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

Are the Intel Fortran run-time libraries thread safe?
Autor admin Última actualización 04/07/2019 - 10:00
Mensajes en el blog

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

It finally happened!

Autor Dmitry Vyukov Última actualización 15/02/2019 - 13:39
Mensajes en el blog

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.   

Autor Última actualización 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.
Autor Martyn Corden (Intel) Última actualización 27/03/2019 - 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.

Autor aaron-tersteeg (Intel) Última actualización 05/07/2019 - 10:15
Article

Determining Root Cause of Segmentation Faults SIGSEGV or SIGBUS errors

SIGSEGV on Linux and SIGBUS on MacOS Root Causes
Autor admin Última actualización 26/12/2018 - 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.
Autor Última actualización 27/03/2019 - 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.
Autor Martyn Corden (Intel) Última actualización 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.
Autor admin Última actualización 05/07/2019 - 14:47