5sIHWglfxoN7cPHkNj1M/KtDY2ZA/Q517IkK+C/MpayhYv4UaPJ9GlJ8Rc6JlMj3qWfNSz5KJ+JR/1LLnsbqhqAUpN03ROnDVyAiUTy41ZHpuWYHCIeNzezcOeGZ/PTXf3YEL2LbsnET0bHugnUL+FwbZ6to4V8FLITXpfPZe4/4ofGLNTCDp9tOes4MT9TPxVCyktWE9qytsv3xuLMS+/3/Q5Sz7qWfJRz5KL+pV81LPEcTiM3ToJs1uhyrKsgGmaLwFnAHdP/P3iW/c3zfaYSDKKx+P09Q/T3DNGW0cPFfV/IGesk3xjkAKgAKiPriWv4HCqq1fS6vTgrajFW1ZGgaYvFxEREZkzpjKl+g+BjwBFwP+aphm0LGsZcAFwp2maVwIh4Jwd3jbbYyJzWjxu09VUT6ipjvFAE67B7ZfvvRpZwK+HD8HA5iv5IQbSi+nLO4DM4ip8VbWc6vPvsJUlCatfRERERN6bYduJuRQuASqBBl3+J9OxOz0Lj47QVV/HQGs9/QPD/GNkMW3dQ/xn1r0UOgeI2g56HF5GM4qJFi3FvfggFviyyUibd7c47hX6nCUf9Sz5qGfJRf1KPupZ4uxw+V8V0DjV9+m3OJEZGOzrozkUpzkwiHvbw5QOvY7H7sNj2HiAQDyXtJylHLm6lKG008ksLMBXWUV+qivRpYuIiIjIHqJQJTIFtm3T3dlFaNvrhLsaSRloJTcSIItRbgydQQwnp+SOkp+ez0DuCtILK/FW1lJVXMKXJqcvX5TQYxARERGRvUOhSuQdxscjBBrq6WvZCqFW/je8nC3dMQ42XuaUzE3EbYNeI49QRjm9+WV84ZjllJUWkJ1xdKJLFxEREZEEUKiSeW1kaIi27mGaghGGmi2W9/wVr91LjhEnB4jYKbjTSjloaS1VeQX0uY/CX1VDVUZGoksXERERkTlCoUrmBdu26esbomfLS4x0NODobyUn3InHGODvQwfzbGQR1VmjmFmZNLsX4fJXkl9ew5I1yzkvNJro8kVERERkDlOokn1OLBaju6WJUNNWIoFGGsK5PBoqITY6yLfz7wEgZOcwkFZIf95aDj9gDR9fWEtetgvD2PC2baWk6CMiIiIiIrum3xglqUXCYTrau2gcSKG5c5C1rXfjj3WSZUTJAqK2g17HCvZbuB9lhZX0uIrxVy6kPCcn0aWLiIiIyD5CoUqSxtDoOF3Wqwy3WhihFrJGO/HYIfqiBdw5eBIZaU4W5eYxnl1ESkEFeeU1+CurOcqVxlGTWylL4BGIiIiIyL5IoUrmnHg8Tm9nO8HGOsKdDYwP9vLb4YMJDoxxfvbfWeFqZcDOpM9VSFPOUlxFNVy77EAKctNxGOsTXb6IiIiIzDMKVZJQ4+PjdDc10DiaRXNghLyWx1k99hyZxhglQNyGXiOP2tJsFqxZgMddhl2UT6m3gNJEFy8iIiIigkKVzKJwJEp7UwvDWzdh9zaTMdKBN96D24jzp75TCDnyWe9x0+VegsNbjru0msKqWqqyMvl0oosXEREREXkPClWyV/T39BBosLZPXx5q4dGxpbzU62ZZagufdv+DETuN3hQ/LbkHkOqr4BJzHUVFXpwOR6JLFxERERGZFoUqmZFYPEawtYW23gj1/U762ls4ceAecowRiibWCdluynMWU7G0iooCk/Hc4/AVFlGoACUiIiIi+wCFKpmy8Wic9u5BBl97jFhPE+nD7XhiPWQY4zSNLuPB8DrKvS56M6vozS8jq6Qa/8JaynPzKE908SIiIiIie4lClezU8EA/gfotDLY1QG8zHeE0fh9aTiwe55q8+0k1YgSdPtpzVuAsqGRN1RI+WFlNaooTODTR5YuIiIiIzBqFqnkuHo/TF+iiq7WdurCH5sAQh3TfwyKjhQKgABi0MxhNW8SJB5ZT5s/GmbOE/OJCChzORJcvIiIiIpJwClXzSCwep7N3lKD1Enbba7gGW8mPdpNlhMmPu/hj3+n4PVmE3LU0ZNWSWVyFr6qWEp+fEuDARB+AiIiIiMgcpFC1jwqPjtC1rY6BtnriwSYyhjv4Uf9xDEcdbMh4gSPT36TH4SWQVYvhLSendCE/ql1GZroLOCjR5YuIiIiIJA2Fqn3AYG+Qrvo6GsZyqQ/Gye38FyfZj+ExbDzAqO2iN8XHMSvy8ZWUUOFZQbY/l/xUV6JLFxERERFJegpVScS2bbr7w3Q0t0DdE6QMtJIbCZBrDFMI/GnwSNozFrHSU0aj63DSC6vwVi6ioLgEv8PB4kQfgIiIiIjIPijpQpVpmrXAnYAXCALnWJZVl9iq9rzx8QiBhm30tWwj2t1E2lA7T41W8dRwNT7HAJfnPkmvkUcoo3xy+vJP1yzFnZuT6NJFREREROaVpAtVwG3ALZZl3W2a5lnA7cDRCa5pRkYGB+mqr6OzL8ybQ/m0d/XyufGfkmPEyQEitpNeRwHVJfksXGhS4c8iPf8EqjIyEl26iIiIiMi8l1ShyjRNP7AGOG5i0a+Bm03T9FmW1Z24yqYnHrdpeuw+Iu0W6cMdeOinAOiILODl+AmUF7ppSDmCzIJi8str8JVV4k1xsijRhYuIiIiIyLskVagCyoA2y7JiAJZlxUzTbJ9YnjSh6vFX2sl742lynaP0uQrp96whvaiKJdWLudHnwzAMYFWiyxQRERERkSlItlA1Y15vdkL37/O5OfmIGpprvknVAi+uVD1Ad67z+dyJLkGmST1LPupZ8lHPkov6lXzUs+SSbKGqBSg1TdM5cZbKCZRMLJ+SYHCIeNzeawXuis/nprt7EABPdgb9fSMJqUOmbseeSXJQz5KPepZ81LPkon4lH/UscRwOY7dOwjj2Qi17jWVZAeAl4IyJRWcALybT/VQiIiIiIrJvSbYzVQAXAHeapnklEALOSXA9IiIiIiIyjyVdqLIsazNwYKLrEBERERERgSQMVTPghO3XSSZSovcv06eeJR/1LPmoZ8lHPUsu6lfyUc8SY4ev+7RmkzNsOzGTNiTAYcATiS5CRERERETmvMOBJ6e68nwKVWnA/kAHEEtwLSIiIiIiMvc4gWLgX8DYVN80n0KViIiIiIjIHpdUU6qLiIiIiIjMNQpVIiIiIiIiM6BQJSIiIiIiMgMKVSIiIiIiIjOgUCUiIiIiIjIDClUiIiIiIiIzoFAlIiIiIiIyAymJLmC+ME2zFrgT8AJB4BzLsuoSW9X8Y5pmIxCe+APwZcuyHjJN8yDgdiADaATOsiwrMPGe3RqT3WOa5g3AR4FKYIVlWa9NLH/Pz9DeGJOp20XPGtnJ521iTJ+5BDFN0wvcBSxk+4MttwKftSyre2/0RT2buffpmQ28CsQnVj/bsqxXJ963Afgu23/fewH4N8uyRmYyJlNnmuZ9QBXbezMEXGJZ1kv6ebZv0pmq2XMbcItlWbXALWz/ASOJcZplWasm/jxkmqYB3A1cNNGfx4FrAXZ3TGbkPuAIoOkdy3f1GdobYzJ179UzeMfnDXb/c6XP3B5jA9dblmValrUfsA24dm/0RT3bY3basx3GD9nhc/ZWoMoG/n979x9qd13Hcfw5pzPZZFOWhrYJ4nqJRqY5yB8RQWFKssraNH8GQkompasglH7AKmxhaayFplmkMBAygqQ/bMnSoB9aWPEOKXWamnP+/lXbbn98v9PjdTu7O99zd+7ung+43O/5vD/fz/nc8+Fzv7zP53s+5zrg9Ko6AngOWN4lpp12flUdU1XHAiuBG9pyr2fTkEnVLpDkIOA44Ja26BbguCRvHl2v1ON44OWqWtc+Xg0s7RjTgKpqXVWt7y3rN4cmIzZZf9t0ta0x2wHn3AhV1caqWttT9DvgMCZnXByzIegzZv2cCvyhZ7ViNbCsY0w7oaqe6Xk4F9ji9Wz6MqnaNRYAj1TVZoD297/bcu16P03ylySrkswDFtLzDntVbQD2SnJgh5iGq98cmoyYhmf8fAPn3JSRZC/gYuDnTM64OGZDNm7Mtlqb5N4k30iyb1v2utceeIjX/r8NGtNOSnJ9koeAFcD5eD2btkyqtKd5T1UdAywGZgDfG3F/pOnM+Tb1XUvzWQ/HZvcxfswWVtXxNLfgHgVcOaqO6Y2q6sKqWgh8ieZzapqmTKp2jfXAoUlmArS/D2nLtQttvUWpql4BVgEn0bwL9+ptFEnmA2NVtbFDTMPVbw5NRkxDsJ35Bs65KaHdYGQRsKyqtjA54+KYDdE2xqx3nj0LXM925hnNCtT6jjENqKp+ArwPeBivZ9OSSdUu0O5ydC9wVlt0FnBPVT0xul7teZLMTjK3PZ4BnEkzLn8E9ktyclv1ImBNezxoTEPUbw5NRmzy/6Lpr898A+fcyCVZAbwL+HCb9MLkjItjNiTbGrMkByTZrz3eG/gYr82z24HFSRa1j3tf+0FjmqAkc5Is6Hl8OrAR8Ho2Tc0YGxsbdR/2CEmOpNnq8gDgKZqtLmu0vdqzJDkcuBWY2f78Dbi0qh5NciLNTjlv4rUtfx9vzxsopsEkuQb4KPAWYAPwZFUd3W8OTUZME7etMQNOZzvzrT3HOTciSY4G7gP+AbzUFv+rqj4yGePimHW3vTEDrqJ5bceAfYC7gM9W1fPteUvaOjOBe4ALquqFLjFNTJKDgduA2cBmmoRqeVX9yevZ9GRSJUmSJEkdePufJEmSJHVgUiVJkiRJHZhUSZIkSVIHJlWSJEmS1IFJlSRJkiR1YFIlSdrtJFmd5Mo+8bEkRwz5Oc9O8qthtilJmh7cUl2SNFJJzgQ+B7wdeIHm+3duAr5fVQNdpJKMAYuq6v5txNYC7wY2AS8DdwKf3vodWsOQ5ALgwqo6eUd1JUm7P1eqJEkjk+Ry4LvAt2i+PPhg4CLgJGDWds6ZOYSnvqSq5gBvA+YBVw+hTUnSHmrvUXdAkrRnSjIX+BpwXlXd2hO6Bzi7p96PgJeAw4D3AkuSnAM8XFVXtHU+D1wGjAFXTLQPVbUxya3AxT19uhY4FXgRuA74elVtGb/61K6GXQxcDswHbgYuAY4EVgP7JHke2FRV85KcBqwEFgDPAldX1cqJ9lWSNHW5UiVJGpUTgH2B2yZQ9xPACmB/YF1vIMkHgeXAB4BFwPsn2oEk84EzaBI5aBKqucDhNAncecAn+zTxIWAxcAywFDilqv5Os9p2d1XNqap5bd0fAp+qqv1pbnW8Y6L9lCRNba5USZJGZT6woao2bS1IchdwFE2ydUpV3dmGbquq37bHLyfpbWcpcGNV3de28RXgrB089zVJVtJ8hmstcFl7W+Ey4Niqeg54Lsm3gXNpEqJt+WZVPQ08neTXwDuB27dT93/AUUn+XFVPAU/toI+SpN2EK1WSpFF5Epif5NU3+KrqxHZl50lef41a36edQ8bFH5zAc19aVfOq6tCqOruqnqBJ8maNO/9B4NA+7TzWc/wiMKdP3TOA04AHk/wmyQkT6KckaTdgUiVJGpW7gVeAJROo228XwEdpPqe01cIB+7OBZjXpsHFtPTJAW2/ob1X9vqqWAAcBPwPWDNJJSdLU4+1/kqSRqKqnk3wVWJVkBs1tcy8C7wBm70RTa4Abk/wYeAD48oD92ZxkDbAiyXnAgTSbXwyymcTjwFuTzKqq/yaZBXwc+EVVPZPkWWDzIP2UJE09rlRJkkamqq6iSVy+APyHJhn5AfBF4K4JtvFL4Ds0Gz/cT7cNID5D8zmrf9JsiHEzcMMA7dwB/BV4LMmGtuxc4IE2oboIOKdDPyVJU4hf/itJkiRJHbhSJUmSJEkdmFRJkiRJUgcmVZIkSZLUgUmVJEmSJHVgUiVJkiRJHZhUSZIkSVIHJlWSJEmS1IFJlSRJkiR1YFIlSZIkSR38H+HveVaPjaRDAAAAAElFTkSuQmCC\n", "text/plain": ["<Figure size 1008x432 with 2 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["fig, (ax1, ax2) = plt.subplots(nrows=2, sharex=True)\n", "for ax, pmu_counter in zip([ax1, ax2], [\"PM_RUN_CYC (min)\", \"PM_INST_CMPL (min)\"]):\n", " df.set_index(\"Grid Points\")[pmu_counter].plot(ax=ax, legend=True);\n", " ax.plot(\n", " df[\"Grid Points\"], \n", " linear_function(df[\"Grid Points\"], *fit_parameters[pmu_counter]), \n", " linestyle=\"--\", \n", " label=\"Fit: {:.2f} * x + {:.2f}\".format(*fit_parameters[pmu_counter])\n", " )\n", " ax.legend();"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Please execute the next cell to summarize the first task."]}, {"cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["The algorithm under investigation runs about 8 cycles and executes about 14 instructions per grid point\n"]}], "source": ["print(\"The algorithm under investigation runs about {:.0f} cycles and executes about {:.0f} instructions per grid point\".format(\n", " *[fit_parameters[pmu_counter][0] for pmu_counter in [\"PM_RUN_CYC (min)\", \"PM_INST_CMPL (min)\"]]\n", "))"]}, {"cell_type": "markdown", "metadata": {}, "source": ["**Bonus:**\n", "\n", "The linear fits also calculate a y intersection (\u00bb`b`\u00ab). How do you interpret this value?"]}, {"cell_type": "markdown", "metadata": {"exercise": "solution"}, "source": ["The y axis intersection; that is, `b` of the linear fit, is the inherent overhead of the program execution. Even if our program would not compute any stencil operation at all for any grid point, it would still complete this many (~1800) instructions and run this many (~680) cycles. Interestingly, it is also the unparallelizable overhead of this (toy) example."]}, {"cell_type": "markdown", "metadata": {}, "source": ["We are revisiting the graph in a little while.\n", "\n", "[Back to top](#toc)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Task 2: Measuring Loads and Stores\n", "<a name=\"task2\"></a>\n", "\n", "Looking at the source code, how many loads and stores from / to memory do you expect? Have a look at the loop which we instrumented.\n", "\n", "Let's compare your estimate to what the system actually does!\n", "\n", "### Task A\n", "<a name=\"task2-a\"></a>\n", "\n", "Please measure counters for loads and stores. See the TODOs in [`poisson2d.ld_st.c`](/edit/Tasks/poisson2d.ld_st.c). This time, implement `PM_LD_CMPL` and `PM_ST_CMPL`.\n", "\n", "Compile with `make task2`, test your program with a single run with `make run_task2`, and then finally submit a benchmarking run to the batch system with `make bench_task2`. The following cell will take care of all this.\n", "\n", "[Back to top](#toc)"]}, {"cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["bsub -W 60 -nnodes 1 -Is -P TRN003 jsrun -n 1 -c 1 -g ALL_GPUS ./bench.sh poisson2d.ld_st.bin /gpfs/wolf/trn003/scratch/aherten//poisson2d.ld_st.bin.csv\n", "Job <24416> is submitted to default queue <batch>.\n", "<<Waiting for dispatch ...>>\n", "<<Starting on login1>>\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,4,0.0012,119819,598,817,32902,164,266\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,8,0.0013,161819,808,1027,56902,284,386\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,12,0.0014,221819,1108,1327,71902,359,461\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,16,0.0015,281819,1408,1627,86902,434,536\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,20,0.0015,341819,1708,1927,101902,509,611\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,24,0.0016,401819,2008,2227,116902,584,686\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,28,0.0016,461819,2308,2527,131902,659,761\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,32,0.0018,521819,2608,2827,146902,734,836\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,36,0.0018,581819,2908,3127,161902,809,911\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,40,0.0018,641819,3208,3427,176902,884,986\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,44,0.0019,701819,3508,3727,191902,959,1061\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,48,0.0020,761819,3808,4027,206902,1034,1136\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,52,0.0020,821819,4108,4327,221902,1109,1211\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,56,0.0021,881819,4408,4627,236902,1184,1286\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,60,0.0022,941819,4708,4927,251902,1259,1361\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,64,0.0023,1001819,5008,5227,266902,1334,1436\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,68,0.0023,1061819,5308,5527,281902,1409,1511\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,72,0.0025,1121819,5608,5827,296902,1484,1586\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,76,0.0028,1181819,5908,6127,311902,1559,1661\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,80,0.0025,1241819,6208,6427,326902,1634,1736\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,84,0.0026,1301819,6508,6727,341902,1709,1811\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,88,0.0026,1361819,6808,7027,356902,1784,1886\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,92,0.0027,1421819,7108,7327,371902,1859,1961\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,96,0.0028,1481819,7408,7627,386902,1934,2036\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,100,0.0029,1541819,7708,7927,401902,2009,2111\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,104,0.0029,1601819,8008,8227,416902,2084,2186\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,108,0.0031,1661819,8308,8527,431902,2159,2261\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,112,0.0030,1721819,8608,8827,446902,2234,2336\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,116,0.0031,1781819,8908,9127,461902,2309,2411\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,120,0.0032,1841819,9208,9427,476902,2384,2486\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,124,0.0033,1901819,9508,9727,491902,2459,2561\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,128,0.0033,1961819,9808,10027,506902,2534,2636\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,132,0.0034,2021819,10108,10327,521902,2609,2711\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,136,0.0035,2081819,10408,10627,536902,2684,2786\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,140,0.0036,2141819,10708,10927,551902,2759,2861\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,144,0.0036,2201819,11008,11227,566902,2834,2936\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,148,0.0036,2261819,11308,11527,581902,2909,3011\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,152,0.0037,2321819,11608,11827,596902,2984,3086\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,156,0.0038,2381819,11908,12127,611902,3059,3161\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,160,0.0040,2441819,12208,12427,626902,3134,3236\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,164,0.0039,2501819,12508,12727,641902,3209,3311\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,168,0.0040,2561819,12808,13027,656902,3284,3386\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,172,0.0040,2621819,13108,13327,671902,3359,3461\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,176,0.0041,2681819,13408,13627,686902,3434,3536\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,180,0.0041,2741819,13708,13927,701902,3509,3611\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,184,0.0042,2801819,14008,14227,716902,3584,3686\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,188,0.0044,2861819,14308,14527,731902,3659,3761\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,192,0.0044,2921819,14608,14827,746902,3734,3836\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,196,0.0045,2981819,14908,15127,761902,3809,3911\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,200,0.0045,3041819,15208,15427,776902,3884,3986\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,204,0.0045,3101819,15508,15727,791902,3959,4061\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,208,0.0046,3161819,15808,16027,806902,4034,4136\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,212,0.0047,3221819,16108,16327,821902,4109,4211\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,216,0.0047,3281819,16408,16627,836902,4184,4286\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,220,0.0048,3341819,16708,16927,851902,4259,4361\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,224,0.0049,3401819,17008,17227,866902,4334,4436\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,228,0.0050,3461819,17308,17527,881902,4409,4511\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,232,0.0050,3521819,17608,17827,896902,4484,4586\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,236,0.0051,3581819,17908,18127,911902,4559,4661\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,240,0.0051,3641819,18208,18427,926902,4634,4736\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,244,0.0052,3701819,18508,18727,941902,4709,4811\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,248,0.0053,3761819,18808,19027,956902,4784,4886\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,252,0.0053,3821819,19108,19327,971902,4859,4961\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,256,0.0054,3881819,19408,19627,986902,4934,5036\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,260,0.0055,3941819,19708,19927,1001902,5009,5111\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,264,0.0055,4001819,20008,20227,1016902,5084,5186\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,268,0.0056,4061819,20308,20527,1031902,5159,5261\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,272,0.0057,4121819,20608,20827,1046902,5234,5336\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,276,0.0057,4181819,20908,21127,1061902,5309,5411\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,280,0.0058,4241819,21208,21427,1076902,5384,5486\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,284,0.0059,4301819,21508,21727,1091902,5459,5561\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,288,0.0059,4361819,21808,22027,1106902,5534,5636\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,292,0.0060,4421819,22108,22327,1121902,5609,5711\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,296,0.0061,4481819,22408,22627,1136902,5684,5786\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,300,0.0061,4541819,22708,22927,1151902,5759,5861\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,304,0.0062,4601819,23008,23227,1166902,5834,5936\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,308,0.0063,4661819,23308,23527,1181902,5909,6011\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,312,0.0064,4721819,23608,23827,1196902,5984,6086\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,316,0.0066,4781819,23908,24127,1211902,6059,6161\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,320,0.0065,4841819,24208,24427,1226902,6134,6236\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,324,0.0065,4901819,24508,24727,1241902,6209,6311\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,328,0.0069,4961819,24808,25027,1256902,6284,6386\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,332,0.0066,5021819,25108,25327,1271902,6359,6461\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,336,0.0067,5081819,25408,25627,1286902,6434,6536\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,340,0.0068,5141819,25708,25927,1301902,6509,6611\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,344,0.0069,5201819,26008,26227,1316902,6584,6686\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,348,0.0069,5261819,26308,26527,1331902,6659,6761\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,352,0.0070,5321819,26608,26827,1346902,6734,6836\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,356,0.0070,5381819,26908,27127,1361902,6809,6911\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,360,0.0071,5441819,27208,27427,1376902,6884,6986\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,364,0.0072,5501819,27508,27727,1391902,6959,7061\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,368,0.0072,5561819,27808,28027,1406902,7034,7136\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,372,0.0073,5621819,28108,28327,1421902,7109,7211\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,376,0.0074,5681819,28408,28627,1436902,7184,7286\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,380,0.0074,5741819,28708,28927,1451902,7259,7361\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,384,0.0075,5801819,29008,29227,1466902,7334,7436\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,388,0.0076,5861819,29308,29527,1481902,7409,7511\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,392,0.0076,5921819,29608,29827,1496902,7484,7586\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,396,0.0077,5981819,29908,30127,1511902,7559,7661\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,400,0.0078,6041819,30208,30427,1526902,7634,7736\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,404,0.0079,6101819,30508,30727,1541902,7709,7811\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,408,0.0079,6161819,30808,31027,1556902,7784,7886\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,412,0.0080,6221819,31108,31327,1571902,7859,7961\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,416,0.0081,6281819,31408,31627,1586902,7934,8036\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,420,0.0081,6341819,31708,31927,1601902,8009,8111\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,424,0.0082,6401819,32008,32227,1616902,8084,8186\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,428,0.0082,6461819,32308,32527,1631902,8159,8261\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,432,0.0085,6521819,32608,32827,1646902,8234,8336\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,436,0.0084,6581819,32908,33127,1661902,8309,8411\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,440,0.0084,6641819,33208,33427,1676902,8384,8486\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,444,0.0085,6701819,33508,33727,1691902,8459,8561\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,448,0.0087,6761819,33808,34027,1706902,8534,8636\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,452,0.0087,6821819,34108,34327,1721902,8609,8711\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,456,0.0087,6881819,34408,34627,1736902,8684,8786\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,460