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Q: How to get Intel® Integrated Performance Primitives (Intel® IPP) Static threaded libraries?
The article describes effect of /Qpar-threshold option when doing auto parallelization with Intel C++ compiler.
This document describes how developers can use Intel® Parallel Composer to optimize applications for IA-32 and Intel® 64 processors.
Vectorization is one of many optimizations that are enabled by default in the latest Intel compilers. In order to be vectorized, loops must obey certain conditions, listed below. Some additional ways to help the compiler to vectorize loops are described.
MSC.Software SimXpert* is a fully integrated simulation environment for performing multidiscipline based analysis with a graphical interface designed to facilitate the end-to-end simulations. This article describes the threading of SimXpert.
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.
One key to attaining good parallel performance is choosing the right granularity for the application. Granularity is the amount of real work in the parallel task. If granularity is too fine, then performance can suffer from communication overhead.
How to configure OpenMP in the Intel IPP library to maximize multi-threaded performance of the Intel IPP primitives.
Guided Auto-Parallel - compiler feature providing guidance to user on what changes are necessary for the compiler to automatically add vectorization or parallelization to serial application.