Are the Intel Fortran run-time libraries thread safe?
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.
Improving software performance on parallel software requires a structured approach that makes good use of development resources, obtaining good results quickly.
SIGSEGV on Linux and SIGBUS on MacOS Root Causes
This document describes the cases for which the Intel MKL 10.2 and later 1D complex-to-complex FFTs are threaded.
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.
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.
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.
Many applications and algorithms contain serial optimizations that inadvertently introduce data dependencies and inhibit parallelism. One can often remove such dependences through simple transforms, or even avoid them altogether through.