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Parallelization Using OpenMP*

 

Compiler Methodology for Intel® MIC Architecture

Authored by admin Last updated on 10/01/2019 - 12:34
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New Features for Intel® MIC Architecture in the Intel Compiler

The list below summarizes new features and changes specific to programming for Intel® MIC Architecture with Intel Compiler 15.0:

Authored by AmandaS (Intel) Last updated on 10/01/2019 - 12:34
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Parallelization Using Intel® Threading Building Blocks (Intel® TBB)

Compiler Methodology for Intel® MIC Architecture

Authored by admin Last updated on 08/01/2019 - 09:30
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整理您的数据和代码: 数据和布局 - 第 2 部分

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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Modernizing Software with Future-Proof Code Optimizations

by Henry A. Gabb, Sr. Principal Engineer, Intel Software and Services Group

Authored by Henry Gabb (Intel) Last updated on 07/06/2019 - 17:10
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Optimizing Applications using Intel® Compiler for Intel® Xeon Processors

The key to performance measurement is two-fold, know exactly what you are measuring and collect your baseline data. Next, profile your application and identify a specific and realistic performance goal based on the profiling data. Follow these steps to optimize your software.
Authored by AmandaS (Intel) Last updated on 03/27/2019 - 14:12
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Application Analysis for Intel® MIC Architecture Suitability

Compiler Methodology for Intel® MIC Architecture

Authored by AmandaS (Intel) Last updated on 10/01/2019 - 12:34
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BKMs on the use of the SIMD directive

We had an ask from one of the various "Birds of a Feather" meetings Intel® holds at venues such as at the Super Computing* (SC) and International Super Computing* (ISC) conferences.

Authored by Last updated on 07/06/2019 - 17:00
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Putting Your Data and Code in Order: Data and layout - Part 2

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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Transform Enterprise, HPC & AI, Accelerate Parallel Code

Authored by admin Last updated on 07/06/2019 - 16:15