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.
Authored by Sunny G. (Intel) Last updated on 07/05/2019 - 19:13


Tasks are a lightweight alternative to threads that provide faster startup and shutdown times, better load balancing, an efficient use of available resources, and a higher level of abstraction.
Authored by admin Last updated on 07/05/2019 - 09:51


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

面向英特尔® 至强融核™ 处理器(代号“Knights Landing”)的开发人员访问计划

Intel is bringing to market, in anticipation of general availability of the Intel® Xeon Phi™ Processor (codenamed Knights Landing), the Developer Access Program (DAP). DAP is an early access program for developers worldwide to purchase an Intel Xeon Phi Processor based system.
Authored by Mike P. (Intel) Last updated on 03/21/2019 - 12:00


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 07/05/2019 - 14:48


One key to attaining good parallel performance is choosing the right granularity for the application. Granularity is the amount of real work in the parallel task. If granularity is too fine, then performance can suffer from communication overhead.
Authored by admin Last updated on 07/05/2019 - 19:53

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

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

PARSEC* 3.0 中的多线程代码优化: BlackScholes

The Black-Scholes benchmark is a one of the 13 benchmarks in the PARSEC. This benchmark does option pricing with Black-Scholes Partial Differential Equation (PDE). The Black-Scholes equation is a differential equation that describes how, under a certain set of assumptions, the value of an option changes as the price of the underlying asset changes. Based on this formula, one can compute the...
Authored by Artem G. (Intel) Last updated on 07/04/2019 - 21:42


避免线程之间发生堆冲突 (PDF 256KB)


Authored by admin Last updated on 07/05/2019 - 19:59