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-->Microsoft MPI (MS-MPI) is a Microsoft implementation of the Message Passing Interface standard for developing and running parallel applications on the Windows platform.
MS-MPI offers several benefits:
- Ease of porting existing code that uses MPICH.
- Security based on Active Directory Domain Services.
- High performance on the Windows operating system.
- Binary compatibility across different types of interconnectivity options.
MS-MPI Source Code
Microsoft MPI source code is available on GitHub.
MS-MPI Downloads
The following are current downloads for MS-MPI:
- MS-MPI v10.1.2 (new!) - see Release notes
Earlier versions of MS-MPI are available from the Microsoft Download Center.
Community Resources
Microsoft High Performance Computing Resources
- Featured tutorial: How to compile and run a simple MS-MPI program
- Featured guide: Set up a Windows RDMA cluster with HPC Pack and A8 and A9 instances to run MPI applications
Related Topics
This tutorial is targeted at the intermediate-to-advanced Python user whowants to extend Python into High-Performance Computing. The tutorial willprovide hands-on examples and essential performance tips every developershould know for writing effective parallel Python. The result will be a clearsense of possibilities and best practices using Python in HPC environments.
Many of the examples you often find on parallel Python focus on the mechanicsof getting the parallel infrastructure working with your code, and not onactually building good portable parallel Python. This tutorial is intended tobe a broad introduction to writing high-performance parallel Python that iswell suited to both the beginner and the veteran developer. Parallel efficiencystarts with the speed of the target code itself, so we will start with how toevolve code from for-loops to Python looping constructs and vector programming.We will also discuss tools and techniques to optimize your code for speed andmemory performance.
The tutorial will overview working with the common parallelcommunication technologies (threading, multiprocessing, MPI) and introduce theuse of parallel programming models such as blocking and non-blocking pipes,asynchronous and iterative conditional maps, and map-reduce. We will discussstrategies for extending parallel workflow to utilize hierarchical andheterogeneous computing, distributed parallel computing, and job schedulers.We then return our focus to the speeding up our target code by leveragingparallelism within compiled code using Cython.
At the end of the tutorial,participants should be able to write simple parallel Python scripts, make useof effective parallel programming techniques, and have a framework in place toleverage the power of Python in High-Performance Computing.
Content
All tutorial content can be obtained from this repository either with`git, or by downloading the repository content as a zip file. If you usegit, you can clone this repostory with::
or, download and unzip the zipfile.
As the day of the tutorial get nearer, it is highly recommended to updatethis repository. When tutorial content is added or modified, it isrecommended to update your copy of the tutorial. Tutorial content may beupdated up to the day of the tutorial, during the tutorial, and beyond.To update your copy of the tutorial content with git, change to the tutorialdirectory (i.e.
tuthpc
), then pull an update with::or, download and unzip a new copy of the zipfile.
Requirements
To be able to run the examples, demos, and exercises in this tutorial,the following packages must be installed::
and optionally::
Installation
All packages can be installed with
pip
::and optionally::
The install of
numpy
and llvmlite
can fail. A more stable choice forinstalling these two packages is to use a scientific python distributionsuch as canopy
or anaconda
.Note that on windows,
mpi4py
is known to be a difficult install, andcommonly fails with pip
(or conda
, etc). You may be able to get acompatible wheel at either of these two locations::The optional install for
ipython-parallel
also can be difficult, andcommonly fails with pip
(or conda
, etc). Compatible wheels may beavailable at this location::The optional install for
pyina
is not supported on Windows.The following steps were used by the tutorial author to test on Windows:
Verification
![Hpc Code Source Crackers Hpc Code Source Crackers](/uploads/1/2/5/8/125848418/290139031.png)
To test your installation, change to the tutorial directory, and run::
If you choose not install all optional dependencies, you will see a warning::
Feel free to ignore warnings for optional dependencies.