,0.0088,6941819,34708,34927,1751902,8759,8861\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,464,0.0088,7001819,35008,35227,1766902,8834,8936\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,468,0.0089,7061819,35308,35527,1781902,8909,9011\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,472,0.0090,7121819,35608,35827,1796902,8984,9086\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,476,0.0091,7181819,35908,36127,1811902,9059,9161\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,480,0.0091,7241819,36208,36427,1826902,9134,9236\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,484,0.0092,7301819,36508,36727,1841902,9209,9311\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,488,0.0093,7361819,36808,37027,1856902,9284,9386\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,492,0.0094,7421819,37108,37327,1871902,9359,9461\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,496,0.0095,7481819,37408,37627,1886902,9434,9536\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,500,0.0094,7541819,37708,37927,1901902,9509,9611\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,504,0.0095,7601819,38008,38227,1916902,9584,9686\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,508,0.0096,7661819,38308,38527,1931902,9659,9761\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,512,0.0097,7721819,38608,38827,1946902,9734,9836\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,516,0.0098,7781819,38908,39127,1961902,9809,9911\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,520,0.0098,7841819,39208,39427,1976902,9884,9986\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,524,0.0099,7901819,39508,39727,1991902,9959,10061\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,528,0.0099,7961819,39808,40027,2006902,10034,10136\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,532,0.0100,8021819,40108,40327,2021902,10109,10211\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,536,0.0101,8081819,40408,40627,2036902,10184,10286\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,540,0.0101,8141819,40708,40927,2051902,10259,10361\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,544,0.0103,8201819,41008,41227,2066902,10334,10436\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,548,0.0103,8261819,41308,41527,2081902,10409,10511\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,552,0.0104,8321819,41608,41827,2096902,10484,10586\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,556,0.0106,8381819,41908,42127,2111902,10559,10661\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,560,0.0106,8441819,42208,42427,2126902,10634,10736\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,564,0.0106,8501819,42508,42727,2141902,10709,10811\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,568,0.0107,8561819,42808,43027,2156902,10784,10886\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,572,0.0108,8621819,43108,43327,2171902,10859,10961\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,576,0.0109,8681819,43408,43627,2186902,10934,11036\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,580,0.0110,8741819,43708,43927,2201902,11009,11111\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,584,0.0110,8801819,44008,44227,2216902,11084,11186\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,588,0.0110,8861819,44308,44527,2231902,11159,11261\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,592,0.0111,8921819,44608,44827,2246902,11234,11336\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,596,0.0113,8981819,44908,45127,2261902,11309,11411\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,600,0.0113,9041819,45208,45427,2276902,11384,11486\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,604,0.0114,9101819,45508,45727,2291902,11459,11561\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,608,0.0115,9161819,45808,46027,2306902,11534,11636\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,612,0.0115,9221819,46108,46327,2321902,11609,11711\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,616,0.0115,9281819,46408,46627,2336902,11684,11786\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,620,0.0116,9341819,46708,46927,2351902,11759,11861\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,624,0.0117,9401819,47008,47227,2366902,11834,11936\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,628,0.0117,9461819,47308,47527,2381902,11909,12011\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,632,0.0118,9521819,47608,47827,2396902,11984,12086\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,636,0.0119,9581819,47908,48127,2411902,12059,12161\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,640,0.0119,9641819,48208,48427,2426902,12134,12236\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,644,0.0121,9701819,48508,48727,2441902,12209,12311\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,648,0.0121,9761819,48808,49027,2456902,12284,12386\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,652,0.0121,9821819,49108,49327,2471902,12359,12461\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,656,0.0122,9881819,49408,49627,2486902,12434,12536\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,660,0.0123,9941819,49708,49927,2501902,12509,12611\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,664,0.0123,10001819,50008,50227,2516902,12584,12686\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,668,0.0124,10061819,50308,50527,2531902,12659,12761\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,672,0.0124,10121819,50608,50827,2546902,12734,12836\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,676,0.0126,10181819,50908,51127,2561902,12809,12911\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,680,0.0126,10241819,51208,51427,2576902,12884,12986\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,684,0.0127,10301819,51508,51727,2591902,12959,13061\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,688,0.0128,10361819,51808,52027,2606902,13034,13136\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,692,0.0128,10421819,52108,52327,2621902,13109,13211\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,696,0.0129,10481819,52408,52627,2636902,13184,13286\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,700,0.0131,10541819,52708,52927,2651902,13259,13361\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,704,0.0131,10601819,53008,53227,2666902,13334,13436\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,708,0.0130,10661819,53308,53527,2681902,13409,13511\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,712,0.0131,10721819,53608,53827,2696902,13484,13586\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,716,0.0132,10781819,53908,54127,2711902,13559,13661\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,720,0.0132,10841819,54208,54427,2726902,13634,13736\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,724,0.0134,10901819,54508,54727,2741902,13709,13811\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,728,0.0134,10961819,54808,55027,2756902,13784,13886\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,732,0.0134,11021819,55108,55327,2771902,13859,13961\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,736,0.0135,11081819,55408,55627,2786902,13934,14036\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,740,0.0137,11141819,55708,55927,2801902,14009,14111\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,744,0.0138,11201819,56008,56227,2816902,14084,14186\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,748,0.0137,11261819,56308,56527,2831902,14159,14261\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,752,0.0138,11321819,56608,56827,2846902,14234,14336\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,756,0.0139,11381819,56908,57127,2861902,14309,14411\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,760,0.0140,11441819,57208,57427,2876902,14384,14486\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,764,0.0140,11501819,57508,57727,2891902,14459,14561\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,768,0.0141,11561819,57808,58027,2906902,14534,14636\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,772,0.0141,11621819,58108,58327,2921902,14609,14711\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,776,0.0142,11681819,58408,58627,2936902,14684,14786\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,780,0.0143,11741819,58708,58927,2951902,14759,14861\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,784,0.0144,11801819,59008,59227,2966902,14834,14936\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,788,0.0144,11861819,59308,59527,2981902,14909,15011\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,792,0.0145,11921819,59608,59827,2996902,14984,15086\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,796,0.0145,11981819,59908,60127,3011902,15059,15161\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,800,0.0147,12041819,60208,60427,3026902,15134,15236\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,804,0.0147,12101819,60508,60727,3041902,15209,15311\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,808,0.0148,12161819,60808,61027,3056902,15284,15386\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,812,0.0148,12221819,61108,61327,3071902,15359,15461\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,816,0.0150,12281819,61408,61627,3086902,15434,15536\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,820,0.0149,12341819,61708,61927,3101902,15509,15611\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,824,0.0150,12401819,62008,62227,3116902,15584,15686\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,828,0.0151,12461819,62308,62527,3131902,15659,15761\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,832,0.0152,12521819,62608,62827,3146902,15734,15836\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,836,0.0152,12581819,62908,63127,3161902,15809,15911\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,840,0.0153,12641819,63208,63427,3176902,15884,15986\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,844,0.0153,12701819,63508,63727,3191902,15959,16061\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,848,0.0154,12761819,63808,64027,3206902,16034,16136\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,852,0.0155,12821819,64108,64327,3221902,16109,16211\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,856,0.0156,12881819,64408,64627,3236902,16184,16286\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,860,0.0156,12941819,64708,64927,3251902,16259,16361\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,864,0.0157,13001819,65008,65227,3266902,16334,16436\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,868,0.0158,13061819,65308,65527,3281902,16409,16511\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,872,0.0159,13121819,65608,65827,3296902,16484,16586\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,876,0.0159,13181819,65908,66127,3311902,16559,16661\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,880,0.0160,13241819,66208,66427,3326902,16634,16736\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,884,0.0160,13301819,66508,66727,3341902,16709,16811\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,888,0.0161,13361819,66808,67027,3356902,16784,16886\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,892,0.0162,13421819,67108,67327,3371902,16859,16961\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,896,0.0163,13481819,67408,67627,3386902,16934,17036\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,900,0.0164,13541819,67708,67927,3401902,17009,17111\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,904,0.0165,13601819,68008,68227,3416902,17084,17186\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,908,0.0165,13661819,68308,68527,3431902,17159,17261\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,912,0.0166,13721819,68608,68827,3446902,17234,17336\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,916,0.0166,13781819,68908,69127,3461902,17309,17411\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,920,0.0167,13841819,69208,69427,3476902,17384,17486\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,924,0.0168,13901819,69508,69727,3491902,17459,17561\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,928,0.0169,13961819,69808,70027,3506902,17534,17636\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,932,0.0175,14021819,70108,70327,3521902,17609,17711\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,936,0.0170,14081819,70408,70627,3536902,17684,17786\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,940,0.0171,14141819,70708,70927,3551902,17759,17861\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,944,0.0171,14201819,71008,71227,3566902,17834,17936\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,948,0.0172,14261819,71308,71527,3581902,17909,18011\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,952,0.0172,14321819,71608,71827,3596902,17984,18086\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,956,0.0173,14381819,71908,72127,3611902,18059,18161\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,960,0.0174,14441819,72208,72427,3626902,18134,18236\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,964,0.0176,14501819,72508,72727,3641902,18209,18311\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,968,0.0178,14561819,72808,73027,3656902,18284,18386\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,972,0.0177,14621819,73108,73327,3671902,18359,18461\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,976,0.0178,14681819,73408,73627,3686902,18434,18536\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,980,0.0179,14741819,73708,73927,3701902,18509,18611\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,984,0.0179,14801819,74008,74227,3716902,18584,18686\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,988,0.0180,14861819,74308,74527,3731902,18659,18761\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,992,0.0181,14921819,74608,74827,3746902,18734,18836\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,996,0.0182,14981819,74908,75127,3761902,18809,18911\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,1000,0.0182,15041819,75208,75427,3776902,18884,18986\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,1004,0.0183,15101819,75508,75727,3791902,18959,19061\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,1008,0.0183,15161819,75808,76027,3806902,19034,19136\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,1012,0.0184,15221819,76108,76327,3821902,19109,19211\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,1016,0.0185,15281819,76408,76627,3836902,19184,19286\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,1020,0.0185,15341819,76708,76927,3851902,19259,19361\n", "iter,ny,nx,Runtime,PM_LD_CMPL (total),PM_LD_CMPL (min), PM_LD_CMPL (max),PM_ST_CMPL (total),PM_ST_CMPL (min), PM_ST_CMPL (max)\n", "200,32,1024,0.0186,15401819,77008,77227,3866902,19334,19436\n", "mv /gpfs/wolf/trn003/scratch/aherten//poisson2d.ld_st.bin.csv .\n"]}], "source": ["!make bench_task2"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Once the run finished, let's plot it again in the course of the following cells (non-interactive: `make graph_task2a`)."]}, {"cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>iter</th>\n", " <th>ny</th>\n", " <th>nx</th>\n", " <th>Runtime</th>\n", " <th>PM_LD_CMPL (total)</th>\n", " <th>PM_LD_CMPL (min)</th>\n", " <th>PM_LD_CMPL (max)</th>\n", " <th>PM_ST_CMPL (total)</th>\n", " <th>PM_ST_CMPL (min)</th>\n", " <th>PM_ST_CMPL (max)</th>\n", " <th>Grid Points</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>200</td>\n", " <td>32</td>\n", " <td>4</td>\n", " <td>0.0012</td>\n", " <td>119819</td>\n", " <td>598</td>\n", " <td>817</td>\n", " <td>32902</td>\n", " <td>164</td>\n", " <td>266</td>\n", " <td>128</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>200</td>\n", " <td>32</td>\n", " <td>8</td>\n", " <td>0.0013</td>\n", " <td>161819</td>\n", " <td>808</td>\n", " <td>1027</td>\n", " <td>56902</td>\n", " <td>284</td>\n", " <td>386</td>\n", " <td>256</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>200</td>\n", " <td>32</td>\n", " <td>12</td>\n", " <td>0.0014</td>\n", " <td>221819</td>\n", " <td>1108</td>\n", " <td>1327</td>\n", " <td>71902</td>\n", " <td>359</td>\n", " <td>461</td>\n", " <td>384</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>200</td>\n", " <td>32</td>\n", " <td>16</td>\n", " <td>0.0015</td>\n", " <td>281819</td>\n", " <td>1408</td>\n", " <td>1627</td>\n", " <td>86902</td>\n", " <td>434</td>\n", " <td>536</td>\n", " <td>512</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>200</td>\n", " <td>32</td>\n", " <td>20</td>\n", " <td>0.0015</td>\n", " <td>341819</td>\n", " <td>1708</td>\n", " <td>1927</td>\n", " <td>101902</td>\n", " <td>509</td>\n", " <td>611</td>\n", " <td>640</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>"], "text/plain": [" iter ny nx Runtime PM_LD_CMPL (total) PM_LD_CMPL (min) \\\n", "0 200 32 4 0.0012 119819 598 \n", "1 200 32 8 0.0013 161819 808 \n", "2 200 32 12 0.0014 221819 1108 \n", "3 200 32 16 0.0015 281819 1408 \n", "4 200 32 20 0.0015 341819 1708 \n", "\n", " PM_LD_CMPL (max) PM_ST_CMPL (total) PM_ST_CMPL (min) PM_ST_CMPL (max) \\\n", "0 817 32902 164 266 \n", "1 1027 56902 284 386 \n", "2 1327 71902 359 461 \n", "3 1627 86902 434 536 \n", "4 1927 101902 509 611 \n", "\n", " Grid Points \n", "0 128 \n", "1 256 \n", "2 384 \n", "3 512 \n", "4 640 "]}, "execution_count": 8, "metadata": {}, "output_type": "execute_result"}], "source": ["df_ldst = pd.read_csv(\"poisson2d.ld_st.bin.csv\", skiprows=range(2, 50000, 2))\n", "df_ldst[\"Grid Points\"] = df_ldst[\"nx\"] * df_ldst[\"ny\"] \n", "df_ldst.head()"]}, {"cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 1008x432 with 2 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["fig, (ax1, ax2) = plt.subplots(nrows=2, sharex=True)\n", "df_ldst.set_index(\"Grid Points\")[\"PM_LD_CMPL (min)\"].plot(ax=ax1, legend=True);\n", "df_ldst.set_index(\"Grid Points\")[\"PM_ST_CMPL (min)\"].plot(ax=ax2, legend=True);"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Also this behaviour looks \u2013\u00a0at a first glance \u2013\u00a0linear. We can again fit a first-order polynom (and re-use our previously defined function `curve_fit`)!"]}, {"cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Counter PM_LD_CMPL (min) is proportional to the grid points (nx*ny) by a factor of 2.3437 (\u00b1 0.000037)\n", "Counter PM_ST_CMPL (min) is proportional to the grid points (nx*ny) by a factor of 0.5860 (\u00b1 0.000019)\n"]}], "source": ["_fit, _cov = common.print_and_return_fit(\n", " [\"PM_LD_CMPL (min)\", \"PM_ST_CMPL (min)\"], \n", " df_ldst.set_index(\"Grid Points\"), \n", " linear_function,\n", " format_value=\".4f\"\n", ")\n", "fit_parameters = {**fit_parameters, **_fit}\n", "fit_covariance = {**fit_covariance, **_cov}"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Let's overlay this in one common plot:"]}, {"cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 1008x432 with 2 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["fig, (ax1, ax2) = plt.subplots(nrows=2, sharex=True)\n", "for ax, pmu_counter in zip([ax1, ax2], [\"PM_LD_CMPL (min)\", \"PM_ST_CMPL (min)\"]):\n", " df_ldst.set_index(\"Grid Points\")[pmu_counter].plot(ax=ax, legend=True);\n", " ax.plot(\n", " df_ldst[\"Grid Points\"], \n", " linear_function(df[\"Grid Points\"], *fit_parameters[pmu_counter]), \n", " linestyle=\"--\", \n", " label=\"Fit: {:.2f} * x + {:.2f}\".format(*fit_parameters[pmu_counter])\n", " )\n", " ax.legend();"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Did you expect more?\n", "\n", "The reason is simple: Among the load and store instructions counted by `PM_LD_CMPL` and `PM_ST_CMPL` are vector instructions which can load and store multiple (in this case: two) values at a time. To see how many *bytes* are loaded and stored, we need to measure counters for vectorized loads and stores as well.\n", "\n", "### TASK B\n", "<a name=\"task2-b\"></a>\n", "\n", "Please measure counters for _vectorized_ loads and _vectorized_ stores. See the TODOs in [`poisson2d.vld.c`](poisson2d.vld.c) and [`poisson2d.vst.c`](poisson2d.vst.c) (*Note: These vector counters can not be measured together and need separate files and runs*). Can you find out the name of the counters yourself, using `papi_native_avail | grep VECTOR_`?