文献类型：期刊文献

英文题名：Optimizing Sparse Matrix-Vector Multiplication on GPUS via Index Compression

作者：Sun, Xue[1,2]; Wei, Kai-Cheng[3]; Lai, Lien-Fu[3]; Tsai, Sung-Han[3]; Wu, Chao-Chin[3]

第一作者：孙雪;Sun, Xue

通讯作者：Wu, Chao-Chin

机构：[1] College of Urban Rail Transit and Logistics, Beijing Union University, Beijing, China; [2] Department of Electrical Engineering, National Changhua University of Education, Changhua, Taiwan; [3] Department of Computer Science and Information Engineering, National Changhua University of Education, Changhua, Taiwan

第一机构：北京联合大学城市轨道交通与物流学院

年份：2018

起止页码：598-602

外文期刊名：Proceedings of 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2018

收录：EI(收录号：20190406426497)

语种：英文

外文关键词：Program processors - Matrix algebra

摘要：Sparse matrix-vector multiplication (SpMV) as one of the most significant scientific kernels has been widely used in many scientific disciplines. In practical applications, large-scale spare matrices are usually used for calculation. During these years, Graphic Processing Unit (GPU) has become a powerful platform for high-performance computing, and optimizing SpMV on GPU based systems for efficient performance is the principal interest in many researches. In this paper, we proposed a new method to optimize SpMV on GPUs via index compression. Our index compression method can reduce the index value of the access space. The memory space for recording each column index is significantly reduced from two bytes to one byte, which outperforms the previous work on access performance. The main contributions we make are as follows: (1) Only one byte for each column index is required, which can significantly reduce the working set of the column index and further improve the cache hit ration. (2) Our method can be applied to any kind of matrices, while the previous work can only apply to subset of the matrices. Computational experiments on problems according to the previous work reveal that the best performance improvement ration for ours is up to about 1.5. ? 2018 IEEE.