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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "linear-bangkok",
+   "metadata": {},
+   "source": [
+    "### HIGH THROUGHPUT COMPUTING WITH DASK\n",
+    "\n",
+    "**Organisers:** Alan O’Cais, David Swenson  \n",
+    "**Website:** https://www.cecam.org/workshop-details/1022\n",
+    "\n",
+    "**Synopsis:**\n",
+    "High-throughput (task-based) computing is a flexible approach to parallelisation. It involves splitting a problem into loosely-coupled tasks. A scheduler then orchestrates the parallel execution of those tasks, allowing programs to adaptively scale their resource usage. E-CAM has extended the data-analytics framework Dask with a capable and efficient library to handle such workloads. This workshop will be held as a series of virtual seminars/tutorials on tools in the Dask HPC ecosystem.\n",
+    "\n",
+    "**Programme:**\n",
+    "- 21 January 2021, 3pm CET (2pm UTC): Dask - a flexible library for parallel computing in Python\n",
+    "  - YouTube link: https://youtu.be/Tl8rO-baKuY\n",
+    "  - GitHub Repo: https://github.com/jacobtomlinson/dask-video-tutorial-2020\n",
+    "\n",
+    "- 4 February 2021, 3pm CET (2pm UTC): Dask-Jobqueue - a library that integrates Dask with standard HPC queuing systems, such as SLURM or PBS\n",
+    "  - YouTube link: https://youtu.be/iNxhHXzmJ1w\n",
+    "  - GitHub Repo: https://github.com/ExaESM-WP4/workshop-Dask-Jobqueue-cecam-2021-02\n",
+    "\n",
+    "- 11 February 2021, 3pm CET (2pm UTC) : Jobqueue-Features - a library that enables functionality aimed at enhancing scalability\n",
+    "  - YouTube link: https://youtu.be/FpMua8iJeTk\n",
+    "  - GitHub Repo: https://github.com/E-CAM/jobqueue_features_workshop_materials"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "precious-roommate",
+   "metadata": {},
+   "source": [
+    "Please clone:  \n",
+    "    https://github.com/ExaESM-WP4/workshop-Dask-Jobqueue-cecam-2021-02"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "latter-specialist",
+   "metadata": {},
+   "source": [
+    "and open  \n",
+    "`11_Dask_LocalCluster_Example.ipynb`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "comparable-amber",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
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+   "file_extension": ".py",
+   "mimetype": "text/x-python",
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+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
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+ "nbformat": 4,
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+}