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Mathematical Problems in Engineering
Volume 2016, Article ID 8471283, 12 pages
http://dx.doi.org/10.1155/2016/8471283
Research Article

A Novel CSR-Based Sparse Matrix-Vector Multiplication on GPUs

1Zhijiang College, Zhejiang University of Technology, Hangzhou 310024, China
2College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China

Received 4 January 2016; Accepted 27 March 2016

Academic Editor: Veljko Milutinovic

Copyright © 2016 Guixia He and Jiaquan Gao. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Sparse matrix-vector multiplication (SpMV) is an important operation in scientific computations. Compressed sparse row (CSR) is the most frequently used format to store sparse matrices. However, CSR-based SpMVs on graphic processing units (GPUs), for example, CSR-scalar and CSR-vector, usually have poor performance due to irregular memory access patterns. This motivates us to propose a perfect CSR-based SpMV on the GPU that is called PCSR. PCSR involves two kernels and accesses CSR arrays in a fully coalesced manner by introducing a middle array, which greatly alleviates the deficiencies of CSR-scalar (rare coalescing) and CSR-vector (partial coalescing). Test results on a single C2050 GPU show that PCSR fully outperforms CSR-scalar, CSR-vector, and CSRMV and HYBMV in the vendor-tuned CUSPARSE library and is comparable with a most recently proposed CSR-based algorithm, CSR-Adaptive. Furthermore, we extend PCSR on a single GPU to multiple GPUs. Experimental results on four C2050 GPUs show that no matter whether the communication between GPUs is considered or not PCSR on multiple GPUs achieves good performance and has high parallel efficiency.