Hybrid Parallelism: A MiniFE* Case Study

This case study examines the situation where the problem decomposition is the same for threading as it is for Message Passing Interface* (MPI); that is, the threading parallelism is elevated to the same level as MPI parallelism.
Authored by David M. Last updated on 10/14/2016 - 11:51

Palestra: Como otimizar seu código sem ser um "ninja" em Computação Paralela

Não perca a palestra "Como otimizar seu código sem ser um "ninja" em Computação Paralela" da Intel que será ministrada durante a Semana sobre Programação Massivamente Paralela em Petrópolis, RJ, no Laboratório Nacional de Computação Científica. Data: 02/02/2016 - 11h30 Local: LNCC - Av. Getúlio Vargas, 333 - Quitandinha - Petrópolis/RJ
Authored by Igor F. (Intel) Last updated on 10/20/2016 - 16:19

Optimizing Without Breaking a Sweat

This article describes novel techniques developed to optimize DreamWork Animation's rendering, animation, and special effects applications without recompiling or relinking by preloading highly optimized libraries at run-time.
Authored by John O (Intel) Last updated on 10/20/2016 - 16:19

Get Ground-Breaking Media Performance - Connect with Intel at IBC 2016

Visit with Intel at IBC in Amsterdam, Sept. 9 to 13. Preview exciting demos of Intel's leading media technologies - which help the media industry, broadcasters, video solution providers and more - deliver visually-stunning viewing experiences. With video internet content exploding and UHD TV purchases increasing at high rates, media and video solution providers need to stay competitive by...
Authored by Brenda C. (Intel) Last updated on 10/14/2016 - 01:46


When confronted with nested loops, the granularity of the computations that are assigned to threads will directly affect performance. Loop transformations such as splitting and merging nested loops can make parallelization easier and more productive.
Authored by admin Last updated on 10/13/2016 - 01:34

使用英特尔® Parallel Composer 在代码中添加并行执行能力

This article provides an overview of the methods available in Intel® Parallel Composer, along with a comparison of their key benefits.
Authored by mark-sabahi (Intel) Last updated on 10/24/2016 - 04:12

线程化与英特尔® IPP 高效多媒体函数库

线程化与英特尔® IPP 高效多媒体函数库 (PDF 230KB)

Authored by admin Last updated on 10/24/2016 - 03:44

英特尔® 数学核心函数库中的并行性

The Intel® Math Kernel Library (Intel® MKL) contains a large collection of functions that can benefit math-intensive applications.
Authored by admin Last updated on 10/12/2016 - 00:26

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 10/09/2016 - 03:01
For more complete information about compiler optimizations, see our Optimization Notice.