Implementation and Evaluation of Parallel Sparse Matrix-Vector Products on Distributed Memory Parallel Computers

The sparse matrix vector product (SMVP) is the kernel operation in many scientific applications. This kernel is an irregular problem, which has led to the development of several compressed storage formats. This paper discusses scalable implementations of sparse matrix-vector products, which are crucial for high performance solutions of large-scale linear equations, on distributed memory parallel computers using message passing. Five storage formats for sparse matrices are evaluated. We conduct numerical experiments on several different sparse matrices and show the parallel performance of our sparse matrix-vector product routines