Performance Optimizations and Bounds for Sparse Matrix-Vector Multiply
Richard W. Vuduc, James Weldon Demmel, Katherine Yelick, Shoaib Kamil, Rajesh Nishtala, Benjamin Lee · 2002
We consider performance tuning, by code and data structure reorganization, of sparse matrix-vector multiply (SpMV), one of the most important computational kernels in scientific applications. This paper addresses the fundamental questions of what limits exist on such performance tuning, and how closely tuned code approaches these limits. Specifically,