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	Optimizing and auto-tuning scale-free sparse matrix-vector multiplication on Intel Xeon Phi
	Tang, Wai Teng ; Zhao, Ruizhe ; Lu, Mian ; Liang, Yun ; Huyng, Huynh Phung ; Li, Xibai ; Goh, Rick Siow Mong
	2015
英文摘要	Recently, the Intel Xeon Phi coprocessor has received increasing attention in high performance computing due to its simple programming model and highly parallel architecture. In this paper, we implement sparse matrix vector multiplication (SpMV) for scale-free matrices on the Xeon Phi architecture and optimize its performance. Scale-free sparse matrices are widely used in various application domains, such as in the study of social networks, gene networks and web graphs. We propose a novel SpMV format called vectorized hybrid COO+CSR (VHCC). Our SpMV implementation employs 2D jagged partitioning, tiling and vectorized prefix sum computations to improve hardware resource utilization, and thus overall performance. As the achieved performance depends on the number of vertical panels, we also develop a performance tuning method to guide its selection. Experimental results demonstrate that our SpMV implementation achieves an average 3?? speedup over Intel MKL for a wide range of scale-free matrices. ? 2015 IEEE.; EI; 0
语种	英语
DOI标识	10.1109/CGO.2015.7054194
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/412438]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Tang, Wai Teng,Zhao, Ruizhe,Lu, Mian,et al. Optimizing and auto-tuning scale-free sparse matrix-vector multiplication on Intel Xeon Phi. 2015-01-01.

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