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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
Blog post

1024cores: All about lock-free, concurrency, multicore and parallelism

It finally happened!

Authored by Dmitry Vyukov Last updated on 02/15/2019 - 13:39
Blog post

Visual Studio 2010 Built-in CPU Acceleration

Writing the sample code for this post I was amazed myself to see how simple it was to reach over 20 times performance improvement with so little effort.   

Authored by 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

The Three Stages of Preparation for Optimizing Parallel Software

Improving software performance on parallel software requires a structured approach that makes good use of development resources, obtaining good results quickly.

Authored by aaron-tersteeg (Intel) Last updated on 07/05/2019 - 10:15
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

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