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Using Intel® Advisor to Observe Memory Accesses Dynamically

Intel® Advisor is the premiere tool from Intel for helping to parallelize your code, but it can also be used in other ways.

Автор: Последнее обновление: 31.01.2019 - 09:01
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Improve Intel® MKL Performance for Small Problems: The Use of MKL_DIRECT_CALL

One of the big new features introduced in the Intel® Math Kernel Library (Intel® MKL) 11.2 is the greatly improved performance for small problem sizes.

Автор: Zhang, Zhang (Intel) Последнее обновление: 07.07.2019 - 10:35
Article

Further Vectorization Features of the Intel® Compiler - Webinar Code Samples

The code samples for the webinar "Further Vectorization Features of the Intel® Compiler" given on 4/7/2015 are attached below.

Автор: Martyn Corden (Intel) Последнее обновление: 11.07.2018 - 19:21
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How to Gain Control of Debugging a Fortran Coarray Application

Learn a method for controlling one instance of a Fortran Coarray application while debugging it in Visual Studio for Windows.
Автор: Последнее обновление: 30.05.2018 - 07:00
Article

Array Shape Check: New in Intel® Fortran Compiler 19.0

Array Shape Check: New in Intel® Fortran Compiler 19.0

Автор: Barbara P (Intel) Последнее обновление: 03.07.2019 - 20:00
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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

Автор: Martyn Corden (Intel) Последнее обновление: 15.10.2019 - 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.
Автор: David M. Последнее обновление: 15.10.2019 - 16:40
Article

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.
Автор: David M. Последнее обновление: 15.10.2019 - 16:40
Article

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.
Автор: David M. Последнее обновление: 15.10.2019 - 16:40
Article

Distributed Memory Coarray Programs with Process Pinning

This article describes a method to compile and run a distributed memory coarray program using Intel® Parallel Studio XE Cluster Edition for Linux . An example using Linux* is presented.
Автор: Kenneth Craft (Intel) Последнее обновление: 15.10.2019 - 21:20