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Android* 教程:使用英特尔® 线程构建模块编写多线程应用

近来,我们发布了 “Windows* 8 教程:使用英特尔® 线程构建模块为 Win

Autor Vladimir Polin (Intel) Última actualización 09/05/2019 - 13:00
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Управление режимами вычислений с плавающей запятой при использовании Intel® Threading Building Blocks

В Intel® Threading Building Blocks (Intel® TBB) 4.2, обновление 4, появилась расширенная поддержка управления параметрами вычислений с плавающей запятой.

Autor Alex (Intel) Última actualización 01/08/2019 - 09:30
Forum topic

TBB Warning: Leaked 1 observer_proxy objects.

Hi,

Recently I added my custom task scheduler observer. I got the following warning when debugging:

TBB Warning: Leaked 1 observer_proxy objects.

Autor wzpstbb Última actualización 12/03/2015 - 00:08
Forum topic

Is there any way to observe the threads a task scheduler is managing?

Hello,

We have an OpenGL application. In each frame, we run the following piece of code. 

Autor wzpstbb Última actualización 13/04/2015 - 23:22
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借助 SIMD 数据布局模板优化数据布局

Financial service customers need to improve financial algorithmic performance for models such as Monte Carlo, Black-Scholes, and others. SIMD programming can speed up these workloads. In this paper, we perform data layout optimizations using two approaches on a Black-Scholes workload for European options valuation from the open source Quantlib library.
Autor Nimisha R. (Intel) Última actualización 12/12/2018 - 18:00
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高效并行化

高效并行化文档

面向英特尔® 集成众核架构的编译器方法

高效并行化

Autor Ronald W Green (Blackbelt) Última actualización 30/09/2019 - 17:30
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Привязка потоков (affinity) в Intel® Threading Building Blocks на сопроцессоре Intel® Xeon Phi™

Библиотека Intel® Threading Building Blocks (Intel® TBB) [1] [2] предоставляет высокоуровневые интерфейсы для написания программ, использующих параллельные

Autor Alex (Intel) Última actualización 15/10/2019 - 15:30
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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.
Autor David M. Última actualización 15/10/2019 - 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.
Autor David M. Última actualización 15/10/2019 - 16:40
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应用蚁群优化算法 (ACO) 实施交通网络扩展

In this article an OpenMP* based implementation of the Ant Colony Optimization algorithm was analyzed for bottlenecks with Intel® VTune™ Amplifier XE 2016 together with improvements using hybrid MPI-OpenMP and Intel® Threading Building Blocks were introduced to achieve efficient scaling across a four-socket Intel® Xeon® processor E7-8890 v4 processor-based system.
Autor Sunny G. (Intel) Última actualización 15/10/2019 - 16:40