Developer Reference for Intel® oneAPI Math Kernel Library for C

ID 766684
Date 3/22/2024
Public

A newer version of this document is available. Customers should click here to go to the newest version.

Document Table of Contents

Parallelism in Extended Eigensolver Routines

How you achieve parallelism in Extended Eigensolver routines depends on which interface you use. Parallelism (via shared memory programming) is not explicitly implemented in Extended Eigensolver routines within one node: the inner linear systems are currently solved one after another.

  • Using the Extended Eigensolver RCI interfaces, you can achieve parallelism by providing a threaded inner system solver and a matrix-matrix multiplication routine. When using the RCI interfaces, you are responsible for activating the threaded capabilities of your BLAS and LAPACK libraries most likely using the shell variable OMP_NUM_THREADS.

  • Using the predefined Extended Eigensolver interfaces, parallelism can be implicitly obtained within the shared memory version of BLAS, LAPACK or Intel® oneAPI Math Kernel Library (oneMKL) PARDISO. The shell variableMKL_NUM_THREADScan be used for automatically setting the number of OpenMP threads (cores) for BLAS, LAPACK, and Intel® oneAPI Math Kernel Library (oneMKL) PARDISO.

Product and Performance Information

Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.

Notice revision #20201201