Analysis of partitioning models and metrics in parallel sparse matrix-vector multiplication

Abstract

Graph/hypergraph partitioning models and methods have been successfully used to minimize the communication among processors in several parallel computing applications. Parallel sparse matrix-vector multiplication (SpMxV) is one of the representative applications that renders these models and methods indispensable in many scientific computing contexts. We investigate the interplay of the partitioning metrics and execution times of SpMxV implementations in three libraries: Trilinos, PETSc, and an in-house one. We carry out experiments with up to 512 processors and investigate the results with regression analysis. Our experiments show that the partitioning metrics influence the performance greatly in a distributed memory setting. The regression analyses demonstrate which metric is the most influential for the execution time of the libraries. © 2014 Springer-Verlag.