Article

Elemental functions: Writing data parallel code in C/C++ using Intel® Cilk™ Plus

Intel® Cilk™ Plus provides simple to use language extensions to express data and task-parallelism to the C and C++ language. This article describes one of these programming constructs: “elemental functions”.
作者: 最后更新时间: 2018/12/31 - 15:00
Article

Getting Started with Intel® Cilk™ Plus SIMD Vectorization and SIMD-enabled Functions

A tutorial on how to use #pragma simd and SIMD-enabled function features in Intel® Cilk™ Plus.
作者: 最后更新时间: 2018/05/25 - 15:30
Article

Getting Started with Intel® Cilk™ Plus Array Notations

A simple introduction on how use Array Notations feature in Intel® Cilk™ Plus.
作者: 最后更新时间: 2018/05/25 - 15:30
Article

Intel® Cilk™ Plus – AOBench Sample

This is the AOBench example associated with the "Intel® Cilk™ Plus – The Simplest Path to Parallelism" how-to article.  It shows an Ambient Occlusion algorithm implemented as serial loops, one us
作者: 最后更新时间: 2018/05/25 - 15:30
Article

Best Practices for Using Intel® Cilk™ Plus

Performance tuning of an existing application is truly a challenge and it depends on a lot of factors like the nature of algorithm the application works on, if the implementation is scalable

作者: Anoop M. (Intel) 最后更新时间: 2018/05/25 - 15:30
Article

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

作者: Anoop M. (Intel) 最后更新时间: 2018/12/12 - 18:00
Article

Vectorizing Loops with Calls to User-Defined External Functions

Introduction

作者: Anoop M. (Intel) 最后更新时间: 2018/12/12 - 18:00
博客

Optimizing Big Data processing with Haswell 256-bit Integer SIMD instructions

Big Data requires processing huge amounts of data. Intel Advanced Vector Extensions 2 (aka AVX2) promoted most Intel AVX 128-bits integer SIMD instruction sets to 256-bits.

作者: gaston-hillar (Blackbelt) 最后更新时间: 2019/07/06 - 17:00
Article

Putting Your Data and Code in Order: Data and layout - Part 2

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.
作者: David M. 最后更新时间: 2019/07/06 - 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.
作者: David M. 最后更新时间: 2019/07/06 - 16:40