Automatic tuning of sparse matrix-vector multiplication on multicore clusters

doi:10.1007/s11432-014-5254-x

CORC > 计算技术研究所 > 中国科学院计算技术研究所 > 中国科学院计算技术研究所期刊论文 > 英文

	Automatic tuning of sparse matrix-vector multiplication on multicore clusters
	Li ShiGang 1; Hu ChangJun 3; Zhang JunChao 2; Zhang YunQuan 1
刊名	SCIENCE CHINA-INFORMATION SCIENCES
	2015-09-01
卷号	58 期号:9 页码:14
关键词	SpMV PGAS hybridization model-driven multicore clusters
ISSN号	1674-733X
DOI	10.1007/s11432-014-5254-x
英文摘要	To have good performance and scalability, parallel applications should be sophisticatedly optimized to exploit intra-node parallelism and reduce inter-node communication on multicore clusters. This paper investigates the automatic tuning of the sparse matrix-vector (SpMV) multiplication kernel implemented in a partitioned global address space language, which supports a hybrid thread-and process-based communication layer for multicore systems. One-sided communication is used for inter-node data exchange, while intra-node communication uses a mix of process shared memory and multithreading. We develop performance models to facilitate selecting the best configuration of threads and processes hybridization as well as the best communication pattern for SpMV. As a result, our tuned SpMV in the hybrid runtime environment consumes less memory and reduces inter-node communication volume, without damaging the data locality. Experiments are conducted on 12 real sparse matrices. On 16-node Xeon and 8-node Opteron clusters, our tuned SpMV kernel gets on average 1.4X and 1.5X improvement in performance over the well-optimized process-based message-passing implementation, respectively.
资助项目	State Key Program of National Natural Science of China[61432018] ; State Key Program of National Natural Science of China[61133005] ; National Natural Science Foundation of China[61272136] ; Foundation for Innovative Research Groups of the National Natural Science Foundation of China[61221062] ; National Basic Research Program of China[2013CB329606]
WOS研究方向	Computer Science
语种	英语
出版者	SCIENCE PRESS
WOS记录号	WOS:000359801900012
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/9412]
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Li ShiGang
作者单位	1.Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing 100190, Peoples R China 2.Univ Illinois, Dept Comp Sci, Urbana, IL 61801 USA 3.Univ Sci & Technol Beijing, Sch Comp & Commun Engn, Beijing 100083, Peoples R China
推荐引用方式 GB/T 7714	Li ShiGang,Hu ChangJun,Zhang JunChao,et al. Automatic tuning of sparse matrix-vector multiplication on multicore clusters[J]. SCIENCE CHINA-INFORMATION SCIENCES,2015,58(9):14.
APA	Li ShiGang,Hu ChangJun,Zhang JunChao,&Zhang YunQuan.(2015).Automatic tuning of sparse matrix-vector multiplication on multicore clusters.SCIENCE CHINA-INFORMATION SCIENCES,58(9),14.
MLA	Li ShiGang,et al."Automatic tuning of sparse matrix-vector multiplication on multicore clusters".SCIENCE CHINA-INFORMATION SCIENCES 58.9(2015):14.