\n", "\n", "Compile, test, and bench-run your program again.\n", "\n", "[Back to top](#toc)"]}, {"cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["| PM_VECTOR_FLOP_CMPL |\n", "| PM_VECTOR_LD_CMPL |\n", "| PM_VECTOR_ST_CMPL |\n"]}], "source": ["!papi_native_avail | grep VECTOR_"]}, {"cell_type": "markdown", "metadata": {}, "source": ["`make bench_task3` will submit benchmark runs of both vectorized counters to the batch system (as two subsequent runs of the individual files)."]}, {"cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["bsub -W 60 -nnodes 1 -Is -P TRN003 jsrun -n 1 -c 1 -g ALL_GPUS ./bench.sh poisson2d.vld.bin /gpfs/wolf/trn003/scratch/aherten//poisson2d.vld.bin.csv\n", "Job <24641> is submitted to default queue <batch>.\n", "<<Waiting for dispatch ...>>\n", "<<Starting on login1>>\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,4,0.0010,0,0,0\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,8,0.0011,114000,570,570\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,12,0.0012,174000,870,870\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,16,0.0012,234000,1170,1170\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,20,0.0013,294000,1470,1470\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,24,0.0014,354000,1770,1770\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,28,0.0014,414000,2070,2070\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,32,0.0015,474000,2370,2370\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,36,0.0016,534000,2670,2670\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,40,0.0016,594000,2970,2970\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,44,0.0017,654000,3270,3270\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,48,0.0018,714000,3570,3570\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,52,0.0018,774000,3870,3870\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,56,0.0019,834000,4170,4170\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,60,0.0020,894000,4470,4470\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,64,0.0021,954000,4770,4770\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,68,0.0022,1014000,5070,5070\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,72,0.0022,1074000,5370,5370\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,76,0.0022,1134000,5670,5670\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,80,0.0023,1194000,5970,5970\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,84,0.0024,1254000,6270,6270\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,88,0.0024,1314000,6570,6570\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,92,0.0025,1374000,6870,6870\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,96,0.0027,1434000,7170,7170\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,100,0.0026,1494000,7470,7470\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,104,0.0029,1554000,7770,7770\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,108,0.0027,1614000,8070,8070\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,112,0.0028,1674000,8370,8370\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,116,0.0029,1734000,8670,8670\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,120,0.0029,1794000,8970,8970\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,124,0.0030,1854000,9270,9270\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,128,0.0032,1914000,9570,9570\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,132,0.0031,1974000,9870,9870\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,136,0.0032,2034000,10170,10170\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,140,0.0033,2094000,10470,10470\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,144,0.0033,2154000,10770,10770\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,148,0.0034,2214000,11070,11070\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,152,0.0036,2274000,11370,11370\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,156,0.0035,2334000,11670,11670\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,160,0.0036,2394000,11970,11970\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,164,0.0037,2454000,12270,12270\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,168,0.0037,2514000,12570,12570\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,172,0.0038,2574000,12870,12870\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,176,0.0039,2634000,13170,13170\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,180,0.0039,2694000,13470,13470\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,184,0.0040,2754000,13770,13770\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,188,0.0041,2814000,14070,14070\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,192,0.0041,2874000,14370,14370\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,196,0.0042,2934000,14670,14670\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,200,0.0042,2994000,14970,14970\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,204,0.0043,3054000,15270,15270\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,208,0.0045,3114000,15570,15570\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,212,0.0045,3174000,15870,15870\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,216,0.0045,3234000,16170,16170\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,220,0.0046,3294000,16470,16470\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,224,0.0048,3354000,16770,16770\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,228,0.0047,3414000,17070,17070\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,232,0.0048,3474000,17370,17370\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,236,0.0048,3534000,17670,17670\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,240,0.0049,3594000,17970,17970\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,244,0.0050,3654000,18270,18270\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,248,0.0052,3714000,18570,18570\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,252,0.0051,3774000,18870,18870\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,256,0.0052,3834000,19170,19170\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,260,0.0052,3894000,19470,19470\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,264,0.0053,3954000,19770,19770\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,268,0.0054,4014000,20070,20070\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,272,0.0054,4074000,20370,20370\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,276,0.0055,4134000,20670,20670\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,280,0.0056,4194000,20970,20970\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,284,0.0056,4254000,21270,21270\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,288,0.0057,4314000,21570,21570\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,292,0.0058,4374000,21870,21870\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,296,0.0058,4434000,22170,22170\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,300,0.0059,4494000,22470,22470\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,304,0.0059,4554000,22770,22770\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,308,0.0060,4614000,23070,23070\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,312,0.0061,4674000,23370,23370\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,316,0.0062,4734000,23670,23670\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,320,0.0062,4794000,23970,23970\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,324,0.0063,4854000,24270,24270\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,328,0.0063,4914000,24570,24570\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,332,0.0064,4974000,24870,24870\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,336,0.0065,5034000,25170,25170\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,340,0.0065,5094000,25470,25470\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,344,0.0066,5154000,25770,25770\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,348,0.0069,5214000,26070,26070\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,352,0.0068,5274000,26370,26370\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,356,0.0070,5334000,26670,26670\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,360,0.0069,5394000,26970,26970\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,364,0.0070,5454000,27270,27270\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,368,0.0070,5514000,27570,27570\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,372,0.0071,5574000,27870,27870\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,376,0.0073,5634000,28170,28170\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,380,0.0073,5694000,28470,28470\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,384,0.0073,5754000,28770,28770\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,388,0.0074,5814000,29070,29070\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,392,0.0074,5874000,29370,29370\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,396,0.0076,5934000,29670,29670\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,400,0.0075,5994000,29970,29970\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,404,0.0076,6054000,30270,30270\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,408,0.0077,6114000,30570,30570\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,412,0.0078,6174000,30870,30870\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,416,0.0079,6234000,31170,31170\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,420,0.0079,6294000,31470,31470\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,424,0.0079,6354000,31770,31770\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,428,0.0080,6414000,32070,32070\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,432,0.0080,6474000,32370,32370\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,436,0.0081,6534000,32670,32670\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,440,0.0082,6594000,32970,32970\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,444,0.0083,6654000,33270,33270\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,448,0.0084,6714000,33570,33570\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,452,0.0084,6774000,33870,33870\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,456,0.0084,6834000,34170,34170\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,460,0.0085,6894000,34470,34470\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,464,0.0086,6954000,34770,34770\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,468,0.0087,7014000,35070,35070\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,472,0.0088,7074000,35370,35370\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,476,0.0088,7134000,35670,35670\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,480,0.0089,7194000,35970,35970\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,484,0.0090,7254000,36270,36270\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,488,0.0091,7314000,36570,36570\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,492,0.0091,7374000,36870,36870\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,496,0.0091,7434000,37170,37170\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,500,0.0094,7494000,37470,37470\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,504,0.0093,7554000,37770,37770\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,508,0.0095,7614000,38070,38070\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,512,0.0096,7674000,38370,38370\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,516,0.0095,7734000,38670,38670\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,520,0.0095,7794000,38970,38970\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,524,0.0097,7854000,39270,39270\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,528,0.0097,7914000,39570,39570\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,532,0.0098,7974000,39870,39870\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,536,0.0098,8034000,40170,40170\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,540,0.0099,8094000,40470,40470\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,544,0.0100,8154000,40770,40770\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,548,0.0101,8214000,41070,41070\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,552,0.0101,8274000,41370,41370\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,556,0.0104,8334000,41670,41670\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,560,0.0103,8394000,41970,41970\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,564,0.0103,8454000,42270,42270\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,568,0.0106,8514000,42570,42570\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,572,0.0105,8574000,42870,42870\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,576,0.0106,8634000,43170,43170\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,580,0.0108,8694000,43470,43470\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,584,0.0109,8754000,43770,43770\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,588,0.0108,8814000,44070,44070\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,592,0.0109,8874000,44370,44370\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,596,0.0109,8934000,44670,44670\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,600,0.0110,8994000,44970,44970\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,604,0.0111,9054000,45270,45270\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,608,0.0112,9114000,45570,45570\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,612,0.0112,9174000,45870,45870\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,616,0.0114,9234000,46170,46170\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,620,0.0113,9294000,46470,46470\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,624,0.0114,9354000,46770,46770\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,628,0.0117,9414000,47070,47070\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,632,0.0116,9474000,47370,47370\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,636,0.0116,9534000,47670,47670\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,640,0.0117,9594000,47970,47970\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,644,0.0119,9654000,48270,48270\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,648,0.0118,9714000,48570,48570\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,652,0.0119,9774000,48870,48870\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,656,0.0119,9834000,49170,49170\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,660,0.0121,9894000,49470,49470\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,664,0.0122,9954000,49770,49770\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,668,0.0123,10014000,50070,50070\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,672,0.0122,10074000,50370,50370\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,676,0.0123,10134000,50670,50670\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,680,0.0123,10194000,50970,50970\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,684,0.0125,10254000,51270,51270\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,688,0.0125,10314000,51570,51570\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,692,0.0127,10374000,51870,51870\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,696,0.0126,10434000,52170,52170\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,700,0.0127,10494000,52470,52470\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,704,0.0128,10554000,52770,52770\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,708,0.0129,10614000,53070,53070\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,712,0.0128,10674000,53370,53370\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,716,0.0131,10734000,53670,53670\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,720,0.0130,10794000,53970,53970\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,724,0.0130,10854000,54270,54270\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,728,0.0132,10914000,54570,54570\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,732,0.0133,10974000,54870,54870\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,736,0.0135,11034000,55170,55170\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,740,0.0135,11094000,55470,55470\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,744,0.0135,11154000,55770,55770\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,748,0.0134,11214000,56070,56070\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,752,0.0135,11274000,56370,56370\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,756,0.0136,11334000,56670,56670\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,760,0.0137,11394000,56970,56970\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,764,0.0137,11454000,57270,57270\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,768,0.0138,11514000,57570,57570\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,772,0.0139,11574000,57870,57870\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,776,0.0141,11634000,58170,58170\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,780,0.0140,11694000,58470,58470\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,784,0.0142,11754000,58770,58770\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,788,0.0141,11814000,59070,59070\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,792,0.0142,11874000,59370,59370\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,796,0.0143,11934000,59670,59670\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,800,0.0143,11994000,59970,59970\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,804,0.0145,12054000,60270,60270\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,808,0.0145,12114000,60570,60570\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,812,0.0145,12174000,60870,60870\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,816,0.0148,12234000,61170,61170\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,820,0.0148,12294000,61470,61470\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,824,0.0148,12354000,61770,61770\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,828,0.0148,12414000,62070,62070\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,832,0.0149,12474000,62370,62370\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,836,0.0150,12534000,62670,62670\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,840,0.0150,12594000,62970,62970\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,844,0.0151,12654000,63270,63270\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,848,0.0153,12714000,63570,63570\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,852,0.0153,12774000,63870,63870\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,856,0.0153,12834000,64170,64170\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,860,0.0154,12894000,64470,64470\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,864,0.0154,12954000,64770,64770\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,868,0.0155,13014000,65070,65070\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,872,0.0157,13074000,65370,65370\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,876,0.0156,13134000,65670,65670\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,880,0.0157,13194000,65970,65970\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,884,0.0157,13254000,66270,66270\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,888,0.0158,13314000,66570,66570\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,892,0.0159,13374000,66870,66870\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,896,0.0160,13434000,67170,67170\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,900,0.0160,13494000,67470,67470\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,904,0.0162,13554000,67770,67770\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,908,0.0162,13614000,68070,68070\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,912,0.0163,13674000,68370,68370\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,916,0.0163,13734000,68670,68670\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,920,0.0164,13794000,68970,68970\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,924,0.0165,13854000,69270,69270\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,928,0.0166,13914000,69570,69570\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,932,0.0166,13974000,69870,69870\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,936,0.0167,14034000,70170,70170\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,940,0.0167,14094000,70470,70470\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,944,0.0168,14154000,70770,70770\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,948,0.0170,14214000,71070,71070\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,952,0.0171,14274000,71370,71370\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,956,0.0171,14334000,71670,71670\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,960,0.0171,14394000,71970,71970\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,964,0.0175,14454000,72270,72270\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,968,0.0176,14514000,72570,72570\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,972,0.0176,14574000,72870,72870\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,976,0.0175,14634000,73170,73170\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,980,0.0178,14694000,73470,73470\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,984,0.0180,14754000,73770,73770\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,988,0.0178,14814000,74070,74070\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,992,0.0179,14874000,74370,74370\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,996,0.0181,14934000,74670,74670\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,1000,0.0180,14994000,74970,74970\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,1004,0.0182,15054000,75270,75270\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,1008,0.0181,15114000,75570,75570\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,1012,0.0183,15174000,75870,75870\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,1016,0.0183,15234000,76170,76170\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,1020,0.0186,15294000,76470,76470\n", "iter,ny,nx,Runtime,PM_VECTOR_LD_CMPL (total),PM_VECTOR_LD_CMPL (min), PM_VECTOR_LD_CMPL (max)\n", "200,32,1024,0.0182,15354000,76770,76770\n", "mv /gpfs/wolf/trn003/scratch/aherten//poisson2d.vld.bin.csv .