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面向英特尔® 至强融核™ 协处理器(和英特尔® 至强® 处理器)架构应用的浮点计算 R2R 再现性

 

问题

如果在相同处理器上针对相同输入数据重新运行相同的程序,得到的结果相同吗?

Authored by Last updated on 03/21/2019 - 12:08
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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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Run-to-Run Reproducibility of Floating-Point Calculations for Applications on Intel® Xeon Phi™ Coprocessors (and Intel® Xeon® Processors)

The Issue

If I rerun the identical program on the identical input data on an identical processor, will I get an identical result?

Authored by Martyn Corden (Intel) Last updated on 06/14/2019 - 12:10
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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

Authored by Martyn Corden (Intel) Last updated on 03/27/2019 - 15:50
Blog post

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