Optimization of serial and parallel communications for parallel geometric multigrid method

Abstract

The parallel multigrid method is expected to play an important role in large-scale scientific computing on post-peta/exa-scale supercomputer systems, and it also includes serial and parallel communication processes which are generally expensive. In the present work, new format for sparse matrix storage based on sliced Ellpack-Itpack (ELL) format is proposed for optimization of serial communication in data transfer through memories, and hierarchical coarse grid aggregation (hCGA) is introduced for optimization of parallel communication by message passing. The proposed methods are implemented for pGW3D-FVM, a parallel code for 3D groundwater flow simulations using the multigrid method, and the robustness and performance of the code was evaluated on up to 4,096 nodes (65,536 cores) of the Fujistu FX10 supercomputer system at the University of Tokyo. The parallel multigrid solver using the sliced ELL format provided performance improvement in both weak scaling (25%–31%) and strong scaling (9%–22%) compared to the code using the original ELL format. Moreover, hCGA provided excellent performance improvement in both weak scaling (1.61 times) and strong scaling (6.27 times) for flat MPI parallel programming model.