Performance Evaluation of Sparse Storage Formats

Sparse matrices are pervasive in many Computational Science and Engineering (CS&E) applications. There is a significant number of storage formats used to represent sparse matrices. This paper presents a performance evaluation of storage formats for the main kernel of iterative methods for numerical linear algebra, namely matrix-vector multiplication. The experiments consider a set of almost 200 sparse matrices from the Matrix Market collection covering both systems of linear equations and eigenvalue problems. For each matrix, the experiments perform the matrix-vector multiplication with most commonly used sparse storage formats and also the recently proposed Java Sparse Array storage fonmat. To the best of the authors´ knowledge, there is no other performance evaluation of storage formats for sparse matrices which consider such a variety of matrices and storage formats.