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OpenMP* SIMD for Inclusive/Exclusive Scans

The Intel® C++ Compiler 19.0 and the Intel® Fortran Compiler 19.1 support the OpenMP* SIMD SCAN feature for inclusive and exclusive scans.
作者: Varsha M. (Intel) 最后更新时间: 2019/07/23 - 09:16
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Virtual Vector Function Supported in Intel® C++ Compiler 17.0

Intel® C++ Compiler 17.0 starts supporting virtual vector functions.

作者: Chen, Yuan (Intel) 最后更新时间: 2019/10/01 - 13:05
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如何在英特尔® 至强融核™ 处理器中使用 MPI-3 共享内存

学习如何在英特尔® 至强融核™ 处理器中使用 MPI-3 共享内存
作者: Nguyen, Loc Q (Intel) 最后更新时间: 2019/10/02 - 15:37
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How to use the MPI-3 Shared Memory in Intel® Xeon Phi™ Processors

Code Sample included: Learn how to use MPI-3 shared memory feature using the corresponding APIs on the Intel® Xeon Phi™ processor.
作者: Nguyen, Loc Q (Intel) 最后更新时间: 2019/10/15 - 15:30
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Explicit Vector Programming in Fortran

No longer does Moore’s Law result in higher frequencies and improved scalar application performance; instead, higher transistor counts lead to increased parallelism, both through more cores and thr

作者: Martyn Corden (Intel) 最后更新时间: 2019/10/15 - 15:30
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Recognize and Measure Vectorization Performance

Get a background on vectorization and learn different techniques to evaluate its effectiveness.
作者: David M. 最后更新时间: 2019/10/15 - 15:30
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Приводим данные и код в порядок: данные и разметка, часть 2

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.
作者: David M. 最后更新时间: 2019/10/15 - 16:40
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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.
作者: David M. 最后更新时间: 2019/10/15 - 16:40
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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.
作者: David M. 最后更新时间: 2019/10/15 - 16:40