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英特尔向量化工具箱:3. 使用英特尔编译器的向量化报告确定候选循环

英特尔向量化工具箱:3. 使用英特尔编译器的向量化报告确定候选循环
作者: Ronald W Green (Blackbelt) 最后更新时间: 2018/05/25 - 15:30
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英特尔向量化工具箱:1. 确定发布版编译的性能测试基准

英特尔向量化工具箱:1. 确定发布版编译的性能测试基准
作者: Ronald W Green (Blackbelt) 最后更新时间: 2018/05/25 - 15:30
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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 15.0.0.070

原因:

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。

作者: Devorah H. (Intel) 最后更新时间: 2019/07/05 - 14:23
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This article completes an analysis of a problem erroneously reported on the Intel® Developer Zone forum: Vectorization failed because of unsigned integer? It provides a more detailed examination showing that unsigned integer is not impacting compiler vectorization but what methodology to use when a modern C/C++ compiler fails to auto-vectorize for-loops.
作者: 最后更新时间: 2019/07/05 - 14:46
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英特尔® 数据分析加速库

The Intel® Data Analytics Acceleration Library (Intel® DAAL) helps speed big data analytics by providing highly optimized algorithmic building blocks for all data analysis stages (Pre-processing, Transformation, Analysis, Modeling, Validation, and Decision Making) for offline, streaming and distributed analytics usages. It’s designed for use with popular data platforms including Hadoop*, Spark*,...
作者: James R. (Blackbelt) 最后更新时间: 2019/08/27 - 13:50
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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