\n", "bsub -W 60 -nnodes 1 -Is -P TRN003 jsrun -n 1 -c 1 -g ALL_GPUS ./bench.sh poisson2d.vst.bin /gpfs/wolf/trn003/scratch/aherten//poisson2d.vst.bin.csv\n", "Job <24642> is submitted to default queue <batch>.\n", "<<Waiting for dispatch ...>>\n", "<<Starting on login1>>\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,4,0.0010,200,1,1\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,8,0.0011,18200,91,91\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,12,0.0012,30200,151,151\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,16,0.0012,42200,211,211\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,20,0.0013,54200,271,271\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,24,0.0013,66200,331,331\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,28,0.0014,78200,391,391\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,32,0.0015,90200,451,451\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,36,0.0015,102200,511,511\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,40,0.0016,114200,571,571\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,44,0.0017,126200,631,631\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,48,0.0017,138200,691,691\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,52,0.0018,150200,751,751\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,56,0.0019,162200,811,811\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,60,0.0020,174200,871,871\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,64,0.0020,186200,931,931\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,68,0.0022,198200,991,991\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,72,0.0023,210200,1051,1051\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,76,0.0022,222200,1111,1111\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,80,0.0023,234200,1171,1171\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,84,0.0024,246200,1231,1231\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,88,0.0024,258200,1291,1291\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,92,0.0025,270200,1351,1351\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,96,0.0025,282200,1411,1411\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,100,0.0026,294200,1471,1471\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,104,0.0027,306200,1531,1531\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,108,0.0028,318200,1591,1591\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,112,0.0028,330200,1651,1651\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,116,0.0029,342200,1711,1711\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,120,0.0030,354200,1771,1771\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,124,0.0030,366200,1831,1831\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,128,0.0031,378200,1891,1891\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,132,0.0032,390200,1951,1951\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,136,0.0032,402200,2011,2011\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,140,0.0033,414200,2071,2071\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,144,0.0033,426200,2131,2131\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,148,0.0035,438200,2191,2191\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,152,0.0035,450200,2251,2251\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,156,0.0035,462200,2311,2311\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,160,0.0036,474200,2371,2371\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,164,0.0038,486200,2431,2431\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,168,0.0037,498200,2491,2491\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,172,0.0038,510200,2551,2551\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,176,0.0038,522200,2611,2611\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,180,0.0039,534200,2671,2671\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,184,0.0040,546200,2731,2731\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,188,0.0040,558200,2791,2791\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,192,0.0041,570200,2851,2851\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,196,0.0042,582200,2911,2911\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,200,0.0044,594200,2971,2971\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,204,0.0043,606200,3031,3031\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,208,0.0044,618200,3091,3091\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,212,0.0044,630200,3151,3151\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,216,0.0045,642200,3211,3211\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,220,0.0046,654200,3271,3271\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,224,0.0046,666200,3331,3331\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,228,0.0047,678200,3391,3391\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,232,0.0048,690200,3451,3451\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,236,0.0048,702200,3511,3511\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,240,0.0049,714200,3571,3571\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,244,0.0050,726200,3631,3631\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,248,0.0050,738200,3691,3691\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,252,0.0051,750200,3751,3751\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,256,0.0052,762200,3811,3811\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,260,0.0052,774200,3871,3871\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,264,0.0053,786200,3931,3931\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,268,0.0054,798200,3991,3991\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,272,0.0054,810200,4051,4051\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,276,0.0055,822200,4111,4111\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,280,0.0055,834200,4171,4171\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,284,0.0056,846200,4231,4231\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,288,0.0057,858200,4291,4291\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,292,0.0057,870200,4351,4351\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,296,0.0058,882200,4411,4411\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,300,0.0059,894200,4471,4471\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,304,0.0059,906200,4531,4531\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,308,0.0060,918200,4591,4591\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,312,0.0061,930200,4651,4651\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,316,0.0061,942200,4711,4711\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,320,0.0062,954200,4771,4771\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,324,0.0063,966200,4831,4831\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,328,0.0063,978200,4891,4891\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,332,0.0064,990200,4951,4951\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,336,0.0065,1002200,5011,5011\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,340,0.0066,1014200,5071,5071\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,344,0.0066,1026200,5131,5131\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,348,0.0067,1038200,5191,5191\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,352,0.0069,1050200,5251,5251\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,356,0.0068,1062200,5311,5311\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,360,0.0068,1074200,5371,5371\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,364,0.0069,1086200,5431,5431\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,368,0.0070,1098200,5491,5491\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,372,0.0071,1110200,5551,5551\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,376,0.0071,1122200,5611,5611\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,380,0.0072,1134200,5671,5671\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,384,0.0073,1146200,5731,5731\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,388,0.0073,1158200,5791,5791\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,392,0.0074,1170200,5851,5851\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,396,0.0075,1182200,5911,5911\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,400,0.0075,1194200,5971,5971\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,404,0.0076,1206200,6031,6031\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,408,0.0077,1218200,6091,6091\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,412,0.0077,1230200,6151,6151\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,416,0.0080,1242200,6211,6211\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,420,0.0078,1254200,6271,6271\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,424,0.0079,1266200,6331,6331\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,428,0.0080,1278200,6391,6391\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,432,0.0081,1290200,6451,6451\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,436,0.0082,1302200,6511,6511\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,440,0.0082,1314200,6571,6571\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,444,0.0083,1326200,6631,6631\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,448,0.0083,1338200,6691,6691\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,452,0.0084,1350200,6751,6751\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,456,0.0085,1362200,6811,6811\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,460,0.0085,1374200,6871,6871\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,464,0.0087,1386200,6931,6931\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,468,0.0086,1398200,6991,6991\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,472,0.0087,1410200,7051,7051\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,476,0.0088,1422200,7111,7111\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,480,0.0090,1434200,7171,7171\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,484,0.0089,1446200,7231,7231\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,488,0.0090,1458200,7291,7291\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,492,0.0092,1470200,7351,7351\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,496,0.0092,1482200,7411,7411\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,500,0.0092,1494200,7471,7471\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,504,0.0093,1506200,7531,7531\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,508,0.0094,1518200,7591,7591\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,512,0.0095,1530200,7651,7651\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,516,0.0096,1542200,7711,7711\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,520,0.0096,1554200,7771,7771\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,524,0.0096,1566200,7831,7831\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,528,0.0097,1578200,7891,7891\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,532,0.0097,1590200,7951,7951\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,536,0.0098,1602200,8011,8011\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,540,0.0100,1614200,8071,8071\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,544,0.0099,1626200,8131,8131\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,548,0.0100,1638200,8191,8191\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,552,0.0101,1650200,8251,8251\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,556,0.0102,1662200,8311,8311\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,560,0.0102,1674200,8371,8371\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,564,0.0105,1686200,8431,8431\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,568,0.0104,1698200,8491,8491\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,572,0.0105,1710200,8551,8551\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,576,0.0105,1722200,8611,8611\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,580,0.0108,1734200,8671,8671\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,584,0.0108,1746200,8731,8731\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,588,0.0109,1758200,8791,8791\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,592,0.0109,1770200,8851,8851\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,596,0.0109,1782200,8911,8911\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,600,0.0111,1794200,8971,8971\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,604,0.0111,1806200,9031,9031\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,608,0.0112,1818200,9091,9091\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,612,0.0112,1830200,9151,9151\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,616,0.0114,1842200,9211,9211\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,620,0.0113,1854200,9271,9271\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,624,0.0114,1866200,9331,9331\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,628,0.0114,1878200,9391,9391\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,632,0.0116,1890200,9451,9451\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,636,0.0116,1902200,9511,9511\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,640,0.0117,1914200,9571,9571\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,644,0.0118,1926200,9631,9631\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,648,0.0118,1938200,9691,9691\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,652,0.0121,1950200,9751,9751\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,656,0.0121,1962200,9811,9811\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,660,0.0121,1974200,9871,9871\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,664,0.0121,1986200,9931,9931\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,668,0.0122,1998200,9991,9991\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,672,0.0122,2010200,10051,10051\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,676,0.0124,2022200,10111,10111\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,680,0.0123,2034200,10171,10171\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,684,0.0124,2046200,10231,10231\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,688,0.0126,2058200,10291,10291\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,692,0.0127,2070200,10351,10351\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,696,0.0126,2082200,10411,10411\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,700,0.0128,2094200,10471,10471\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,704,0.0127,2106200,10531,10531\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,708,0.0128,2118200,10591,10591\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,712,0.0129,2130200,10651,10651\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,716,0.0130,2142200,10711,10711\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,720,0.0130,2154200,10771,10771\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,724,0.0131,2166200,10831,10831\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,728,0.0131,2178200,10891,10891\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,732,0.0132,2190200,10951,10951\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,736,0.0134,2202200,11011,11011\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,740,0.0134,2214200,11071,11071\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,744,0.0134,2226200,11131,11131\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,748,0.0135,2238200,11191,11191\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,752,0.0136,2250200,11251,11251\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,756,0.0136,2262200,11311,11311\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,760,0.0137,2274200,11371,11371\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,764,0.0138,2286200,11431,11431\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,768,0.0138,2298200,11491,11491\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,772,0.0139,2310200,11551,11551\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,776,0.0139,2322200,11611,11611\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,780,0.0140,2334200,11671,11671\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,784,0.0141,2346200,11731,11731\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,788,0.0142,2358200,11791,11791\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,792,0.0142,2370200,11851,11851\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,796,0.0144,2382200,11911,11911\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,800,0.0144,2394200,11971,11971\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,804,0.0144,2406200,12031,12031\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,808,0.0146,2418200,12091,12091\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,812,0.0146,2430200,12151,12151\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,816,0.0146,2442200,12211,12211\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,820,0.0147,2454200,12271,12271\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,824,0.0148,2466200,12331,12331\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,828,0.0149,2478200,12391,12391\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,832,0.0149,2490200,12451,12451\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,836,0.0150,2502200,12511,12511\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,840,0.0151,2514200,12571,12571\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,844,0.0152,2526200,12631,12631\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,848,0.0151,2538200,12691,12691\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,852,0.0152,2550200,12751,12751\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,856,0.0153,2562200,12811,12811\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,860,0.0154,2574200,12871,12871\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,864,0.0155,2586200,12931,12931\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,868,0.0155,2598200,12991,12991\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,872,0.0156,2610200,13051,13051\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