With automatic parallelization, the compiler detects loops that can be safely and efficiently executed in parallel and generates multithreaded code.
How to configure OpenMP in the Intel IPP library to maximize multi-threaded performance of the Intel IPP primitives.
internal error: 0_1204 when openmp used with exception handling
The Intel C++ and Fortran compilers for Windows* and Linux* provide 'legacy' and 'compatibility' implementations of the OpenMP THREADPRIVATE directive. The 'compatibility' option should not be used when everything is compiled by Intel compilers.
The newest releases of the Intel® C++ and Fortran Compilers support new features in the OpenMP* 3.1 Specification
Intel MKL in Microsoft Visual Studio
The compiler does not parallelize OpenMP loops that contain a "continue" statement in a C++ catch block inside the parallel region.
Sparse BLAS routines can be useful to implement iterative methods for solving large sparse systems of equations or eigenvalue problems
This article discussions parallelization and provides links that will help you understand your programming environment and evaluate the suitability of your app.
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.