Benchmarking sparse matrix-Vector multiply in five minutes

Abstract

We present a benchmark for evaluating the performance of Sparse matrix-dense vector multiply (abbreviated as SpMV) on scalar uniprocessor machines. Though SpMV is an important kernel in scientific computation, there are currently no adequate benchmarks for measuring its performance across many platforms. Our work serves as a reliable predictor of expected SpMV performance across many platforms, and takes no more than five minutes to obtain its results.