,876,0.0156,2622200,13111,13111\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,880,0.0157,2634200,13171,13171\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,884,0.0158,2646200,13231,13231\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,888,0.0159,2658200,13291,13291\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,892,0.0159,2670200,13351,13351\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,896,0.0160,2682200,13411,13411\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,900,0.0160,2694200,13471,13471\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,904,0.0162,2706200,13531,13531\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,908,0.0162,2718200,13591,13591\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,912,0.0163,2730200,13651,13651\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,916,0.0163,2742200,13711,13711\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,920,0.0164,2754200,13771,13771\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,924,0.0165,2766200,13831,13831\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,928,0.0166,2778200,13891,13891\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,932,0.0168,2790200,13951,13951\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,936,0.0167,2802200,14011,14011\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,940,0.0169,2814200,14071,14071\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,944,0.0169,2826200,14131,14131\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,948,0.0169,2838200,14191,14191\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,952,0.0170,2850200,14251,14251\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,956,0.0170,2862200,14311,14311\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,960,0.0171,2874200,14371,14371\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,964,0.0175,2886200,14431,14431\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,968,0.0175,2898200,14491,14491\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,972,0.0176,2910200,14551,14551\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,976,0.0176,2922200,14611,14611\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,980,0.0178,2934200,14671,14671\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,984,0.0178,2946200,14731,14731\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,988,0.0179,2958200,14791,14791\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,992,0.0178,2970200,14851,14851\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,996,0.0181,2982200,14911,14911\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,1000,0.0180,2994200,14971,14971\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,1004,0.0181,3006200,15031,15031\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,1008,0.0182,3018200,15091,15091\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,1012,0.0183,3030200,15151,15151\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,1016,0.0183,3042200,15211,15211\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,1020,0.0184,3054200,15271,15271\n", "iter,ny,nx,Runtime,PM_VECTOR_ST_CMPL (total),PM_VECTOR_ST_CMPL (min), PM_VECTOR_ST_CMPL (max)\n", "200,32,1024,0.0182,3066200,15331,15331\n", "mv /gpfs/wolf/trn003/scratch/aherten//poisson2d.vst.bin.csv .\n"]}], "source": ["!make bench_task3"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Let's plot it again, as soon as the run finishes! Non-interactively, call `graph_task2b`.\n", "\n", "*Because we couldn't measure the two vector counters at the same time, we have two CSV files to read in now. We combine them into one common dataframe `df_vldvst` in the following.*"]}, {"cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": ["df_vld = pd.read_csv(\"poisson2d.vld.bin.csv\", skiprows=range(2, 50000, 2))\n", "df_vst = pd.read_csv(\"poisson2d.vst.bin.csv\", skiprows=range(2, 50000, 2))\n", "df_vldvst = pd.concat([df_vld.set_index(\"nx\"), df_vst.set_index(\"nx\")[['PM_VECTOR_ST_CMPL (total)', 'PM_VECTOR_ST_CMPL (min)', ' PM_VECTOR_ST_CMPL (max)']]], axis=1).reset_index()"]}, {"cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>nx</th>\n", " <th>iter</th>\n", " <th>ny</th>\n", " <th>Runtime</th>\n", " <th>PM_VECTOR_LD_CMPL (total)</th>\n", " <th>PM_VECTOR_LD_CMPL (min)</th>\n", " <th>PM_VECTOR_LD_CMPL (max)</th>\n", " <th>PM_VECTOR_ST_CMPL (total)</th>\n", " <th>PM_VECTOR_ST_CMPL (min)</th>\n", " <th>PM_VECTOR_ST_CMPL (max)</th>\n", " <th>Grid Points</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>4</td>\n", " <td>200</td>\n", " <td>32</td>\n", " <td>0.0010</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>200</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>128</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>8</td>\n", " <td>200</td>\n", " <td>32</td>\n", " <td>0.0011</td>\n", " <td>114000</td>\n", " <td>570</td>\n", " <td>570</td>\n", " <td>18200</td>\n", " <td>91</td>\n", " <td>91</td>\n", " <td>256</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>12</td>\n", " <td>200</td>\n", " <td>32</td>\n", " <td>0.0012</td>\n", " <td>174000</td>\n", " <td>870</td>\n", " <td>870</td>\n", " <td>30200</td>\n", " <td>151</td>\n", " <td>151</td>\n", " <td>384</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>16</td>\n", " <td>200</td>\n", " <td>32</td>\n", " <td>0.0012</td>\n", " <td>234000</td>\n", " <td>1170</td>\n", " <td>1170</td>\n", " <td>42200</td>\n", " <td>211</td>\n", " <td>211</td>\n", " <td>512</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>20</td>\n", " <td>200</td>\n", " <td>32</td>\n", " <td>0.0013</td>\n", " <td>294000</td>\n", " <td>1470</td>\n", " <td>1470</td>\n", " <td>54200</td>\n", " <td>271</td>\n", " <td>271</td>\n", " <td>640</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>"], "text/plain": [" nx iter ny Runtime PM_VECTOR_LD_CMPL (total) PM_VECTOR_LD_CMPL (min) \\\n", "0 4 200 32 0.0010 0 0 \n", "1 8 200 32 0.0011 114000 570 \n", "2 12 200 32 0.0012 174000 870 \n", "3 16 200 32 0.0012 234000 1170 \n", "4 20 200 32 0.0013 294000 1470 \n", "\n", " PM_VECTOR_LD_CMPL (max) PM_VECTOR_ST_CMPL (total) \\\n", "0 0 200 \n", "1 570 18200 \n", "2 870 30200 \n", "3 1170 42200 \n", "4 1470 54200 \n", "\n", " PM_VECTOR_ST_CMPL (min) PM_VECTOR_ST_CMPL (max) Grid Points \n", "0 1 1 128 \n", "1 91 91 256 \n", "2 151 151 384 \n", "3 211 211 512 \n", "4 271 271 640 "]}, "execution_count": 32, "metadata": {}, "output_type": "execute_result"}], "source": ["df_vldvst[\"Grid Points\"] = df_vldvst[\"nx\"] * df_vldvst[\"ny\"] \n", "df_vldvst.head()"]}, {"cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 1008x432 with 2 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["fig, (ax1, ax2) = plt.subplots(nrows=2, sharex=True)\n", "df_vldvst.set_index(\"Grid Points\")[\"PM_VECTOR_LD_CMPL (min)\"].plot(ax=ax1, legend=True);\n", "df_vldvst.set_index(\"Grid Points\")[\"PM_VECTOR_ST_CMPL (min)\"].plot(ax=ax2, legend=True);"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Also here seems to be a linear correlation. Let's do our fitting and plot directly."]}, {"cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Counter PM_VECTOR_LD_CMPL (min) is proportional to the grid points (nx*ny) by a factor of 2.3439 (\u00b1 0.000111)\n", "Counter PM_VECTOR_ST_CMPL (min) is proportional to the grid points (nx*ny) by a factor of 0.4688 (\u00b1 0.000012)\n"]}], "source": ["_fit, _cov = common.print_and_return_fit(\n", " [\"PM_VECTOR_LD_CMPL (min)\", \"PM_VECTOR_ST_CMPL (min)\"], \n", " df_vldvst.set_index(\"Grid Points\"), \n", " linear_function,\n", " format_value=\".4f\",\n", ")\n", "fit_parameters = {**fit_parameters, **_fit}\n", "fit_covariance = {**fit_covariance, **_cov}"]}, {"cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 1008x432 with 2 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["fig, (ax1, ax2) = plt.subplots(nrows=2, sharex=True)\n", "for ax, pmu_counter in zip([ax1, ax2], [\"PM_VECTOR_LD_CMPL (min)\", \"PM_VECTOR_ST_CMPL (min)\"]):\n", " df_vldvst.set_index(\"Grid Points\")[pmu_counter].plot(ax=ax, legend=True);\n", " ax.plot(\n", " df_vldvst[\"Grid Points\"], \n", " linear_function(df[\"Grid Points\"], *fit_parameters[pmu_counter]), \n", " linestyle=\"--\", \n", " label=\"Fit: {:.2f} * x + {:.2f}\".format(*fit_parameters[pmu_counter])\n", " )\n", " ax.legend();"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Let's try to make sense of those numbers.\n", "\n", "Vector loads and vector stores use two 8 Byte values at a time. When we measured loads and stores with `LD_CMPL` and `ST_CMPL` in part A of this task, we measured total number of stores and loads; that is: vector and scalar versions of the instructions. In order to convert the load and store instructions into **bytes** loaded and stored, we need to separate them. The difference of total instructions and vector instructions yield scalar instructions. We multiply the scalar instructions by 8 Byte (double precision) and the vector instructions by 16 Byte (two loads or stores of double precision). That yields the loaded or stored data (or, more precisely, the instruction-equivalent data).\n", "\n", "To formualize it, see the following equations, as an example for load ($ld$), with $b$ denoting data loaded in bytes and $n$ denoting the number of instructions.\n", "\n", "\\begin{align}\n", "b_\\text{ld} &= b_\\text{ld}^\\text{scalar} + b_\\text{ld}^\\text{vector}\\\\\n", "b_\\text{ld}^\\text{scalar} &= n_\\text{ld}^\\text{scalar} * 8\\,\\text{Byte} \\\\\n", "b_\\text{ld}^\\text{vector} &= n_\\text{ld}^\\text{vector} * 16\\,\\text{Byte} \\\\\n", "n_\\text{ld}^\\text{scalar} &= n_\\text{ld}^\\text{total} - n_\\text{ld}^\\text{vector}\\\\\n", "\\Rightarrow b_\\text{ld} &= n_\\text{ld}^\\text{scalar}* 8 \\,\\text{Byte} + n_\\text{ld}^\\text{vector} * 16\\,\\text{Byte} \\\\\n", "& = (n_\\text{ld}^\\text{scalar}+2 n_\\text{ld}^\\text{vector}) * 8\\,Byte \\\\\n", "& = (n_\\text{ld}^\\text{total} - n_\\text{ld}^\\text{vector} + 2 n_\\text{ld}^\\text{vector}) * 8\\,Byte \\\\\n", "& = (n_\\text{ld}^\\text{total} + n_\\text{ld}^\\text{vector}) *8\\,Byte \n", "\\end{align}\n", "\n", "We are going to print this in the next cell. In case you look at this Notebook non-interactively, call `graph_task2b-2`."]}, {"cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 1008x432 with 1 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["df_byte = pd.DataFrame()\n", "df_byte[\"Loads\"] = (df_vldvst.set_index(\"Grid Points\")[\"PM_VECTOR_LD_CMPL (min)\"] + df_ldst.set_index(\"Grid Points\")[\"PM_LD_CMPL (min)\"])*8\n", "df_byte[\"Stores\"] = (df_vldvst.set_index(\"Grid Points\")[\"PM_VECTOR_ST_CMPL (min)\"] + df_ldst.set_index(\"Grid Points\")[\"PM_ST_CMPL (min)\"])*8\n", "ax = df_byte.plot()\n", "ax.set_ylabel(\"Bytes\");"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Let's quantify the difference by, again, fitting a linear function to the data."]}, {"cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Counter Loads is proportional to the grid points (nx*ny) by a factor of 37.5010 (\u00b1 0.000592)\n", "Counter Stores is proportional to the grid points (nx*ny) by a factor of 8.4379 (\u00b1 0.000247)\n"]}], "source": ["_fit, _cov = common.print_and_return_fit(\n", " [\"Loads\", \"Stores\"], \n", " df_byte, \n", " linear_function\n", ")\n", "fit_parameters = {**fit_parameters, **_fit}\n", "fit_covariance = {**fit_covariance, **_cov}"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Analagously to the proportionality factors, this mich is loaded/stored per grid point."]}, {"cell_type": "markdown", "metadata": {}, "source": ["*Not really a* <a name=\"task2-c\"></a>**TASK C**: We can combine this information with the cycles measured in Task 1 to create a bandwidth of exchanged bytes per cycle."]}, {"cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [], "source": ["df_bandwidth = pd.DataFrame()\n", "df_bandwidth[\"Bandwidth / Byte/Cycle\"] = (df_byte[\"Loads\"] + df_byte[\"Stores\"]) / df.set_index(\"Grid Points\")[\"PM_RUN_CYC (min)\"]"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Let's display it as a function of grid points. And also compare it to the available L1 cache bandwidth in a second (sub-)plot. Non-interactive users, call `make graph_task2c`."]}, {"cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 1008x432 with 2 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["fig, (ax1, ax2) = plt.subplots(nrows=2, sharex=True)\n", "for ax in [ax1, ax2]:\n", " df_bandwidth[\"Bandwidth / Byte/Cycle\"].plot(ax=ax, legend=True, label=\"Jacobi Bandwidth\")\n", " ax.set_ylabel(\"Byte/Cycle\")\n", "ax2.axhline(2*16, color=sns.color_palette()[1], label=\"L1 Bandwidth\");\n", "ax2.legend();"]}, {"cell_type": "markdown", "metadata": {}, "source": ["As you can see, we are quite a bit away from the available L1 cache bandwidth. Can you think of reasons why?"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Task E1: Measuring FlOps\n", "<a name=\"taske1\"></a>\n", "\n", "If you still have time, feel free to work on the following extended task.\n", "\n", "\n", "**TASK**: Please measure counters for _vectorized_ floating point operations and _scalar_ floating point operations. The two counters can also not be measured during the same run. So please see the TODOs in [`poisson2d.sflops.c`](/edit/Tasks/poisson2d.sflops.c) and [`poisson2d.vflops.c`](/edit/Tasks/poisson2d.vflops.c). By now you should be able to find out the names of the counters by yourself (*Hint: they include the words \u00bbscalar\u00ab and \u00bbvector\u00ab\u2026*).\n", "\n", "As usual, compile, test, and bench-run your program.\n", "\n", "[Back to top](#toc)"]}, {"cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["bsub -W 60 -nnodes 1 -Is -P TRN003 jsrun -n 1 -c 1 -g ALL_GPUS ./bench.sh poisson2d.sflop.bin /gpfs/wolf/trn003/scratch/aherten//poisson2d.sflop.bin.csv\n", "Job <24645> is submitted to default queue <batch>.\n", "<<Waiting for dispatch ...>>\n", "<<Starting on login1>>\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,4,0.0010,96000,480,480\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,8,0.0011,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,12,0.0012,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,16,0.0012,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,20,0.0013,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,24,0.0013,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,28,0.0014,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,32,0.0015,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,36,0.0015,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,40,0.0016,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,44,0.0017,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,48,0.0017,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,52,0.0018,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,56,0.0022,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,60,0.0019,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,64,0.0021,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,68,0.0022,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,72,0.0021,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,76,0.0022,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,80,0.0023,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,84,0.0025,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,88,0.0024,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,92,0.0025,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,96,0.0025,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,100,0.0026,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,104,0.0027,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,108,0.0027,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,112,0.0028,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,116,0.0028,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,120,0.0031,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,124,0.0030,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,128,0.0030,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,132,0.0031,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,136,0.0032,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,140,0.0032,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,144,0.0033,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,148,0.0034,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,152,0.0035,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,156,0.0035,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,160,0.0036,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,164,0.0036,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,168,0.0037,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,172,0.0038,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,176,0.0038,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,180,0.0039,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,184,0.0040,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,188,0.0040,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,192,0.0041,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,196,0.0042,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,200,0.0042,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,204,0.0043,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,208,0.0043,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,212,0.0044,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,216,0.0045,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,220,0.0045,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,224,0.0046,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,228,0.0047,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,232,0.0047,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,236,0.0048,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,240,0.0049,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,244,0.0049,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,248,0.0051,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,252,0.0051,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,256,0.0053,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,260,0.0052,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,264,0.0053,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,268,0.0054,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,272,0.0054,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,276,0.0054,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,280,0.0055,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,284,0.0056,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,288,0.0056,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,292,0.0057,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,296,0.0058,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,300,0.0058,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,304,0.0059,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,308,0.0060,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,312,0.0060,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,316,0.0062,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,320,0.0062,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,324,0.0062,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,328,0.0063,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,332,0.0064,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,336,0.0065,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,340,0.0065,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,344,0.0066,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,348,0.0066,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,352,0.0067,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,356,0.0068,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,360,0.0069,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,364,0.0069,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,368,0.0070,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,372,0.0072,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,376,0.0071,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,380,0.0071,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,384,0.0072,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,388,0.0073,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,392,0.0074,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,396,0.0076,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,400,0.0075,