## Filtros

Documentação

### Utilities de Intel® C++ Compiler Code Samples

This section contains the following utility classes:

Última atualização em 21/03/2019 - 09:08
Documentação

### Finance: Monte Carlo de Intel® C++ Compiler Code Samples

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

Última atualização em 21/03/2019 - 09:08
Documentação

### Classic Algorithms de 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.

Última atualização em 21/03/2019 - 09:08
Documentação

### Support de Intel® C++ Compiler Code Samples

Intel(R) C++ Compiler product

Última atualização em 21/03/2019 - 09:08
Article

### Vectorizing Loops with Calls to User-Defined External Functions

Introduction

Criado por Anoop M. (Intel) Última atualização em 12/12/2018 - 18:00
Documentação

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

Merge sort algorithm is a comparison-based sorting algorithm.

Última atualização em 21/03/2019 - 09:08
Documentação

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

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

Última atualização em 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.
Criado por David M. Última atualização em 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.
Criado por David M. Última atualização em 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.
Criado por David M. Última atualização em 06/07/2019 - 16:40