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OpenMP* and the Intel® IPP Library

How to configure OpenMP in the Intel IPP library to maximize multi-threaded performance of the Intel IPP primitives.
Authored by Last updated on 07/31/2019 - 14:30
Article

Code Sample: Exploring MPI for Python* on Intel® Xeon Phi™ Processor

Learn how to write an MPI program in Python*, and take advantage of Intel® multicore architectures using OpenMP threads and Intel® AVX512 instructions.
Authored by Nguyen, Loc Q (Intel) Last updated on 10/15/2019 - 15:30
Article

Fine-Tuning Optimization for a Numerical Method for Hyperbolic Equations Applied to a Porous Media Flow Problem with Intel® Tools

This paper presents an analysis for potential optimization for a Godunov-type semi-discrete central scheme, for a particular hyperbolic problem implicated in porous media flow, using OpenMP* and Intel® Advanced Vector Extensions 2.
Authored by Last updated on 07/03/2019 - 20:00
File Wrapper

Parallel Universe Magazine - Issue 22, September 2015

Authored by admin Last updated on 12/12/2018 - 18:08
File Wrapper

Parallel Universe Magazine - Issue 27, January 2017

Authored by admin Last updated on 10/01/2019 - 16:55
File Wrapper

Parallel Universe Magazine - Issue 24, March 2016

Authored by admin Last updated on 12/12/2018 - 18:08
Article

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 10/15/2019 - 16:50
Article

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 09/30/2019 - 17:28
Article

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 10/15/2019 - 16:40
Article

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 10/15/2019 - 16:40