From c42bf8bb5bfe6ab968bcc7dc8f043d69ce6c82cf Mon Sep 17 00:00:00 2001
From: Repo Updater <noreply@mpcdf.mpg.de>
Date: Thu, 17 Nov 2022 08:39:18 +0100
Subject: [PATCH] 82f97415 some polishing of the first part of parallel
---
...--Parallel_Programming_Shared_Memory.ipynb | 14 +-
notebooks/4b--Parallel_Frameworks.ipynb | 27 +-
notebooks/fig/dask-array.svg | 349 ++++++++++++++++++
3 files changed, 375 insertions(+), 15 deletions(-)
create mode 100644 notebooks/fig/dask-array.svg
diff --git a/notebooks/4a--Parallel_Programming_Shared_Memory.ipynb b/notebooks/4a--Parallel_Programming_Shared_Memory.ipynb
index d8d2fdc..ab82f36 100644
--- a/notebooks/4a--Parallel_Programming_Shared_Memory.ipynb
+++ b/notebooks/4a--Parallel_Programming_Shared_Memory.ipynb
@@ -258,11 +258,10 @@
"* Processes (shared memory)\n",
" * `multiprocessing`, comparably easy to use\n",
" * restricted to one node\n",
- "* MPI (distributed memory)\n",
+ "* MPI or parallel frameworks (distributed memory)\n",
" * `mpi4py`\n",
" * more complex\n",
- " * can be used over a cluster\n",
- " * separate lecture"
+ " * can be used over a cluster"
]
},
{
@@ -318,7 +317,9 @@
"* Idea: Start several processes on one node to distribute the workload over CPUs\n",
"* Use processes instead of threads $\\to$ sidestep the global interpreter lock\n",
"* `multiprocessing` module, part of the Python standard library\n",
- "* Most important use case: Data parallelism with the `Pool` class"
+ "* Most important use case: Data parallelism with the `Pool` class\n",
+ "* Explicit interprocess communication possible via Queues\n",
+ "* https://docs.python.org/3/library/multiprocessing.html"
]
},
{
@@ -461,9 +462,10 @@
"### Asynchronous parallel computing via `concurrent.futures`\n",
"\n",
"* `concurrent.futures` enables the *asynchronous* execution of tasks\n",
- "* useful with `ProcessPoolExecutor` which actually builds upon `multiprocessing`\n",
+ "* useful with `ProcessPoolExecutor()` which actually builds upon `multiprocessing`\n",
"* `map` offloads tasks to the worker Pool and returns an iterator that is order-preserving\n",
- "* 'futures' are tasks that are offloaded via `submit()`, `as_completed` provides an iterator in completion-order"
+ "* 'futures' are tasks that are offloaded via `submit()`, `as_completed` provides an iterator in completion-order\n",
+ "* https://docs.python.org/3/library/concurrent.futures.html"
]
},
{
diff --git a/notebooks/4b--Parallel_Frameworks.ipynb b/notebooks/4b--Parallel_Frameworks.ipynb
index fc37ee6..84b37c0 100644
--- a/notebooks/4b--Parallel_Frameworks.ipynb
+++ b/notebooks/4b--Parallel_Frameworks.ipynb
@@ -27,7 +27,7 @@
"* Motivation\n",
"* Overview on Parallel Frameworks\n",
"* Example: Dask\n",
- " * Concepts\n",
+ " * Basic concepts\n",
" * Dask-MPI on a Slurm cluster"
]
},
@@ -82,7 +82,7 @@
" * not designed to run web services (e.g. dashboards, scheduler services)\n",
" * workloads are submitted via batch jobs\n",
" * non-interactive use highly preferred\n",
- " * limited support by Python frameworks for parallel computing (exception: Dask), often workarounds needed"
+ " * sometimes workarounds are needed to get Python frameworks for parallel computing to run (exception: Dask-MPI)"
]
},
{
@@ -98,13 +98,14 @@
"* Dask is a library for parallel distributed computing in Python\n",
"* Components\n",
" * resource abstraction called Cluster (a potentially distributed scalable pool of workers), e.g.\n",
- " * LocalCluster()\n",
- " * SSHCluster()\n",
- " * MPI-based cluster\n",
+ " * LocalCluster() : default, workers on local machine\n",
+ " * SSHCluster() : workers on remote machines accessible via SSH\n",
+ " * KubeCluster() : workers on pods running in a Kubernetes cluster\n",
+ " * MPI-based cluster : workers running as MPI tasks on an HPC system\n",
" * dynamic task scheduler\n",
" * parallel collections such as arrays and dataframes\n",
"\n",
- "\n",
+ "\n",
"(image credit: dask documentation)"
]
},
@@ -120,7 +121,11 @@
"\n",
"* *Dask Array* implements a subset of the NumPy ndarray interface using blocked algorithms\n",
"* it cuts up the large array into many small arrays for parallel distributed processing\n",
- "* Dask blocked algorithms are coordinated using Dask graphs"
+ "* Dask blocked algorithms are mapped to tasks and coordinated using Dask graphs\n",
+ "* not limited to native NumPy, supports generic ndarray-like implementations (CuPy, H5PY)\n",
+ "\n",
+ "\n",
+ "(image credit: dask documentation)"
]
},
{
@@ -626,7 +631,7 @@
"\n",
"* eager evaluation of custom functions via the Dask workers\n",
"* Dask futures implements most of `concurrent.futures` for distributed processing\n",
- " * `Client.map()` $\\to$ returns an iterator containing the return values of the map'ped function\n",
+ " * `Client.map()` $\\to$ returns an iterator on the return values of the map'ped function\n",
" * `Client.submit()` $\\to$ return value must be fetched using the (blocking) `result()` call of the futures object\n",
"* futures (i.e. function and its arguments) are sent as tasks to remote workers for processing, the Dask scheduler takes care of building and updating the dependency graph"
]
@@ -634,7 +639,11 @@
{
"cell_type": "code",
"execution_count": 25,
- "metadata": {},
+ "metadata": {
+ "slideshow": {
+ "slide_type": "subslide"
+ }
+ },
"outputs": [
{
"name": "stdout",
diff --git a/notebooks/fig/dask-array.svg b/notebooks/fig/dask-array.svg
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