Implementing a Sparse Matrix Vector Product for the SELL-C/SELL-C-σ formats on NVIDIA GPUs

TitleImplementing a Sparse Matrix Vector Product for the SELL-C/SELL-C-σ formats on NVIDIA GPUs
Publication TypeTech Report
Year of Publication2014
AuthorsAnzt, H., S. Tomov, and J. Dongarra
Technical Report Series TitleUniversity of Tennessee Computer Science Technical Report
NumberUT-EECS-14-727
Date Published04-2014
InstitutionUniversity of Tennessee
AbstractNumerical methods in sparse linear algebra typically rely on a fast and efficient matrix vector product, as this usually is the backbone of iterative algorithms for solving eigenvalue problems or linear systems. Against the background of a large diversity in the characteristics of high performance computer architectures, it is a challenge to derive a cross-platform efficient storage format along with fast matrix vector kernels. Recently, attention focused on the SELL-C- format, a sliced ELLPACK format enhanced by row-sorting to reduce the fill in when padding rows with zeros. In this paper we propose an additional modification resulting in the padded sliced ELLPACK (SELLP) format, for which we develop a sparse matrix vector CUDA kernel that is able to efficiently exploit the computing power of NVIDIA GPUs. We show that the kernel we developed outperforms straight-forward implementations for the widespread CSR and ELLPACK formats, and is highly competitive to the implementations in the highly optimized CUSPARSE library.
Project Tags: