Article
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
File Wrapper

Parallel Universe Magazine - Issue 20, February 2015

Authored by admin Last updated on 12/12/2018 - 18:08
Article

Efficient Parallelization

This article is part of the Intel® Modern Code Developer Community documentation which supports developers in leveraging application performance in code through a systematic step-by-step optimization framework methodology. This article addresses: Thread level parallelization.
Authored by Ronald W Green (Blackbelt) Last updated on 09/30/2019 - 17:28
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

Vectorization Essentials

Vectorization essentials to effectively use feature in the Intel® Xeon product family
Authored by admin Last updated on 10/02/2019 - 15:11
Article

Parallelization Using Intel® Threading Building Blocks (Intel® TBB)

Compiler Methodology for Intel® MIC Architecture

Authored by admin Last updated on 10/15/2019 - 21:13
Article

Explicit Vector Programming in Fortran

No longer does Moore’s Law result in higher frequencies and improved scalar application performance; instead, higher transistor counts lead to increased parallelism, both through more cores and thr

Authored by Martyn Corden (Intel) Last updated on 10/15/2019 - 15:30
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

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