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.
Автор: Последнее обновление: 31.07.2019 - 14:30

Improving Averaging Filter Performance Using Intel® Cilk™ Plus

Intel® Cilk™ Plus is an extension to the C and C++ languages to support data and task parallelism.  It provides three new keywords to i

Автор: Anoop M. (Intel) Последнее обновление: 12.12.2018 - 18:00

Empowering Oil and Gas Applications for Intel® Xeon®, Xeon Phi™ architectures using Intel® Tools

Improving the performance of software applications is a constant challenge for software developers in the Oil and Gas industry.

Автор: админ Последнее обновление: 06.07.2019 - 19:20

Eight Optimizations for 3-Dimensional Finite Difference (3DFD) Code with an Isotropic (ISO)

This article describes how to implement and optimize a three-dimension isotropic kernel with finite differences to run on the Intel® Xeon® Processor and Intel® Xeon Phi™.
Автор: Cédric ANDREOLLI (Intel) Последнее обновление: 06.07.2019 - 16:40

Part 1: SIMD Parallelism and Intrinsics

In the previous lectures, we already discussed the purpose and the architecture of Intel® Xeon Phi™ coprocessors.

Автор: админ Последнее обновление: 21.03.2019 - 12:00

Part 1: Introduction

By: Vadim Karpusenko, PhD; Andrey Vladimirov, PhD; Ryo Asai

In this episode we will cover structure of this video course.

Videos Within This Chapter:

Автор: админ Последнее обновление: 26.02.2019 - 09:08

第 1 集:SIMD 并行化和内联函数

在之前的讲座中,我们已经讨论了英特尔® 至强融核™ 协处理器的目的和架构。 之后,我们研究了面向英特尔至强融核协处理器的编程模型:本机和卸载。 现在我们进入第四章“表达并行化”。 在本章中,我们的目标是学习如何在应用中针对英特尔至强处理器和至强融核协处理器表达数据并行化、线程并行化和进程并行化。

Автор: Последнее обновление: 26.04.2019 - 04:05

Code Sample: Allocate Memory Efficiently on an Intel® Xeon Phi™ Processor

How to efficiently use Multi-Channel DRAM (MCDRAM) and synchronous dynamic random-access memory.
Автор: Mike P. (Intel) Последнее обновление: 06.07.2019 - 16:40

Reduce Boilerplate Code in Parallelized Loops with C++11 Lambda Expressions

Parallelize loops with Intel® Threading Building Blocks using Intel® C++ Compiler for lambda expressions.
Автор: gaston-hillar (Blackbelt) Последнее обновление: 12.12.2018 - 18:00

Recognize and Measure Vectorization Performance

Get a background on vectorization and learn different techniques to evaluate its effectiveness.
Автор: David M. Последнее обновление: 06.07.2019 - 16:40