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,404,0.0076,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,408,0.0076,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,412,0.0077,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,416,0.0078,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,420,0.0078,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,424,0.0079,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,428,0.0079,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,432,0.0080,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,436,0.0081,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,440,0.0082,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,444,0.0082,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,448,0.0084,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,452,0.0083,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,456,0.0084,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,460,0.0085,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,464,0.0085,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,468,0.0086,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,472,0.0087,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,476,0.0089,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,480,0.0088,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,484,0.0089,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,488,0.0089,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,492,0.0090,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,496,0.0091,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,500,0.0092,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,504,0.0092,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,508,0.0093,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,512,0.0094,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,516,0.0094,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,520,0.0095,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,524,0.0096,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,528,0.0096,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,532,0.0098,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,536,0.0097,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,540,0.0098,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,544,0.0099,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,548,0.0100,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,552,0.0101,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,556,0.0101,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,560,0.0102,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,564,0.0103,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,568,0.0104,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,572,0.0105,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,576,0.0105,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,580,0.0106,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,584,0.0107,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,588,0.0107,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,592,0.0108,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,596,0.0109,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,600,0.0110,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,604,0.0111,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,608,0.0111,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,612,0.0112,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,616,0.0112,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,620,0.0113,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,624,0.0114,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,628,0.0115,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,632,0.0115,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,636,0.0115,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,640,0.0116,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,644,0.0118,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,648,0.0117,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,652,0.0119,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,656,0.0119,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,660,0.0121,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,664,0.0120,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,668,0.0122,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,672,0.0121,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,676,0.0124,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,680,0.0123,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,684,0.0125,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,688,0.0124,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,692,0.0125,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,696,0.0126,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,700,0.0127,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,704,0.0126,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,708,0.0127,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,712,0.0129,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,716,0.0128,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,720,0.0129,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,724,0.0132,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,728,0.0131,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,732,0.0131,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,736,0.0133,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,740,0.0133,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,744,0.0133,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,748,0.0134,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,752,0.0136,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,756,0.0136,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,760,0.0136,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,764,0.0136,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,768,0.0138,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,772,0.0138,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,776,0.0139,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,780,0.0139,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,784,0.0140,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,788,0.0140,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,792,0.0141,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,796,0.0142,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,800,0.0143,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,804,0.0143,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,808,0.0144,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,812,0.0144,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,816,0.0145,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,820,0.0146,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,824,0.0148,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,828,0.0147,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,832,0.0148,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,836,0.0149,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,840,0.0150,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,844,0.0150,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,848,0.0150,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,852,0.0151,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,856,0.0152,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,860,0.0152,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,864,0.0153,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,868,0.0154,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,872,0.0156,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,876,0.0156,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,880,0.0156,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,884,0.0157,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,888,0.0157,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,892,0.0158,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,896,0.0159,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,900,0.0159,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,904,0.0161,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,908,0.0162,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,912,0.0164,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,916,0.0163,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,920,0.0164,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,924,0.0165,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,928,0.0166,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,932,0.0166,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,936,0.0167,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,940,0.0167,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,944,0.0168,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,948,0.0169,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,952,0.0172,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,956,0.0171,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,960,0.0172,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,964,0.0175,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,968,0.0175,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,972,0.0176,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,976,0.0177,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,980,0.0178,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,984,0.0178,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,988,0.0179,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,992,0.0179,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,996,0.0182,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,1000,0.0181,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,1004,0.0182,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,1008,0.0182,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,1012,0.0184,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,1016,0.0184,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,1020,0.0186,0,0,0\n", "iter,ny,nx,Runtime,PM_SCALAR_FLOP_CMPL (total),PM_SCALAR_FLOP_CMPL (min), PM_SCALAR_FLOP_CMPL (max)\n", "200,32,1024,0.0182,0,0,0\n", "mv /gpfs/wolf/trn003/scratch/aherten//poisson2d.sflop.bin.csv .\n", "bsub -W 60 -nnodes 1 -Is -P TRN003 jsrun -n 1 -c 1 -g ALL_GPUS ./bench.sh poisson2d.vflop.bin /gpfs/wolf/trn003/scratch/aherten//poisson2d.vflop.bin.csv\n", "Job <24646> is submitted to default queue <batch>.\n", "<<Waiting for dispatch ...>>\n", "<<Starting on login1>>\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,4,0.0010,0,0,0\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,8,0.0011,150000,750,750\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,12,0.0012,246000,1230,1230\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,16,0.0012,342000,1710,1710\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,20,0.0013,438000,2190,2190\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,24,0.0013,534000,2670,2670\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,28,0.0014,630000,3150,3150\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,32,0.0015,726000,3630,3630\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,36,0.0016,822000,4110,4110\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,40,0.0016,918000,4590,4590\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,44,0.0017,1014000,5070,5070\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,48,0.0017,1110000,5550,5550\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,52,0.0018,1206000,6030,6030\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,56,0.0019,1302000,6510,6510\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,60,0.0019,1398000,6990,6990\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,64,0.0020,1494000,7470,7470\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,68,0.0022,1590000,7950,7950\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,72,0.0021,1686000,8430,8430\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,76,0.0022,1782000,8910,8910\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,80,0.0023,1878000,9390,9390\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,84,0.0025,1974000,9870,9870\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,88,0.0024,2070000,10350,10350\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,92,0.0026,2166000,10830,10830\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,96,0.0025,2262000,11310,11310\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,100,0.0026,2358000,11790,11790\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,104,0.0027,2454000,12270,12270\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,108,0.0027,2550000,12750,12750\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,112,0.0029,2646000,13230,13230\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,116,0.0029,2742000,13710,13710\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,120,0.0029,2838000,14190,14190\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,124,0.0030,2934000,14670,14670\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,128,0.0031,3030000,15150,15150\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,132,0.0031,3126000,15630,15630\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,136,0.0032,3222000,16110,16110\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,140,0.0032,3318000,16590,16590\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,144,0.0033,3414000,17070,17070\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,148,0.0036,3510000,17550,17550\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,152,0.0035,3606000,18030,18030\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,156,0.0035,3702000,18510,18510\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,160,0.0036,3798000,18990,18990\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,164,0.0036,3894000,19470,19470\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,168,0.0037,3990000,19950,19950\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,172,0.0038,4086000,20430,20430\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,176,0.0038,4182000,20910,20910\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,180,0.0039,4278000,21390,21390\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,184,0.0040,4374000,21870,21870\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,188,0.0041,4470000,22350,22350\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,192,0.0041,4566000,22830,22830\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,196,0.0042,4662000,23310,23310\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,200,0.0042,4758000,23790,23790\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,204,0.0043,4854000,24270,24270\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,208,0.0044,4950000,24750,24750\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,212,0.0044,5046000,25230,25230\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,216,0.0045,5142000,25710,25710\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,220,0.0046,5238000,26190,26190\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,224,0.0046,5334000,26670,26670\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,228,0.0048,5430000,27150,27150\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,232,0.0049,5526000,27630,27630\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,236,0.0048,5622000,28110,28110\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,240,0.0049,5718000,28590,28590\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,244,0.0049,5814000,29070,29070\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,248,0.0050,5910000,29550,29550\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,252,0.0051,6006000,30030,30030\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,256,0.0051,6102000,30510,30510\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,260,0.0052,6198000,30990,30990\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,264,0.0053,6294000,31470,31470\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,268,0.0054,6390000,31950,31950\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,272,0.0054,6486000,32430,32430\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,276,0.0054,6582000,32910,32910\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,280,0.0055,6678000,33390,33390\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,284,0.0056,6774000,33870,33870\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,288,0.0057,6870000,34350,34350\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,292,0.0057,6966000,34830,34830\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,296,0.0058,7062000,35310,35310\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,300,0.0059,7158000,35790,35790\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,304,0.0059,7254000,36270,36270\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,308,0.0060,7350000,36750,36750\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,312,0.0062,7446000,37230,37230\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,316,0.0061,7542000,37710,37710\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,320,0.0062,7638000,38190,38190\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,324,0.0062,7734000,38670,38670\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,328,0.0063,7830000,39150,39150\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,332,0.0064,7926000,39630,39630\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,336,0.0065,8022000,40110,40110\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,340,0.0065,8118000,40590,40590\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,344,0.0066,8214000,41070,41070\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,348,0.0066,8310000,41550,41550\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,352,0.0067,8406000,42030,42030\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,356,0.0068,8502000,42510,42510\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,360,0.0068,8598000,42990,42990\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,364,0.0069,8694000,43470,43470\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,368,0.0070,8790000,43950,43950\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,372,0.0070,8886000,44430,44430\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,376,0.0071,8982000,44910,44910\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,380,0.0072,9078000,45390,45390\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,384,0.0072,9174000,45870,45870\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,388,0.0073,9270000,46350,46350\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,392,0.0074,9366000,46830,46830\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,396,0.0074,9462000,47310,47310\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,400,0.0075,9558000,47790,47790\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,404,0.0075,9654000,48270,48270\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,408,0.0076,9750000,48750,48750\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,412,0.0077,9846000,49230,49230\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,416,0.0079,9942000,49710,49710\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,420,0.0078,10038000,50190,50190\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,424,0.0080,10134000,50670,50670\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,428,0.0080,10230000,51150,51150\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,432,0.0080,10326000,51630,51630\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,436,0.0083,10422000,52110,52110\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,440,0.0082,10518000,52590,52590\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,444,0.0083,10614000,53070,53070\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,448,0.0083,10710000,53550,53550\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,452,0.0083,10806000,54030,54030\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,456,0.0084,10902000,54510,54510\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,460,0.0085,10998000,54990,54990\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,464,0.0085,11094000,55470,55470\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,468,0.0086,11190000,55950,55950\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,472,0.0087,11286000,56430,56430\