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Parallel Universe Magazine #12: Advanced Vectorization

This blog contains additional content for the article "Advanced Vectorization" from Parallel Universe #12:

Автор: Последнее обновление: 03.07.2019 - 20:08
Article

Intel® System Studio - Multicore Programming with Intel® Cilk™ Plus

Intel System Studio not only provides a variety of signal processing primitives via Intel® Integrated Performance Primitives (Intel® IPP), and Intel® Math Kernel Library (Intel® MKL), but also allows developing high-performance low-latency custom code (Intel C++ Compiler with Intel Cilk Plus). Since Intel Cilk Plus is built into the compiler, it can be used where it demands an efficient threading...
Автор: Hans P. (Intel) Последнее обновление: 11.12.2017 - 10:48
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

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

Explicit Vector Programming – Best Known Methods

Vectorizing improves performance, and achieving high performance can save power. Introduction to tools for vectorizing compute-intensive processing.
Автор: Последнее обновление: 24.04.2019 - 11:25
Article

借助英特尔® Cilk™ Plus 实现并行化

面向英特尔® MIC 架构的编译器方法 高效并行化,借助英特尔® Cilk™ Plus 实现并行化

概述

Автор: Ronald W Green (Blackbelt) Последнее обновление: 30.09.2019 - 17:30
Article

Parallelization with Intel® Cilk™ Plus

Compiler Methodology for Intel® MIC Architecture

Автор: Ronald W Green (Blackbelt) Последнее обновление: 30.09.2019 - 17:28
Article

Improve Performance with Vectorization

This article focuses on the steps to improve software performance with vectorization. Included are examples of full applications along with some simpler cases to illustrate the steps to vectorization.
Автор: David M. Последнее обновление: 15.10.2019 - 15:30
Article

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.
Автор: David M. Последнее обновление: 15.10.2019 - 16:40
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.
Автор: David M. Последнее обновление: 15.10.2019 - 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.
Автор: David M. Последнее обновление: 15.10.2019 - 16:40