Performance evaluation of the sparse matrix-vector multiplication on modern architectures

Abstract

In this paper, we revisit the performance issues of the widely used sparse matrix-vector multiplication (SpMxV) kernel on modern microarchitectures. Previous scientific work reports a number of different factors that may significantly reduce performance. However, the interaction of these factors with the underlying architectural characteristics is not clearly understood, a fact that may lead to misguided, and thus unsuccessful attempts for optimization. In order to gain an insight into the details of SpMxV performance, we conduct a suite of experiments on a rich set of matrices for three different commodity hardware platforms. In addition, we investigate the parallel version of the kernel and report on the corresponding performance results and their relation to each architecture's specific multithreaded configuration. Based on our experiments, we extract useful conclusions that can serve as guidelines for the optimization process of both single and multithreaded versions of the kernel.