We contribute a third-party survey of sparse matrix-vector (SpMV) product performance on industrial-strength, large matrices using: (1) The SpMV implementations in Intel MKL, the Trilinos project (Tpetra subpackage), the CUSPARSE library, and the CUSP library, each running on modern architectures. (2) NVIDIA GPUs and Intel multi-core CPUs (supported by each software package). (3) The CSR, BSR, COO, HYB, and ELL matrix formats (supported by each software package).
@article{arxiv.1608.00636,
title = {A survey of sparse matrix-vector multiplication performance on large matrices},
author = {Max Grossman and Christopher Thiele and Mauricio Araya-Polo and Florian Frank and Faruk O. Alpak and Vivek Sarkar},
journal= {arXiv preprint arXiv:1608.00636},
year = {2016}
}
Comments
Rice Oil & Gas High Performance Computing Workshop. March 2016