In diesem Artikel wird der inkrementelle OpenMP Ansatz zur Parallelisierung von sequentiellen Programmen vorgestellt. Der Schwerpunkt liegt auf der praktischen Darstellung von einfachen Programmbeispielen und nicht auf der Vollständigkeit der Beschreibung
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.
SIGSEGV on Linux and SIGBUS on MacOS Root Causes
Advice and background information is given on typical issues that may arise when threading an application using the Intel Fortran Compiler and other software tools, whether using OpenMP, automatic parallelization or threaded libraries.
Download this guide for developing multithreaded applications, which also includes general topics such as application threading and synchronization.
A toolkit that gives 6 Steps to Increase Performance Through Vectorization in Your Application
The Intel® Data Analytics Acceleration Library (Intel® DAAL) helps speed big data analytics by providing highly optimized algorithmic building blocks for all data analysis stages (Pre-processing, Transformation, Analysis, Modeling, Validation, and Decision Making) for offline, streaming and distributed analytics usages. It’s designed for use with popular data platforms including Hadoop*, Spark*,...
Parallelize loops with Intel® Threading Building Blocks using Intel® C++ Compiler for lambda expressions.
Intel® Parallel Studio XE is a very popular product from Intel that includes the Intel® Compilers, Intel® Performance Libraries, tools for analysis, debugging and tuning, tools for MPI and the Intel® MPI Library. Did you know that some of these are available for free? Here is a guide to “what is available free” from the Intel Parallel Studio XE suites.