学习如何在英特尔® 至强融核™ 处理器中使用 MPI-3 共享内存
Vectorization Advisor is like having a trusted friend look over your code and give you advice based on what he sees. As you’ll see in this article, user feedback on the tool has included, “there are significant speedups produced by following advisor output, I'm already sold on this tool!”
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。
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...
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.
This paper demonstrates a special version of Caffe* — a deep learning framework originally developed by the Berkeley Vision and Learning Center (BVLC) — that is optimized for Intel® architecture.