BeBOP SpMV Benchmark
Sparse matrix-vector mulitply (SpMV) is a common operation in scientific
codes. It finds applications in iterative methods to solve sparse linear
systems and information retreival, among other places. Thus knowing how
well a particular machine performs SpMV is useful for both vendors and
consumers alike. Our benchmark provides an effective way of evaluating a
particular platform's ability to perform SpMV, and it can do so in only a
We currently have available for
download a benchmark for uniprocessor SpMV. We hope to have a
distributed-memory parallel version available in the future.
H. Gahvari, M. Hoemmen, J. Demmel, K. Yelick. Benchmarking Sparse
Matrix-Vector Multiply in Five Minutes. SPEC Benchmark Workshop 2007,
Austin, TX, January 21, 2007.
H. Gahvari. Benchmarking Sparse
Matrix-Vector Multiply. Master's Thesis, University of California,
Berkeley, December 2006.
We maintain our lists of benchmark results here, and we encourage you to submit your own
results to us. Please place platform information and benchmark numbers
into the form below, and click on the "Submit" button. The results will be
sent for us to look at and then post in our results section. Please fill
in every entry.