diff --git a/pySDC/projects/GPU/analysis_scripts/parallel_scaling.py b/pySDC/projects/GPU/analysis_scripts/parallel_scaling.py
index fe190bd6f489ef7c00d72a7a211915a4366910ac..94d3b37d8beb37bbad9e7a1b018daef2cf7a61d4 100644
--- a/pySDC/projects/GPU/analysis_scripts/parallel_scaling.py
+++ b/pySDC/projects/GPU/analysis_scripts/parallel_scaling.py
@@ -81,13 +81,17 @@ class ScalingConfig(object):
**kwargs,
)
- def plot_scaling_test(self, ax, quantity='time', **plotting_params): # pragma: no cover
+ def plot_scaling_test(self, ax, quantity='time', space_time=None, **plotting_params): # pragma: no cover
from matplotlib.colors import TABLEAU_COLORS
cmap = TABLEAU_COLORS
colors = list(cmap.values())
for experiment in self.experiments:
+ if space_time is not None:
+ if not experiment.PinT == space_time:
+ continue
+
tasks_time = self.tasks_time if experiment.PinT else 1
timings = {}
@@ -141,8 +145,12 @@ class ScalingConfig(object):
elif quantity == 'throughput_per_task':
timings[np.prod(procs)] = experiment.res**self.ndim / t_mean
elif quantity == 'efficiency':
+ if type(config).__name__ == 'GrayScottScaling3D':
+ norm = 13216322.909
+ else:
+ norm = 1
timings[np.prod(procs) / self.tasks_per_node] = (
- experiment.res**self.ndim / t_mean / np.prod(procs)
+ experiment.res**self.ndim / t_mean / np.prod(procs) / norm
)
elif quantity == 'time':
timings[np.prod(procs) / self.tasks_per_node] = t_mean
@@ -150,11 +158,17 @@ class ScalingConfig(object):
timings[np.prod(procs)] = t_mean
elif quantity == 'min_time_per_task':
timings[np.prod(procs)] = t_min
+ elif quantity == 'min_time':
+ timings[np.prod(procs) / self.tasks_per_node] = t_min
else:
raise NotImplementedError
except (FileNotFoundError, ValueError):
pass
+ if quantity == 'efficiency' and type(config).__name__ == 'RayleighBenard_scaling':
+ norm = max(timings.values())
+ timings = {key: value / norm for key, value in timings.items()}
+
ax.loglog(
timings.keys(),
timings.values(),
@@ -171,7 +185,8 @@ class ScalingConfig(object):
'time': r'$t_\mathrm{step}$ / s',
'time_per_task': r'$t_\mathrm{step}$ / s',
'min_time_per_task': r'minimal $t_\mathrm{step}$ / s',
- 'efficiency': 'efficiency / DoF/s/task',
+ 'min_time': r'minimal $t_\mathrm{step}$ / s',
+ 'efficiency': r'parallel efficiency / \%',
}
ax.set_ylabel(labels[quantity])
@@ -331,17 +346,28 @@ class RayleighBenardDedalusComparisonGPU(GPUConfig, ScalingConfig):
]
-def plot_scalings(problem, **kwargs): # pragma: no cover
+def plot_scalings(problem, XPU=None, space_time=None, **kwargs): # pragma: no cover
if problem == 'GS3D':
- configs = [
- GrayScottSpaceScalingCPU3D(),
- GrayScottSpaceScalingGPU3D(),
- ]
+ if XPU == 'CPU':
+ configs = [GrayScottSpaceScalingCPU3D()]
+ elif XPU == 'GPU':
+ configs = [GrayScottSpaceScalingGPU3D()]
+ else:
+ configs = [GrayScottSpaceScalingCPU3D(), GrayScottSpaceScalingGPU3D()]
elif problem == 'RBC':
- configs = [
- RayleighBenardSpaceScalingGPU(),
- RayleighBenardSpaceScalingCPU(),
- ]
+ if XPU == 'CPU':
+ configs = [
+ RayleighBenardSpaceScalingCPU(),
+ ]
+ elif XPU == 'GPU':
+ configs = [
+ RayleighBenardSpaceScalingGPU(),
+ ]
+ else:
+ configs = [
+ RayleighBenardSpaceScalingGPU(),
+ RayleighBenardSpaceScalingCPU(),
+ ]
elif problem == 'RBC_dedalus':
configs = [
RayleighBenardDedalusComparison(),
@@ -358,31 +384,26 @@ def plot_scalings(problem, **kwargs): # pragma: no cover
('RBC', 'time'): {'x': [1 / 10, 64], 'y': [60, 60 / 640]},
('RBC', 'time_per_task'): {'x': [1, 640], 'y': [60, 60 / 640]},
('RBC', 'min_time_per_task'): {'x': [1, 640], 'y': [60, 60 / 640]},
+ ('RBC', 'min_time'): {'x': [1, 640], 'y': [60, 60 / 640]},
('RBC', 'throughput_per_task'): {'x': [1 / 1, 640], 'y': [2e4, 2e4 * 640]},
}
- fig, ax = plt.subplots(figsize=figsize_by_journal('TUHH_thesis', 1, 0.6))
- configs[1].plot_scaling_test(ax=ax, quantity='efficiency')
- # ax.legend(frameon=False)
- box = ax.get_position()
- ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
- ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
-
- ax.set_yscale('linear')
- path = f'{PROJECT_PATH}/plots/scaling_{problem}_efficiency.pdf'
- fig.savefig(path, bbox_inches='tight')
- print(f'Saved {path!r}', flush=True)
-
