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Intel® Threading Building Blocks, OpenMP*, or native threads?

Which API do you choose to introduce threading to your software application, if you have a choice? Is there one answer that always works?

Authored by Michael V. (Intel) Last updated on 07/05/2019 - 09:20
Article

Intel® Fortran Compiler for Linux* - Are the libraries thread safe?

Are the Intel Fortran run-time libraries thread safe?
Authored by admin Last updated on 07/04/2019 - 10:00
Article

OpenMP und inkrementelle Parallelisierung - (article in german)

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
Authored by admin Last updated on 12/12/2018 - 18:00
Article

Requirements for Vectorizable Loops

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.
Authored by Martyn Corden (Intel) Last updated on 03/27/2019 - 14:36
Article

Determining Root Cause of Segmentation Faults SIGSEGV or SIGBUS errors

SIGSEGV on Linux and SIGBUS on MacOS Root Causes
Authored by admin Last updated on 12/26/2018 - 14:09
Article

Intel® MKL Threaded 1D FFTs

This document describes the cases for which the Intel MKL 10.2 and later 1D complex-to-complex FFTs are threaded.
Authored by Last updated on 03/27/2019 - 10:00
Article

Threading Fortran Applications for Parallel Performance on Multi-Core Systems

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.
Authored by Martyn Corden (Intel) Last updated on 12/12/2018 - 18:00
Article

Automatic Parallelization with Intel® Compilers

With automatic parallelization, the compiler detects loops that can be safely and efficiently executed in parallel and generates multithreaded code.
Authored by admin Last updated on 07/04/2019 - 21:33
Article

Loop Modifications to Enhance Data-Parallel Performance

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.
Authored by admin Last updated on 07/05/2019 - 14:47
Article

Granularity and Parallel Performance

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.
Authored by admin Last updated on 07/05/2019 - 19:52