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Efficient Parallelization

This article is part of the Intel® Modern Code Developer Community documentation which supports developers in leveraging application performance in code through a systematic step-by-step optimization framework methodology. This article addresses: Thread level parallelization.
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Improving Averaging Filter Performance Using Intel® Cilk™ Plus

Intel® Cilk™ Plus is an extension to the C and C++ languages to support data and task parallelism.  It provides three new keywords to i

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Explicit Vector Programming – Best Known Methods

Vectorizing improves performance, and achieving high performance can save power. Introduction to tools for vectorizing compute-intensive processing.
Authored by Last updated on 04/24/2019 - 11:25
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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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In this pair of articles on performance and memory covers basic concepts to provide guidance to developers seeking 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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Improve Performance with Vectorization

This article focuses on the steps to improve software performance with vectorization. Included are examples of full applications along with some simpler cases to illustrate the steps to vectorization.
Authored by David M. Last updated on 07/06/2019 - 16:40
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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