Performance evaluation of sparse matrix-vector product (SpMV) computation on GPU architecture

TitrePerformance evaluation of sparse matrix-vector product (SpMV) computation on GPU architecture
Publication TypeConference Paper
Year of Publication2014
AuthorsKasmi, N, Mahmoudi, SAhmed, Zbakh, M, Manneback, P
Conference Name2014 SECOND WORLD CONFERENCE ON COMPLEX SYSTEMS (WCCS)
PublisherIbn Zohr Univ; Moroccan Soc of Complex Syst; IEEE Morocco; Int Acad for Syst and Cybernet Sci IASCYS
ISBN Number978-1-4799-4647-1
Abstract

Sparse matrices are entailed in many linear algebra problems such as linear systems resolution, matrix eigenvalues/vectors computation and partial differential equations, wherefore sparse matrix vector product (SpMV) constitutes a basic kernel for solving many scientific and engineering applications problems. With the appearance of Graphics Processing Units (GPUs) as platforms that provides important acceleration factors, the optimization of SpMV on GPUs and its implementation has been a subject of broad research for the last few years. In this work we present a comparative evaluation of sparse matrix vector product (SpMV) on different platforms. We use Cusp library on CUDA architecture GPUs and MKL Intel library as reference on CPUs. Experimental results have been conducted using a set of matrices from matrix market repositoryi, comparing performance between GPU-based Cusp(2)and CPU-based MKL(3)libraries. The results showed a global speedup, obtained with GPU, ranging from 1.1 x to 4.6 x compared to CPU implementations. An analysis and evaluation of these results is discussed.

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