我们将讨论 OpenMP for 循环中的并行规约。
When confronted with nested loops, the granularity of the computations that are assigned to threads will directly affect performance. Loop transformations such as splitting and merging nested loops can make parallelization easier and more productive.
英特尔® 数据分析加速库（英特尔® DAAL）是一种高性能库，它提供了丰富的算法集，从面向数据集的最基本的描述统计，到更高级的数据挖掘和机器学习算法。它可以帮助开发人员轻松地开发高度优化的大数据算法。
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.
An Intro to Multi-Level Parallelism for High-Performance Computing by Clay Breshears | Life Sciences Software Architect, Intel
Caffe* is a deep learning framework developed by the Berkeley Vision and Learning Center (BVLC). Caffe optimized for Intel architecture is currently integrated with the latest release of Intel® Math Kernel Library (Intel® MKL) 2017 optimized for Advanced Vector Extensions (AVX)-2 and AVX-512 instructions which are supported in Intel® Xeon® and Intel® Xeon Phi™ processors (among others). This...
Checksums are widely used for checking the integrity of data in applications such as storage and networking. We present fast methods of computing checksums on Intel® processors. Instead of computing the checksum of the input with a traditional linear method, we describe a faster method to split the data into a number of interleaved parallel streams, compute the checksum on these segments in...
The computer learning code Caffe* has been optimized for Intel® Xeon Phi™ processors. This article provides detailed instructions on how to compile and run this Caffe* optimized for Intel® architecture to obtain the best performance on Intel Xeon Phi processors.
Many applications and algorithms contain serial optimizations that inadvertently introduce data dependencies and inhibit parallelism. One can often remove such dependences through simple transforms, or even avoid them altogether through.