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题名:
crsd: application specific auto-tuning of spmv for diagonal sparse matrices
作者: Sun Xiangzheng ; Zhang Yunquan ; Wang Ting ; Long Guoping ; Zhang Xianyi ; Li Yan
会议文集: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
会议名称: 17th International Conference on Parallel Processing, Euro-Par 2011
会议日期: August 29,
出版日期: 2011
会议地点: Bordeaux, France
关键词: Distributed computer systems ; Optimization
出版地: Germany
ISSN: 3029743
ISBN: 9783642233968
部门归属: (1) Lab. of Parallel Software and Computational Science, Institute of Software, Graduate University of Chinese Academy of Sciences, China
英文摘要: Sparse Matrix-Vector multiplication (SpMV) is an important computational kernel in scientific applications. Its performance highly depends on the nonzero distribution of sparse matrices. In this paper, we propose a new storage format for diagonal sparse matrices, defined as Compressed Row Segment with Diagonal-pattern (CRSD). We design diagonal patterns to represent the diagonal distribution. As the diagonal distributions are similar within matrices from one application, some diagonal patterns remain unchanged. First, we sample one matrix to obtain the unchanged diagonal patterns. Next, the optimal SpMV codelets are generated automatically for those diagonal patterns. Finally, we combine the generated codelets as the optimal SpMV implementation. In addition, the information collected during auto-tuning process is also utilized for parallel implementation to achieve load-balance. Experimental results demonstrate that the speedup reaches up to 2.37 (1.70 on average) in comparison with DIA and 4.60 (2.10 on average) in comparison with CSR under the same number of threads on two mainstream multi-core platforms. © 2011 Springer-Verlag.
内容类型: 会议论文
URI标识: http://ir.iscas.ac.cn/handle/311060/14335
Appears in Collections:并行计算实验室 _会议论文

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Recommended Citation:
Sun Xiangzheng,Zhang Yunquan,Wang Ting,et al. crsd: application specific auto-tuning of spmv for diagonal sparse matrices[C]. 见:17th International Conference on Parallel Processing, Euro-Par 2011. Bordeaux, France. August 29,.
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