Communication-Computation Overlapping with Dynamic Loop Scheduling for Preconditioned Parallel Iterative Solvers on Multicore and Manycore Clusters

摘要

Preconditioned parallel solvers based on the Krylov iterative method are widely used in scientific and engineering applications. Communication overhead is a critical issue when executing these solvers on large-scale massively parallel supercomputers. In this work, we introduced communication-computation (CC) overlapping with dynamic loop scheduling of OpenMP to the sparse matrix-vector multiplication (SpMV) process of a parallel iterative solver. We then used the solver to evaluate the performance of a parallel finite element application (GeoFEM/Cube) on multicore and manycore clusters. The dynamic loop scheduling of OpenMP improved the efficiency of CC overlapping in halo exchanges, and the developed method attained a significant performance improvement of 40-50% for parallel iterative solvers in strong scaling using up to 16,384 cores of a Fujitsu PRIMEHPC FX10 supercomputer and an Intel Xeon Phi (KNL) cluster. Finally, the developed method was applied to GeoFEM/Cube using a parallel BiCGSTAB solver with sparse approximate inverse (SAI) preconditioning, and a 15-20% performance improvement was obtained on 12,288 cores of the Fujitsu FX10 and the KNL cluster.