Abstract
In this paper, we proposed a parallel algorithm to implement the sparse matrix transposition using ELLPACK-R format on the graphic processing units. By utilizing the tremendous memory bandwidth and the texture memory, the performance of this algorithm can be efficiently improved. Experimental results show that the performance of the proposed algorithm can be improved up to 8x times on Nvidia Tesla C2070, compared with the implementation on the Intel Xeon E5-2650 CPU. It also can be concluded that it is not wise to accelerate the transposition algorithm for the matrices in the ELLPACK-R format with violent divergence in the number of nonzero elements among the rows.
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Acknowledgments
This work was supported by the National Science Foundation of China under Grants 61402499 and 61202127, and the National High Technology Research and Development Program of China under Grants 2012AA012706.
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Guo, S., Dou, Y., Lei, Y., Wang, Q., Xia, F., Chen, J. (2016). Designing Parallel Sparse Matrix Transposition Algorithm Using ELLPACK-R for GPUs. In: Xu, W., Xiao, L., Li, J., Zhang, C. (eds) Computer Engineering and Technology. NCCET 2015. Communications in Computer and Information Science, vol 592. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49283-3_7
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DOI: https://doi.org/10.1007/978-3-662-49283-3_7
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