OpenMP* and the Intel® IPP Library

How to configure OpenMP in the Intel IPP library to maximize multi-threaded performance of the Intel IPP primitives.
作者: 最后更新时间: 2019/07/31 - 14:30

Intel® IPP Functions Optimized for Intel® Advanced Vector Extensions 2 (Intel® AVX2)

List of Intel IPP functions optimized for processor code name Haswell and Skylake
作者: Shaojuan Z. (Intel) 最后更新时间: 2019/07/31 - 14:30

Fast Gathering-based SpMxV for Linear Feature Extraction

This algorithm can be used to improve sparse matrix-vector and matrix-matrix multiplication in any numerical computation. As we know, there are lots of applications involving semi-sparse matrix computation in High Performance Computing. Additionally, in popular perceptual computing low-level engines, especially speech and facial recognition, semi-sparse matrices are found to be very common....
作者: 最后更新时间: 2018/12/12 - 18:00

Intel® System Studio Matrix Multiplication Sample

This is a "matrix multiplication" example that illustrates different features of Intel® System Studio on Microsoft* Visual Studio* IDE, Eclipse* IDE and on Yocto* Target System By Downloading or copying all or any part of the sample source code, you agree to the terms of the Intel® Sample Source Code License Agreement
作者: 最后更新时间: 2019/06/23 - 18:50

Benefits of Intel® Optimized Caffe* in comparison with BVLC Caffe*

作者: JON J K. (Intel) 最后更新时间: 2018/05/30 - 07:00
File Wrapper

Parallel Universe Magazine - Issue 24, March 2016

作者: 管理 最后更新时间: 2018/12/12 - 18:08

Intel® Math Kernel Library Improved Small Matrix Performance Using Just-in-Time (JIT) Code Generation for Matrix Multiplication (GEMM)

    The most commonly used and performance-critical Intel® Math Kernel Library (Intel® MKL) functions are the general matrix multiply (GEMM) functions.

作者: Gennady F. (Blackbelt) 最后更新时间: 2019/03/21 - 03:01

Intel® IPP - Threading / OpenMP* FAQ

This page contains common questions and answers on multi-threading in the Intel IPP.
作者: 最后更新时间: 2019/10/10 - 10:48

Recognize and Measure Vectorization Performance

Get a background on vectorization and learn different techniques to evaluate its effectiveness.
作者: David M. 最后更新时间: 2019/10/15 - 15:30

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: 2019/10/15 - 16:40