Author(s)

Zhiqi Wang¹, Tongxiang Gu^2*

¹School of Mathematics and Statistics, Northeast Normal University, Changchun, China.
²Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics, Beijing, China.

The sparse matrix vector multiplication (SpMV) is inevitable in almost all kinds of scientific computation, such as iterative methods for solving linear systems and eigenvalue problems. With the emergence and development of Graphics Processing Units (GPUs), high efficient formats for SpMV should be constructed. The performance of SpMV is mainly determinted by the storage format for sparse matrix. Based on the idea of JAD format, this paper improved the ELLPACK-R format, reduced the waiting time between different threads in a warp, and the speed up achieved about 1.5 in our experimental results. Compared with other formats, such as CSR, ELL, BiELL and so on, our format performance of SpMV is optimal over 70 percent of the test matrix. We proposed a method based on parameters to analyze the performance impact on different formats. In addition, a formula was constructed to count the computation and the number of iterations.

SpMV, GPU, Storage Format, High Performance

Wang, Z. and Gu, T. (2020) PELLR: A Permutated ELLPACK-R Format for SpMV on GPUs. Journal of Computer and Communications, 8, 44-58. doi: 10.4236/jcc.2020.84004.