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,476,0.0087,11382000,56910,56910\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,480,0.0088,11478000,57390,57390\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,484,0.0089,11574000,57870,57870\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,488,0.0089,11670000,58350,58350\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,492,0.0091,11766000,58830,58830\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,496,0.0091,11862000,59310,59310\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,500,0.0091,11958000,59790,59790\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,504,0.0092,12054000,60270,60270\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,508,0.0093,12150000,60750,60750\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,512,0.0094,12246000,61230,61230\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,516,0.0096,12342000,61710,61710\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,520,0.0096,12438000,62190,62190\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,524,0.0095,12534000,62670,62670\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,528,0.0098,12630000,63150,63150\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,532,0.0097,12726000,63630,63630\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,536,0.0097,12822000,64110,64110\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,540,0.0098,12918000,64590,64590\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,544,0.0100,13014000,65070,65070\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,548,0.0102,13110000,65550,65550\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,552,0.0102,13206000,66030,66030\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,556,0.0101,13302000,66510,66510\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,560,0.0103,13398000,66990,66990\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,564,0.0103,13494000,67470,67470\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,568,0.0104,13590000,67950,67950\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,572,0.0105,13686000,68430,68430\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,576,0.0105,13782000,68910,68910\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,580,0.0107,13878000,69390,69390\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,584,0.0108,13974000,69870,69870\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,588,0.0107,14070000,70350,70350\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,592,0.0108,14166000,70830,70830\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,596,0.0109,14262000,71310,71310\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,600,0.0110,14358000,71790,71790\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,604,0.0110,14454000,72270,72270\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,608,0.0111,14550000,72750,72750\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,612,0.0114,14646000,73230,73230\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,616,0.0112,14742000,73710,73710\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,620,0.0113,14838000,74190,74190\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,624,0.0114,14934000,74670,74670\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,628,0.0116,15030000,75150,75150\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,632,0.0115,15126000,75630,75630\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,636,0.0117,15222000,76110,76110\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,640,0.0116,15318000,76590,76590\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,644,0.0118,15414000,77070,77070\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,648,0.0117,15510000,77550,77550\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,652,0.0119,15606000,78030,78030\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,656,0.0119,15702000,78510,78510\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,660,0.0120,15798000,78990,78990\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,664,0.0120,15894000,79470,79470\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,668,0.0121,15990000,79950,79950\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,672,0.0121,16086000,80430,80430\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,676,0.0123,16182000,80910,80910\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,680,0.0122,16278000,81390,81390\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,684,0.0125,16374000,81870,81870\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,688,0.0124,16470000,82350,82350\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,692,0.0126,16566000,82830,82830\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,696,0.0125,16662000,83310,83310\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,700,0.0127,16758000,83790,83790\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,704,0.0128,16854000,84270,84270\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,708,0.0128,16950000,84750,84750\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,712,0.0128,17046000,85230,85230\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,716,0.0128,17142000,85710,85710\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,720,0.0129,17238000,86190,86190\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,724,0.0130,17334000,86670,86670\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,728,0.0130,17430000,87150,87150\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,732,0.0132,17526000,87630,87630\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,736,0.0132,17622000,88110,88110\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,740,0.0133,17718000,88590,88590\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,744,0.0133,17814000,89070,89070\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,748,0.0134,17910000,89550,89550\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,752,0.0134,18006000,90030,90030\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,756,0.0136,18102000,90510,90510\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,760,0.0136,18198000,90990,90990\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,764,0.0136,18294000,91470,91470\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,768,0.0137,18390000,91950,91950\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,772,0.0139,18486000,92430,92430\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,776,0.0139,18582000,92910,92910\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,780,0.0139,18678000,93390,93390\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,784,0.0140,18774000,93870,93870\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,788,0.0140,18870000,94350,94350\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,792,0.0142,18966000,94830,94830\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,796,0.0142,19062000,95310,95310\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,800,0.0144,19158000,95790,95790\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,804,0.0143,19254000,96270,96270\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,808,0.0144,19350000,96750,96750\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,812,0.0145,19446000,97230,97230\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,816,0.0145,19542000,97710,97710\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,820,0.0146,19638000,98190,98190\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,824,0.0147,19734000,98670,98670\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,828,0.0147,19830000,99150,99150\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,832,0.0148,19926000,99630,99630\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,836,0.0151,20022000,100110,100110\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,840,0.0150,20118000,100590,100590\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,844,0.0150,20214000,101070,101070\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,848,0.0151,20310000,101550,101550\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,852,0.0152,20406000,102030,102030\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,856,0.0152,20502000,102510,102510\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,860,0.0152,20598000,102990,102990\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,864,0.0153,20694000,103470,103470\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,868,0.0154,20790000,103950,103950\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,872,0.0155,20886000,104430,104430\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,876,0.0155,20982000,104910,104910\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,880,0.0157,21078000,105390,105390\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,884,0.0157,21174000,105870,105870\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,888,0.0158,21270000,106350,106350\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,892,0.0158,21366000,106830,106830\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,896,0.0159,21462000,107310,107310\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,900,0.0161,21558000,107790,107790\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,904,0.0162,21654000,108270,108270\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,908,0.0161,21750000,108750,108750\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,912,0.0163,21846000,109230,109230\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,916,0.0164,21942000,109710,109710\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,920,0.0165,22038000,110190,110190\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,924,0.0164,22134000,110670,110670\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,928,0.0166,22230000,111150,111150\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,932,0.0166,22326000,111630,111630\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,936,0.0167,22422000,112110,112110\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,940,0.0168,22518000,112590,112590\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,944,0.0168,22614000,113070,113070\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,948,0.0169,22710000,113550,113550\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,952,0.0170,22806000,114030,114030\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,956,0.0170,22902000,114510,114510\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,960,0.0171,22998000,114990,114990\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,964,0.0176,23094000,115470,115470\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,968,0.0176,23190000,115950,115950\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,972,0.0177,23286000,116430,116430\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,976,0.0177,23382000,116910,116910\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,980,0.0178,23478000,117390,117390\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,984,0.0178,23574000,117870,117870\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,988,0.0179,23670000,118350,118350\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,992,0.0180,23766000,118830,118830\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,996,0.0181,23862000,119310,119310\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,1000,0.0182,23958000,119790,119790\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,1004,0.0182,24054000,120270,120270\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,1008,0.0182,24150000,120750,120750\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,1012,0.0184,24246000,121230,121230\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,1016,0.0185,24342000,121710,121710\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,1020,0.0184,24438000,122190,122190\n", "iter,ny,nx,Runtime,PM_VECTOR_FLOP_CMPL (total),PM_VECTOR_FLOP_CMPL (min), PM_VECTOR_FLOP_CMPL (max)\n", "200,32,1024,0.0182,24534000,122670,122670\n", "mv /gpfs/wolf/trn003/scratch/aherten//poisson2d.vflop.bin.csv .\n"]}], "source": ["!make bench_task4"]}, {"cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>nx</th>\n", " <th>iter</th>\n", " <th>ny</th>\n", " <th>Runtime</th>\n", " <th>PM_SCALAR_FLOP_CMPL (total)</th>\n", " <th>PM_SCALAR_FLOP_CMPL (min)</th>\n", " <th>PM_SCALAR_FLOP_CMPL (max)</th>\n", " <th>PM_VECTOR_FLOP_CMPL (total)</th>\n", " <th>PM_VECTOR_FLOP_CMPL (min)</th>\n", " <th>PM_VECTOR_FLOP_CMPL (max)</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>4</td>\n", " <td>200</td>\n", " <td>32</td>\n", " <td>0.0010</td>\n", " <td>96000</td>\n", " <td>480</td>\n", " <td>480</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>8</td>\n", " <td>200</td>\n", " <td>32</td>\n", " <td>0.0011</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>150000</td>\n", " <td>750</td>\n", " <td>750</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>12</td>\n", " <td>200</td>\n", " <td>32</td>\n", " <td>0.0012</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>246000</td>\n", " <td>1230</td>\n", " <td>1230</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>16</td>\n", " <td>200</td>\n", " <td>32</td>\n", " <td>0.0012</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>342000</td>\n", " <td>1710</td>\n", " <td>1710</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>20</td>\n", " <td>200</td>\n", " <td>32</td>\n", " <td>0.0013</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>438000</td>\n", " <td>2190</td>\n", " <td>2190</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>"], "text/plain": [" nx iter ny Runtime PM_SCALAR_FLOP_CMPL (total) \\\n", "0 4 200 32 0.0010 96000 \n", "1 8 200 32 0.0011 0 \n", "2 12 200 32 0.0012 0 \n", "3 16 200 32 0.0012 0 \n", "4 20 200 32 0.0013 0 \n", "\n", " PM_SCALAR_FLOP_CMPL (min) PM_SCALAR_FLOP_CMPL (max) \\\n", "0 480 480 \n", "1 0 0 \n", "2 0 0 \n", "3 0 0 \n", "4 0 0 \n", "\n", " PM_VECTOR_FLOP_CMPL (total) PM_VECTOR_FLOP_CMPL (min) \\\n", "0 0 0 \n", "1 150000 750 \n", "2 246000 1230 \n", "3 342000 1710 \n", "4 438000 2190 \n", "\n", " PM_VECTOR_FLOP_CMPL (max) \n", "0 0 \n", "1 750 \n", "2 1230 \n", "3 1710 \n", "4 2190 "]}, "execution_count": 39, "metadata": {}, "output_type": "execute_result"}], "source": ["df_sflop = pd.read_csv(\"poisson2d.sflop.bin.csv\", skiprows=range(2, 50000, 2))\n", "df_vflop = pd.read_csv(\"poisson2d.vflop.bin.csv\", skiprows=range(2, 50000, 2))\n", "df_flop = pd.concat([df_sflop.set_index(\"nx\"), df_vflop.set_index(\"nx\")[['PM_VECTOR_FLOP_CMPL (total)', 'PM_VECTOR_FLOP_CMPL (min)', ' PM_VECTOR_FLOP_CMPL (max)']]], axis=1).reset_index()\n", "df_flop.head()"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Again, the name of the vector counter is a bit misleading; not floating point operations are measured but floating point instructions. To get *real* floating point operations, each value needs to be multiplied by the vector width (2). We can plot the values afterwards (non-interactive: `make graph_task4`)."]}, {"cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": ["df_flop[\"Grid Points\"] = df_flop[\"nx\"] * df_flop[\"ny\"]\n", "df_flop[\"Vector FlOps (min)\"] = df_flop[\"PM_VECTOR_FLOP_CMPL (min)\"] * 2\n", "df_flop[\"Scalar FlOps (min)\"] = df_flop[\"PM_SCALAR_FLOP_CMPL (min)\"]"]}, {"cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 1008x432 with 1 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["df_flop.set_index(\"Grid Points\")[[\"Scalar FlOps (min)\", \"Vector FlOps (min)\"]].plot();"]}, {"cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Counter Scalar FlOps (min) is proportional to the grid points (nx*ny) by a factor of -0.0003 (\u00b1 0.0002)\n", "Counter Vector FlOps (min) is proportional to the grid points (nx*ny) by a factor of 7.5004 (\u00b1 0.0002)\n"]}], "source": ["_fit, _cov = common.print_and_return_fit(\n", " [\"Scalar FlOps (min)\", \"Vector FlOps (min)\"], \n", " df_flop.set_index(\"Grid Points\"), \n", " linear_function\n", ")\n", "fit_parameters = {**fit_parameters, **_fit}\n", "fit_covariance = {**fit_covariance, **_cov}"]}, {"cell_type": "markdown", "metadata": {"exercise": "solution"}, "source": ["Interesting! We seem to be using the vector registers of our system very well. Basically all operations are vector operations!"]}, {"cell_type": "markdown", "metadata": {}, "source": ["With that measured, we can determine the Arithmetic Intensity; the balance of floating point operations to bytes transmitted:\n", "\n", "\\begin{align}\n", "\\text{AI}^\\text{emp} = I_\\text{flop} / I_\\text{mem} \\text{,}\n", "\\end{align}\n", "\n", "with $I$ denoting the respective amount. This is the emperically determined Arithmetic Intensity.\n", "\n", "In the non-interactive version of the Notebook, please plot the graph calling `make graph_task4-2` in the terminal."]}, {"cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [], "source": ["I_flop_scalar = df_flop.set_index(\"Grid Points\")[\"Scalar FlOps (min)\"]\n", "I_flop_vector = df_flop.set_index(\"Grid Points\")[\"Vector FlOps (min)\"]\n", "I_mem_load = df_byte[\"Loads\"]\n", "I_mem_store = df_byte[\"Stores\"]"]}, {"cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 1008x432 with 1 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["df_ai = pd.DataFrame()\n", "df_ai[\"Arithmetic Intensity\"] = (I_flop_scalar + I_flop_vector) / (I_mem_load + I_mem_store)\n", "ax = df_ai.plot();\n", "ax.set_ylabel(\"Byte/FlOp\");"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Thinking back to the first lecture of the tutorial, what Arithemtic Intensity did you expect?"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Task E2: Measuring a Larger Range\n", "<a name=\"taske2\"></a>\n", "\n", "If you still still have time, you might venture into your own benchmarking adventure.\n", "\n", "Maybe you noticed already, for instance in Task 2 C: At the very right to very large numbers of grid points, the behaviour of the graph changed. Something is happening there!\n", "\n", "\n", "**TASK**: Revisit the counters measured above for a larger range of `nx`. Right now, we only studied `nx` until 1000. New effects appear above that value\u00a0\u2013\u00a0partly only well above, though ($nx > 15000$).\n", "\n", "You're on your own here. Edit the `bench.sh` script to change the range and the stepping increments.\n", "\n", "**Good luck!**\n", "\n", "[Back to top](#toc)"]}], "metadata": {"kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}, "language_info": {"codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.0"}}, "nbformat": 4, "nbformat_minor": 4}
\ No newline at end of file
diff --git a/2-Performance_Counters/Handson/Solutions/Hands-On-Performance-Counters.pdf b/2-Performance_Counters/Handson/Solutions/Hands-On-Performance-Counters.pdf
index 0335248e742ef80058639dc237914d48fa62ff8b..24f50ef681567d697e5ce9e199dcbb65483c43fb 100644
Binary files a/2-Performance_Counters/Handson/Solutions/Hands-On-Performance-Counters.pdf and b/2-Performance_Counters/Handson/Solutions/Hands-On-Performance-Counters.pdf differ
diff --git a/2-Performance_Counters/Handson/Solutions/Makefile b/2-Performance_Counters/Handson/Solutions/Makefile
index e725a4125a9fcdf5c7adc4b7a1d989fea2c8ee67..1db4b2f76ed5e40ed11f543e3d3837e46fa33080 100644
--- a/2-Performance_Counters/Handson/Solutions/Makefile
+++ b/2-Performance_Counters/Handson/Solutions/Makefile
@@ -34,42 +34,42 @@ clean:
${RM} -f *.bin
run_task1: poisson2d.ins_cyc.bin
- $(SC18_SUBMIT_CMD) ./$< 200 1024
+ $(SC19_SUBMIT_CMD) ./$< 200 1024
run_task2: poisson2d.ld_st.bin
- $(SC18_SUBMIT_CMD) ./$< 200 1024
+ $(SC19_SUBMIT_CMD) ./$< 200 1024
run_task3_1: poisson2d.vld.bin
- $(SC18_SUBMIT_CMD) ./$< 200 1024
+ $(SC19_SUBMIT_CMD) ./$< 200 1024
run_task3_2: poisson2d.vst.bin
- $(SC18_SUBMIT_CMD) ./$< 200 1024
+ $(SC19_SUBMIT_CMD) ./$< 200 1024
run_task3: run_task3_1 run_task3_2
run_task4_1: poisson2d.sflop.bin
- $(SC18_SUBMIT_CMD) ./$< 200 1024
+ $(SC19_SUBMIT_CMD) ./$< 200 1024
run_task4_2: poisson2d.vflop.bin
- $(SC18_SUBMIT_CMD) ./$< 200 1024
+ $(SC19_SUBMIT_CMD) ./$< 200 1024
run_task4: run_task4_1 run_task4_2
bench_task1: poisson2d.ins_cyc.bin
- $(SC18_SUBMIT_CMD) ./bench.sh $< $(SC18_DIR_SCRATCH)/$<.csv
- mv $(SC18_DIR_SCRATCH)/$<.csv .
+ $(SC19_SUBMIT_CMD) ./bench.sh $< $(SC19_DIR_SCRATCH)/$<.csv
+ mv $(SC19_DIR_SCRATCH)/$<.csv .
bench_task2: poisson2d.ld_st.bin
- $(SC18_SUBMIT_CMD) ./bench.sh $< $(SC18_DIR_SCRATCH)/$<.csv
- mv $(SC18_DIR_SCRATCH)/$<.csv .
