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最大限度提升 CPU 上的 TensorFlow* 性能:推理工作负载的注意事项和建议

本文将介绍使用面向 TensorFlow 的英特尔® 优化* 进行 CPU 推理的性能注意事项
Authored by Nathan Greeneltch (Intel) Last updated on 08/09/2019 - 02:02
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面向英特尔® 架构优化的 Caffe*:使用现代代码技巧

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.
Authored by Last updated on 07/06/2019 - 16:40
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准确预报各种天气:英特尔五步框架帮助实现代码现代化

天气预报是现代生活的一个重要方面,它可在出现恶劣天气状况时即时发出警报,从而帮助有效制定计划和安排物流,并可保护生命财产安全。 但是,准确预测长期的天气情况非常复杂,通常涉及到大量数据集,并且要求对代码进行优化以利用最高级的计算机硬件功能。

Authored by Last updated on 03/21/2019 - 12:00
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英特尔® 至强融核™ 协处理器(代号 “Knights Landing”)— 应用就绪

为了将来在英特尔® 至强™ 处理器和英特尔® 至强融核™ 协处理器(代号 Knights Landing)上实现部分应用就绪,开发人员主要希望从两个方面改进工作负载:

矢量化/代码生成 线程并行性

本文主要讨论矢量化/代码生成,并介绍了一些有用的线程并行工具和资源。

Authored by Last updated on 07/06/2019 - 16:40
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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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了解面向三维同性有限差分 (3DFD) 波动方程代码的 NUMA

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。

Authored by Sunny G. (Intel) Last updated on 07/05/2019 - 20:13
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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...
Authored by Last updated on 07/06/2019 - 16:40
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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.
Authored by David M. Last updated on 07/06/2019 - 16:40
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Vectorization Advisor 助您一臂之力

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!”
Authored by Last updated on 07/06/2019 - 16:40
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安装英特尔® Theano*软件优化包和支持工具

Theano* is a Python* library developed at the LISA lab to define, optimize, and evaluate mathematical expressions, including the ones with multi-dimensional arrays. Theano can be installed and used with several combinations of development tools and libraries on a variety of platforms. This tutorial provides one such recipe describing steps to build and install Intel-optimized Theano with Intel®...
Authored by Sunny G. (Intel) Last updated on 05/08/2018 - 10:50