Документация

Utilities в Intel® C++ Compiler Code Samples

This section contains the following utility classes:

Последнее обновление: 21.03.2019 - 09:08
Документация

Finance: Monte Carlo в Intel® C++ Compiler Code Samples

Monte Carlo algorithms solve deterministic problems by using a probabilistic analogue.

Последнее обновление: 21.03.2019 - 09:08
Документация

Classic Algorithms в Intel® C++ Compiler Code Samples

The followings are samples to demonstrate the Intel(R) Cilk(TM) Plus implementations and its performance benefits for the popular classic algorithms.

Последнее обновление: 21.03.2019 - 09:08
Документация

Support в Intel® C++ Compiler Code Samples

Intel(R) C++ Compiler product

To learn more about Intel(R) C++ Compiler products, please go to Intel(R) C++ Compiler.

Последнее обновление: 21.03.2019 - 09:08
Article

Vectorizing Loops with Calls to User-Defined External Functions

Introduction

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

Sorting Algorithms: Merge Sort в Intel® C++ Compiler Code Samples

Merge sort algorithm is a comparison-based sorting algorithm.

Последнее обновление: 21.03.2019 - 09:08
Документация

Graph Algorithms: Shortest Path в Intel® C++ Compiler Code Samples

Dijkstra algorithm is a graph search algorithm that solves the single-source shortest path problem for a graph wit

Последнее обновление: 21.03.2019 - 09:08
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: 06.07.2019 - 16:40
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: 06.07.2019 - 16:40
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: 06.07.2019 - 16:40