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Apply the concepts of parallelism and distributed memory computing to your code to improve software performance. This paper expands on concepts discussed in Part 1, to consider parallelism, both vectorization (single instruction multiple data SIMD) as well as shared memory parallelism (threading), and distributed memory computing.
Authored by David M. Last updated on 07/06/2019 - 16:40
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Matrix multiplication (MM) of two matrices is one of the most fundamental operations in linear algebra. The algorithm for MM is very simple, it could be easily implemented in any programming language. This paper shows that performance significantly improves when different optimization techniques are applied.
Authored by Last updated on 06/14/2019 - 11:50
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This document is designed to help users get started writing code and running MPI applications using the Intel® MPI Library on a development platform that includes the Intel® Xeon Phi™ processor.
Authored by Nguyen, Loc Q (Intel) Last updated on 03/21/2019 - 12:00
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Code Sample included: Learn how to use MPI-3 shared memory feature using the corresponding APIs on the Intel® Xeon Phi™ processor.
Authored by Nguyen, Loc Q (Intel) Last updated on 07/06/2019 - 16:30
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Recognize and Measure Vectorization Performance

Get a background on vectorization and learn different techniques to evaluate its effectiveness.
Authored by David M. Last updated on 07/06/2019 - 16:40