Parallelism

Intel® oneAPI Math Kernel Library offers performance gains through parallelism provided by the symmetric multiprocessing performance (SMP) feature. You can obtain improvements from SMP in the following ways:

• One way is based on user-managed threads in the program and further distribution of the operations over the threads based on data decomposition, domain decomposition, control decomposition, or some other parallelizing technique. Each thread can use any of the Intel® oneAPI Math Kernel Library functions (except for the deprecated?lacon LAPACK routine) because the library has been designed to be thread-safe.

• Another method is to use the FFT and BLAS level 3 routines. They have been parallelized and require no alterations of your application to gain the performance enhancements of multiprocessing. Performance using multiple processors on the level 3 BLAS shows excellent scaling. Since the threads are called and managed within the library, the application does not need to be recompiled thread-safe.

• Yet another method is to use tuned LAPACK routines. Currently these include the single- and double precision flavors of routines for QR factorization of general matrices, triangular factorization of general and symmetric positive-definite matrices, solving systems of equations with such matrices, as well as solving symmetric eigenvalue problems.

For instructions on setting the number of available processors for the BLAS level 3 and LAPACK routines, see Intel® oneAPI Math Kernel Library Developer Guide.