Analysis of Partitioning Models and Metrics in Parallel Sparse Matrix-Vector Multiplication

International audienceGraph/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 com- puting 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 perfor- mance greatly in a distributed memory setting. The regression analyses demonstrate which metric is the most influential for the execution time of the libraries