Documentation

Utilities from Intel® C++ Compiler Code Samples

This section contains the following utility classes:

Last updated on 03/21/2019 - 09:08
Documentation

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

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

Last updated on 03/21/2019 - 09:08
Documentation

Classic Algorithms from 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.

Last updated on 03/21/2019 - 09:08
Documentation

Support from 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.

Last updated on 03/21/2019 - 09:08
Article

Vectorizing Loops with Calls to User-Defined External Functions

Introduction

Authored by Anoop M. (Intel) Last updated on 12/12/2018 - 18:00
Documentation

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

Merge sort algorithm is a comparison-based sorting algorithm.

Last updated on 03/21/2019 - 09:08
Documentation

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

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

Last updated on 03/21/2019 - 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.
Authored by David M. Last updated on 07/06/2019 - 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.
Authored by David M. Last updated on 07/06/2019 - 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.
Authored by David M. Last updated on 07/06/2019 - 16:40