+ $(SC19_SUBMIT_CMD) ./bench.sh $< $(SC19_DIR_SCRATCH)/$<.csv
+ mv $(SC19_DIR_SCRATCH)/$<.csv .
bench_task3_1: poisson2d.vld.bin
- $(SC18_SUBMIT_CMD) ./bench.sh $< $(SC18_DIR_SCRATCH)/$<.csv
- mv $(SC18_DIR_SCRATCH)/$<.csv .
+ $(SC19_SUBMIT_CMD) ./bench.sh $< $(SC19_DIR_SCRATCH)/$<.csv
+ mv $(SC19_DIR_SCRATCH)/$<.csv .
bench_task3_2: poisson2d.vst.bin
- $(SC18_SUBMIT_CMD) ./bench.sh $< $(SC18_DIR_SCRATCH)/$<.csv
- mv $(SC18_DIR_SCRATCH)/$<.csv .
+ $(SC19_SUBMIT_CMD) ./bench.sh $< $(SC19_DIR_SCRATCH)/$<.csv
+ mv $(SC19_DIR_SCRATCH)/$<.csv .
bench_task3: bench_task3_1 bench_task3_2
bench_task4_1: poisson2d.sflop.bin
- $(SC18_SUBMIT_CMD) ./bench.sh $< $(SC18_DIR_SCRATCH)/$<.csv
- mv $(SC18_DIR_SCRATCH)/$<.csv .
+ $(SC19_SUBMIT_CMD) ./bench.sh $< $(SC19_DIR_SCRATCH)/$<.csv
+ mv $(SC19_DIR_SCRATCH)/$<.csv .
bench_task4_2: poisson2d.vflop.bin
- $(SC18_SUBMIT_CMD) ./bench.sh $< $(SC18_DIR_SCRATCH)/$<.csv
- mv $(SC18_DIR_SCRATCH)/$<.csv .
+ $(SC19_SUBMIT_CMD) ./bench.sh $< $(SC19_DIR_SCRATCH)/$<.csv
+ mv $(SC19_DIR_SCRATCH)/$<.csv .
bench_task4: bench_task4_1 bench_task4_2
clean_scratch_csv:
- ${RM} $(SC18_DIR_SCRATCH)/*.csv
+ ${RM} $(SC19_DIR_SCRATCH)/*.csv
clean_csv: clean_scratch_csv
${RM} *.csv
@@ -82,32 +82,25 @@ graph_task2c: plot-task2c.pdf
graph_task4: plot-task4.pdf
graph_task4-2: plot-task4-2.pdf
plot-task1.pdf: poisson2d.ins_cyc.bin.csv
- @test "$$SC18_MODULE_ACTIVE_1" -eq "1" || "Please load the module of this task, sc18/handson1."
python3 -c "import graphing; graphing.task1()"
@test -n "$$DISPLAY" || "No X forwarding found. Either reconnect with X forwarding (-X / -Y) or download $@ with scp."
display $@
plot-task2a.pdf: poisson2d.ld_st.bin.csv
- @test "$$SC18_MODULE_ACTIVE_1" -eq "1" || "Please load the module of this task, sc18/handson1."
python3 -c "import graphing; graphing.task2a()"
display $@
plot-task2b.pdf: poisson2d.vld.bin.csv poisson2d.vst.bin.csv
- @test "$$SC18_MODULE_ACTIVE_1" -eq "1" || "Please load the module of this task, sc18/handson1."
python3 -c "import graphing; graphing.task2b()"
display $@
plot-task2b-2.pdf: poisson2d.vld.bin.csv poisson2d.vst.bin.csv poisson2d.ld_st.bin.csv
- @test "$$SC18_MODULE_ACTIVE_1" -eq "1" || "Please load the module of this task, sc18/handson1."
python3 -c "import graphing; graphing.task2b(bytes=True)"
display $@
plot-task2c.pdf: poisson2d.vld.bin.csv poisson2d.vst.bin.csv poisson2d.ld_st.bin.csv poisson2d.ins_cyc.bin.csv
- @test "$$SC18_MODULE_ACTIVE_1" -eq "1" || "Please load the module of this task, sc18/handson1."
python3 -c "import graphing; graphing.task2c()"
display $@
plot-task4.pdf: poisson2d.sflop.bin.csv poisson2d.vflop.bin.csv
- @test "$$SC18_MODULE_ACTIVE_1" -eq "1" || "Please load the module of this task, sc18/handson1."
python3 -c "import graphing; graphing.task4()"
display $@
plot-task4-2.pdf: poisson2d.sflop.bin.csv poisson2d.vflop.bin.csv
- @test "$$SC18_MODULE_ACTIVE_1" -eq "1" || "Please load the module of this task, sc18/handson1."
python3 -c "import graphing; graphing.task4(ai=True)"
display $@
diff --git a/2-Performance_Counters/Handson/Solutions/common.py b/2-Performance_Counters/Handson/Solutions/common.py
index 1891a0341f369f7564b4a29b3f4a60e314f4bc9b..9033865e014fce9ece4137cdb11a42884acceae4 100644
--- a/2-Performance_Counters/Handson/Solutions/common.py
+++ b/2-Performance_Counters/Handson/Solutions/common.py
@@ -1,2 +1,22 @@
def normalize(df, old_column, new_column):
df[new_column] = df[old_column] / (df["ny"] * df["nx"])
+
+def print_and_return_fit(list_of_quantities, dataframe, function, format_value=">7.4f", format_uncertainty="f", _print=True):
+ """Use `curve_fit` to fit each quantity in `list_of_quantity` wrt to `dataframe.index`. Print (selectable) and return the result."""
+ import numpy as np
+ from scipy.optimize import curve_fit
+ _fit_parameters = {}
+ _fit_covariance = {}
+ _quantity_padding = np.max([len(_str) for _str in list_of_quantities])
+ for quantity in list_of_quantities:
+ _fit_parameters[quantity], _fit_covariance[quantity] = curve_fit(function, dataframe.index, dataframe[quantity])
+ if (_print):
+ print("Counter {:>{_quantity_padding}} is proportional to the grid points (nx*ny) by a factor of {:{format_value}} (± {:{format_uncertainty}})".format(
+ quantity,
+ _fit_parameters[quantity][0],
+ np.sqrt(np.diag(_fit_covariance[quantity]))[0],
+ _quantity_padding=_quantity_padding,
+ format_value=format_value,
+ format_uncertainty=format_uncertainty
+ ))
+ return (_fit_parameters, _fit_covariance)
\ No newline at end of file
diff --git a/2-Performance_Counters/Handson/Tasks/Makefile b/2-Performance_Counters/Handson/Tasks/Makefile
index e725a4125a9fcdf5c7adc4b7a1d989fea2c8ee67..1db4b2f76ed5e40ed11f543e3d3837e46fa33080 100644
--- a/2-Performance_Counters/Handson/Tasks/Makefile
+++ b/2-Performance_Counters/Handson/Tasks/Makefile
@@ -34,42 +34,42 @@ clean:
${RM} -f *.bin
run_task1: poisson2d.ins_cyc.bin
- $(SC18_SUBMIT_CMD) ./$< 200 1024
+ $(SC19_SUBMIT_CMD) ./$< 200 1024
run_task2: poisson2d.ld_st.bin
- $(SC18_SUBMIT_CMD) ./$< 200 1024
+ $(SC19_SUBMIT_CMD) ./$< 200 1024
run_task3_1: poisson2d.vld.bin
- $(SC18_SUBMIT_CMD) ./$< 200 1024
+ $(SC19_SUBMIT_CMD) ./$< 200 1024
run_task3_2: poisson2d.vst.bin
- $(SC18_SUBMIT_CMD) ./$< 200 1024
+ $(SC19_SUBMIT_CMD) ./$< 200 1024
run_task3: run_task3_1 run_task3_2
run_task4_1: poisson2d.sflop.bin
- $(SC18_SUBMIT_CMD) ./$< 200 1024
+ $(SC19_SUBMIT_CMD) ./$< 200 1024
run_task4_2: poisson2d.vflop.bin
- $(SC18_SUBMIT_CMD) ./$< 200 1024
+ $(SC19_SUBMIT_CMD) ./$< 200 1024
run_task4: run_task4_1 run_task4_2
bench_task1: poisson2d.ins_cyc.bin
- $(SC18_SUBMIT_CMD) ./bench.sh $< $(SC18_DIR_SCRATCH)/$<.csv
- mv $(SC18_DIR_SCRATCH)/$<.csv .
+ $(SC19_SUBMIT_CMD) ./bench.sh $< $(SC19_DIR_SCRATCH)/$<.csv
+ mv $(SC19_DIR_SCRATCH)/$<.csv .
bench_task2: poisson2d.ld_st.bin
- $(SC18_SUBMIT_CMD) ./bench.sh $< $(SC18_DIR_SCRATCH)/$<.csv
- mv $(SC18_DIR_SCRATCH)/$<.csv .
+ $(SC19_SUBMIT_CMD) ./bench.sh $< $(SC19_DIR_SCRATCH)/$<.csv
+ mv $(SC19_DIR_SCRATCH)/$<.csv .
bench_task3_1: poisson2d.vld.bin
- $(SC18_SUBMIT_CMD) ./bench.sh $< $(SC18_DIR_SCRATCH)/$<.csv
- mv $(SC18_DIR_SCRATCH)/$<.csv .
+ $(SC19_SUBMIT_CMD) ./bench.sh $< $(SC19_DIR_SCRATCH)/$<.csv
+ mv $(SC19_DIR_SCRATCH)/$<.csv .
bench_task3_2: poisson2d.vst.bin
- $(SC18_SUBMIT_CMD) ./bench.sh $< $(SC18_DIR_SCRATCH)/$<.csv
- mv $(SC18_DIR_SCRATCH)/$<.csv .
+ $(SC19_SUBMIT_CMD) ./bench.sh $< $(SC19_DIR_SCRATCH)/$<.csv
+ mv $(SC19_DIR_SCRATCH)/$<.csv .
bench_task3: bench_task3_1 bench_task3_2
bench_task4_1: poisson2d.sflop.bin
- $(SC18_SUBMIT_CMD) ./bench.sh $< $(SC18_DIR_SCRATCH)/$<.csv
- mv $(SC18_DIR_SCRATCH)/$<.csv .
+ $(SC19_SUBMIT_CMD) ./bench.sh $< $(SC19_DIR_SCRATCH)/$<.csv
+ mv $(SC19_DIR_SCRATCH)/$<.csv .
bench_task4_2: poisson2d.vflop.bin
- $(SC18_SUBMIT_CMD) ./bench.sh $< $(SC18_DIR_SCRATCH)/$<.csv
- mv $(SC18_DIR_SCRATCH)/$<.csv .
+ $(SC19_SUBMIT_CMD) ./bench.sh $< $(SC19_DIR_SCRATCH)/$<.csv
+ mv $(SC19_DIR_SCRATCH)/$<.csv .
bench_task4: bench_task4_1 bench_task4_2
clean_scratch_csv:
- ${RM} $(SC18_DIR_SCRATCH)/*.csv
+ ${RM} $(SC19_DIR_SCRATCH)/*.csv
clean_csv: clean_scratch_csv
${RM} *.csv
@@ -82,32 +82,25 @@ graph_task2c: plot-task2c.pdf
graph_task4: plot-task4.pdf
graph_task4-2: plot-task4-2.pdf
plot-task1.pdf: poisson2d.ins_cyc.bin.csv
- @test "$$SC18_MODULE_ACTIVE_1" -eq "1" || "Please load the module of this task, sc18/handson1."
python3 -c "import graphing; graphing.task1()"
@test -n "$$DISPLAY" || "No X forwarding found. Either reconnect with X forwarding (-X / -Y) or download $@ with scp."
display $@
plot-task2a.pdf: poisson2d.ld_st.bin.csv
- @test "$$SC18_MODULE_ACTIVE_1" -eq "1" || "Please load the module of this task, sc18/handson1."
python3 -c "import graphing; graphing.task2a()"
display $@
plot-task2b.pdf: poisson2d.vld.bin.csv poisson2d.vst.bin.csv
- @test "$$SC18_MODULE_ACTIVE_1" -eq "1" || "Please load the module of this task, sc18/handson1."
python3 -c "import graphing; graphing.task2b()"
display $@
plot-task2b-2.pdf: poisson2d.vld.bin.csv poisson2d.vst.bin.csv poisson2d.ld_st.bin.csv
- @test "$$SC18_MODULE_ACTIVE_1" -eq "1" || "Please load the module of this task, sc18/handson1."
python3 -c "import graphing; graphing.task2b(bytes=True)"
display $@
plot-task2c.pdf: poisson2d.vld.bin.csv poisson2d.vst.bin.csv poisson2d.ld_st.bin.csv poisson2d.ins_cyc.bin.csv
- @test "$$SC18_MODULE_ACTIVE_1" -eq "1" || "Please load the module of this task, sc18/handson1."
python3 -c "import graphing; graphing.task2c()"
display $@
plot-task4.pdf: poisson2d.sflop.bin.csv poisson2d.vflop.bin.csv
- @test "$$SC18_MODULE_ACTIVE_1" -eq "1" || "Please load the module of this task, sc18/handson1."
python3 -c "import graphing; graphing.task4()"
display $@
plot-task4-2.pdf: poisson2d.sflop.bin.csv poisson2d.vflop.bin.csv
- @test "$$SC18_MODULE_ACTIVE_1" -eq "1" || "Please load the module of this task, sc18/handson1."
python3 -c "import graphing; graphing.task4(ai=True)"
display $@
diff --git a/2-Performance_Counters/Handson/Tasks/common.py b/2-Performance_Counters/Handson/Tasks/common.py
index 1891a0341f369f7564b4a29b3f4a60e314f4bc9b..9033865e014fce9ece4137cdb11a42884acceae4 100644
--- a/2-Performance_Counters/Handson/Tasks/common.py
+++ b/2-Performance_Counters/Handson/Tasks/common.py
@@ -1,2 +1,22 @@
def normalize(df, old_column, new_column):
df[new_column] = df[old_column] / (df["ny"] * df["nx"])
+
+def print_and_return_fit(list_of_quantities, dataframe, function, format_value=">7.4f", format_uncertainty="f", _print=True):
+ """Use `curve_fit` to fit each quantity in `list_of_quantity` wrt to `dataframe.index`. Print (selectable) and return the result."""
+ import numpy as np
+ from scipy.optimize import curve_fit
+ _fit_parameters = {}
+ _fit_covariance = {}
+ _quantity_padding = np.max([len(_str) for _str in list_of_quantities])
+ for quantity in list_of_quantities:
+ _fit_parameters[quantity], _fit_covariance[quantity] = curve_fit(function, dataframe.index, dataframe[quantity])
+ if (_print):
+ print("Counter {:>{_quantity_padding}} is proportional to the grid points (nx*ny) by a factor of {:{format_value}} (± {:{format_uncertainty}})".format(
+ quantity,
+ _fit_parameters[quantity][0],
+ np.sqrt(np.diag(_fit_covariance[quantity]))[0],
+ _quantity_padding=_quantity_padding,
+ format_value=format_value,
+ format_uncertainty=format_uncertainty
+ ))
+ return (_fit_parameters, _fit_covariance)
\ No newline at end of file