Auto-tuned nested parallelism: A way to reduce the execution time of scientific software in NUMA systems
An auto-tuning method is proposed for scientific software in NUMA multiprocessors. The method selects the optimum number of threads to use. The software combines multithreaded routines with OpenMP. The method is based on modeling the execution time of the routines. The methodology is applied to different matrix factorizations (LU, QR and Cholesky).
auto-tuning numa linear algebra performance modeling