- for quantity in ['time', 'throughput', 'time_per_task', 'throughput_per_task', 'min_time_per_task'][::-1]:
+ for quantity in ['time', 'throughput', 'time_per_task', 'throughput_per_task', 'min_time_per_task', 'efficiency'][
+ ::-1
+ ]:
fig, ax = plt.subplots(figsize=figsize_by_journal('TUHH_thesis', 1, 0.6))
for config in configs:
- config.plot_scaling_test(ax=ax, quantity=quantity)
+ config.plot_scaling_test(ax=ax, quantity=quantity, space_time=space_time)
if (problem, quantity) in ideal_lines.keys():
ax.loglog(*ideal_lines[(problem, quantity)].values(), color='black', ls=':', label='ideal')
+ elif quantity == 'efficiency':
+ ax.axhline(1, color='black', ls=':', label='ideal')
+ ax.set_yscale('linear')
+ ax.set_ylim(0, 1.1)
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
- path = f'{PROJECT_PATH}/plots/scaling_{problem}_{quantity}.pdf'
+ path = f'{PROJECT_PATH}/plots/scaling_{problem}_{quantity}_{XPU}_{space_time}.pdf'
fig.savefig(path, bbox_inches='tight')
print(f'Saved {path!r}', flush=True)
@@ -393,8 +414,8 @@ if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--mode', type=str, choices=['run', 'plot'], default='run')
parser.add_argument('--problem', type=str, default='GS')
- parser.add_argument('--XPU', type=str, choices=['CPU', 'GPU'], default='CPU')
- parser.add_argument('--space_time', type=str, choices=['True', 'False'], default='False')
+ parser.add_argument('--XPU', type=str, choices=['CPU', 'GPU', 'both'], default='CPU')
+ parser.add_argument('--space_time', type=str, choices=['True', 'False', 'None'], default='False')
parser.add_argument('--submit', type=str, choices=['True', 'False'], default='True')
parser.add_argument('--nsys_profiling', type=str, choices=['True', 'False'], default='False')
@@ -403,6 +424,13 @@ if __name__ == '__main__':
submit = args.submit == 'True'
nsys_profiling = args.nsys_profiling == 'True'
+ if args.space_time == 'True':
+ space_time = True
+ elif args.space_time == 'False':
+ space_time = False
+ else:
+ space_time = None
+
config_classes = []
if args.problem == 'GS3D':
@@ -429,6 +457,6 @@ if __name__ == '__main__':
if args.mode == 'run':
config.run_scaling_test(submit=submit, nsys_profiling=nsys_profiling)
elif args.mode == 'plot':
- plot_scalings(problem=args.problem)
+ plot_scalings(problem=args.problem, XPU=args.XPU, space_time=space_time)
else:
raise NotImplementedError(f'Don\'t know mode {args.mode!r}')
diff --git a/pySDC/projects/GPU/paper_plots.py b/pySDC/projects/GPU/paper_plots.py
index 607bd8d2d557f3357c895641f1f84f36633cf6f0..2efd6ab7a864b113665058856b3d8e89e955a94a 100644
--- a/pySDC/projects/GPU/paper_plots.py
+++ b/pySDC/projects/GPU/paper_plots.py
@@ -54,6 +54,7 @@ def plot_scalings_separately(problem, journal='TUHH_thesis', **kwargs): # pragm
def make_plots_for_thesis(): # pragma: no cover
from pySDC.projects.GPU.analysis_scripts.plot_RBC_matrix import plot_DCT, plot_preconditioners, plot_ultraspherical
+ from pySDC.projects.GPU.analysis_scripts.parallel_scaling import plot_scalings
# small plots with no simulations
plot_DCT()
@@ -61,8 +62,11 @@ def make_plots_for_thesis(): # pragma: no cover
plot_ultraspherical()
# plot space-time parallel scaling
- for problem in ['GS3D', 'RBC']:
- plot_scalings_separately(problem=problem)
+ plot_scalings(problem='GS3D', XPU='both', space_time=False)
+ plot_scalings(problem='GS3D', XPU='GPU', space_time=None)
+ plot_scalings(problem='RBC', XPU='both', space_time=False)
+ plot_scalings(problem='RBC', XPU='GPU', space_time=None)
+ plot_scalings(problem='RBC', XPU='CPU', space_time=None)
if __name__ == '__main__':
diff --git a/pySDC/projects/Resilience/paper_plots.py b/pySDC/projects/Resilience/paper_plots.py
index 69d176236567ba41acc860f3ea8a7805b4f38964..2095c9e5ed7fb05468b03c7b0ad002afb6564e68 100644
--- a/pySDC/projects/Resilience/paper_plots.py
+++ b/pySDC/projects/Resilience/paper_plots.py
@@ -813,6 +813,7 @@ def plot_recovery_rate_per_acceptance_threshold(problem, target='resilience'):
else:
fig, ax = plt.subplots(figsize=figsize_by_journal(JOURNAL, 0.8, 0.5))
+ ax.axvline(1.1, color='grey', ls=':', label='1.1')
stats_analyser.plot_recovery_thresholds(thresh_range=np.logspace(-1, 4, 500), recoverable_only=False, ax=ax)
ax.set_xscale('log')
ax.set_ylim((-0.05, 1.05))