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Code Sample: Exploring MPI for Python* on Intel® Xeon Phi™ Processor

Learn how to write an MPI program in Python*, and take advantage of Intel® multicore architectures using OpenMP threads and Intel® AVX512 instructions.
Authored by Nguyen, Loc Q (Intel) Last updated on 07/06/2019 - 16:30
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Process and Thread Affinity for Intel® Xeon Phi™ Processors

The Intel® MPI Library and OpenMP* runtime libraries can create affinities between processes or threads, and hardware resources. This affinity keeps an MPI process or OpenMP thread from migrating to a different hardware resource, which can have a dramatic effect on the execution speed of a program.
Authored by Gregg S. (Intel) Last updated on 03/21/2019 - 12:00
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Parallel Universe Magazine - Issue 16, November 2013

Authored by admin Last updated on 12/12/2018 - 18:08
Video

Part 9: Distributed-Memory Parallelism and MPI

In the previous episodes of this chapter, we learned how to use vectorization to parallelize calculations across vector lanes in each core.

Authored by admin Last updated on 03/21/2019 - 12:00
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Parallel Universe Magazine - Issue 18, June 2014

Authored by admin Last updated on 05/16/2019 - 11:39
Video

第 9 集:分布式内存并行化和 MPI

在本章上一集中,我们学习了如何使用矢量在每个内核的矢量平面间并行化计算。 然后,我们讨论了如何使用 OpenMP 在每颗处理器或协处理器的内核间扩展应用。 接下来,在本章最后一集 4.9 集中,我们将研究下一级别的并行化:在多台计算设备和集群环境的多个计算节点间扩展。

Authored by Last updated on 04/26/2019 - 04:06
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Parallel Universe Magazine - Issue 24, March 2016

Authored by admin Last updated on 12/12/2018 - 18:08
Article

著作 - High Performance Parallelism Pearls

A look into the contents of the two "Pearls" books, edited by James Reinders and Jim Jeffers. These books contain a collection of examples of code modernization.
Authored by Mike P. (Intel) Last updated on 03/21/2019 - 12:00
Article

Books - High Performance Parallelism Pearls

A look into the contents of the two "Pearls" books, edited by James Reinders and Jim Jeffers. These books contain a collection of examples of code modernization.
Authored by Mike P. (Intel) Last updated on 03/21/2019 - 12:00
Article

Hybrid Parallelism: Parallel Distributed Memory and Shared Memory Computing

There are two principal methods of parallel computing: distributed memory computing and shared memory computing. As more processor cores are dedicated to large clusters solving scientific and engineering problems, hybrid programming techniques combining the best of distributed and shared memory programs are becoming more popular.
Authored by David M. Last updated on 07/12/2019 - 08:31