SparseX: A Library for High-Performance Sparse Matrix-Vector Multiplication on Multicore Platforms

• Published in: ACM transactions on mathematical software 2018-09, Vol.44 (3), p.1-32
• Main Authors: Elafrou, Athena, Karakasis, Vasileios, Gkountouvas, Theodoros, Kourtis, Kornilios, Goumas, Georgios, Koziris, Nectarios
• Format: Article
• Language: English
• ISSN: 0098-3500; 1557-7295
• DOI: 10.1145/3134442

The Sparse Matrix-Vector Multiplication (SpMV) kernel ranks among the most important and thoroughly studied linear algebra operations, as it lies at the heart of many iterative methods for the solution of sparse linear systems, and often constitutes a severe performance bottleneck. Its optimization, which is intimately associated with the data structures used to store the sparse matrix, has always been of particular interest to the applied mathematics and computer science communities and has attracted further attention since the advent of multicore architectures. In this article, we present SparseX, an open source software package for SpMV targeting multicore platforms, that employs the state-of-the-art Compressed Sparse eXtended (CSX) sparse matrix storage format to deliver high efficiency through a highly usable “BLAS-like” interface that requires limited or no tuning. Performance results indicate that our library achieves superior performance over competitive libraries on large-scale problems.