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Utilities 来自 Intel® C++ Compiler Code Samples

This section contains the following utility classes:

最后更新时间: 2019/03/21 - 09:08
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Finance: Monte Carlo 来自 Intel® C++ Compiler Code Samples

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

最后更新时间: 2019/03/21 - 09:08
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Classic Algorithms 来自 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.

最后更新时间: 2019/03/21 - 09:08
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Support 来自 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.

最后更新时间: 2019/03/21 - 09:08
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
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Sorting Algorithms: Merge Sort 来自 Intel® C++ Compiler Code Samples

Merge sort algorithm is a comparison-based sorting algorithm.

最后更新时间: 2019/03/21 - 09:08
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Graph Algorithms: Shortest Path 来自 Intel® C++ Compiler Code Samples

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

最后更新时间: 2019/03/21 - 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.
作者: David M. 最后更新时间: 2019/07/06 - 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.
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