Certain situations can cause conflicts in the execution environment that make the use of threads in

Intel® oneAPI Math Kernel Library

problematic. This section briefly discusses why these problems exist and how to avoid them.

If your program is parallelized by other means than Intel® OpenMP* run-time library (RTL) and Intel TBB RTL, several calls to

Intel® oneAPI Math Kernel Library

may operate in a multithreaded mode at the same time and result in slow performance due to overuse of machine resources.

The following table considers several cases where the conflicts may arise and provides recommendations depending on your threading model:

Using the

mkl_set_num_threads

and

mkl_domain_set_num_threads

functions to control parallelism of

Intel® oneAPI Math Kernel Library

from parallel user threads may result in a race condition that impacts the performance of the application because these functions operate on internal control variables that are global, that is, apply to all threads. For example, if parallel user threads call these functions to set different numbers of threads for the same function domain, the number of threads actually set is unpredictable. To avoid this kind of data races, use the

mkl_set_num_threads_local

function (see the "Support Functions"

section

in the

Intel® oneAPI Math Kernel Library

Developer Reference

for the function description).