Performance of a Structure-Detecting SpMV Using the CSR Matrix Representation

Abstract

Sparse matrix-vector multiplication (SpMV) is an important building block for many scientific applications. Various formats exist to store and represent sparse matrices in the computer's memory. The compressed row storage format (CRS or CSR) is typically a baseline to report a new hybrid or an improved representation of sparse matrices. In this paper, we describe the implementation and performance benefit of a structure-detecting SpMV algorithm using the CSR format. Our implementation detects contiguous rows in the sparse matrix representation to improve the performance of the computation by making better use of the available memory bandwidth. Applications with mixed or a-priori unknown matrix structures can take advantage of the runtime structure detection. We show that the additional control flow needed does not degrade performance, but may deliver up to twice the performance of the traditional SpMV algorithm.