Balanced CSR sparse matrix-vector product on graphics processors

Abstract

We propose a novel parallel approach to compute the sparse matrix-vector product (SpMV) on graphics processing units (GPUs), optimized for matrices with an irregular row distribution of the non-zero entries. Our algorithm relies on the standard CSR format to store the sparse matrix, requires an inexpensive pre-processing step, and consumes only a minor amount of additional memory compared with significantly more expensive GPU-specific sparse matrix layouts. In addition, we propose a simple heuristic to determine whether our method or the standard CSR SpMV algorithm should be used for a specific matrix. As a result, our proposal, combined with the standard CSR SpMV, can be adopted as the default choice for the implementation of SpMV in general-purpose sparse linear algebra libraries for GPUs.