How to configure OpenMP in the Intel IPP library to maximize multi-threaded performance of the Intel IPP primitives.
List of Intel IPP functions optimized for processor code name Haswell and Skylake
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....
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
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.
This page contains common questions and answers on multi-threading in the Intel IPP.
Get a background on vectorization and learn different techniques to evaluate its effectiveness.
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.