诊断信息 15532: 循环无法进行矢量化处理:编译时间不足妨碍了循环进行优化

产品版本: Intel(R) Visual Fortran 编译器 XE


使用 Visual Fortran 编译器的优化选项 ( -O2  -Qopt-report:2 )  时出现矢量化报告,表示编译时间不足妨碍了优化。

Autor Devorah H. (Intel) Última actualización 05/07/2019 - 14:23

基于英特尔® 架构加速金融应用

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Autor George Raskulinec (Intel) Última actualización 06/07/2019 - 16:40

著作 - High Performance Parallelism Pearls

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Autor Mike P. (Intel) Última actualización 21/03/2019 - 12:00

案例研究: 面向神经细胞模拟优化代码

Intel held the Intel® Modern Code Developer Challenge that had about 2,000 students from 130 universities in 19 countries registered to participate in the Challenge. They were provided access to Intel® Xeon Phi™ coprocessors to optimize code used in a CERN openlab brain simulation research project. In this article Daniel Vea Falguera (Modern Code Developer Challenge winner) shares how he...
Autor Última actualización 06/07/2019 - 16:40

整理您的数据和代码: 优化和内存 — 第 1 部分

This series of two articles discusses how data and memory layout affect performance and suggests specific steps to improve software performance. The basic steps shown in these two articles can yield significant performance gains. These two articles are designed at an intermediate level. It is assumed the reader desires to optimize software performance using common C, C++ and Fortran* programming...
Autor David M. Última actualización 12/12/2018 - 18:00
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Check out the (non) CRC implementation below. What's wrong with it? I'm working on a connectivity library for IoT devices. A serious part of every communication protocol is the data integrity check. You send a collection of bytes to another machine and that machine has to know that there were no errors on the way. IP for example already has a good integrity check. The problem is that a TCP socket...
Autor Última actualización 23/06/2019 - 19:10
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An Intro to Multi-Level Parallelism for High-Performance Computing by Clay Breshears | Life Sciences Software Architect, Intel
Autor Clay B. (Blackbelt) Última actualización 12/12/2018 - 18:08

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本文将介绍一些技巧,帮助软件开发人员识别并修复使用最新英特尔软件开发工具时遇到的与 NUMA 相关的应用性能问题。

Autor Sunny G. (Intel) Última actualización 05/07/2019 - 20:13

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.
Autor David M. Última actualización 06/07/2019 - 16:40
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Autor Última actualización 12/12/2018 - 18:08