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Accelerating Sparse Matrix Vector Multiplication on Many-Core GPUs

Weizhi Xu, Zhiyong Liu, Dongrui Fan, Shuai Jiao, Xiaochun Ye, Fenglong Song, Chenggang Yan
Key Lab of Computer System and Architecture, Institute of Computing Technology, Chinese Academy of Sciences
World Academy of Science, Engineering and Technology, Issue 61, 2012

@article{xu2012accelerating,

   title={Accelerating Sparse Matrix Vector Multiplication on Many-Core GPUs},

   author={Xu, W. and Liu, Z. and Fan, D. and Jiao, S. and Ye, X. and Song, F. and Yan, C.},

   year={2012}

}

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Many-core GPUs provide high computing ability and substantial bandwidth; however, optimizing irregular applications like SpMV on GPUs becomes a difficult but meaningful task. In this paper, we propose a novel method to improve the performance of SpMV on GPUs. A new storage format called HYB-R is proposed to exploit GPU architecture more efficiently. The COO portion of the matrix is partitioned recursively into a ELL portion and a COO portion in the process of creating HYB-R format to ensure that there are as many non-zeros as possible in ELL format. The method of partitioning the matrix is an important problem for HYB-R kernel, so we also try to tune the parameters to partition the matrix for higher performance. Experimental results show that our method can get better performance than the fastest kernel (HYB) in NVIDIA’s SpMV library with as high as 17% speedup.
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