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	Hybrid-optimization strategy for the communication of large-scale kinetic monte carlo simulation
	Wu, Baodong1,2; Li, Shigang1; Zhang, Yunquan1; Nie, Ningming3
刊名	Computer physics communications
	2017-02-01
卷号	211页码:113-123
关键词	Kinetic monte carlo Communication aggregation Shared memory Neighborhood collectives
ISSN号	0010-4655
DOI	10.1016/j.cpc.2016.07.008
通讯作者	Li, shigang(shigangli.cs@gmail.com)
英文摘要	The parallel kinetic monte carlo (kmc) algorithm based on domain decomposition has been widely used in large-scale physical simulations. however, the communication overhead of the parallel kmc algorithm is critical, and severely degrades the overall performance and scalability. in this paper, we present a hybrid optimization strategy to reduce the communication overhead for the parallel kmc simulations. we first propose a communication aggregation algorithm to reduce the total number of messages and eliminate the communication redundancy. then, we utilize the shared memory to reduce the memory copy overhead of the intra-node communication. finally, we optimize the communication scheduling using the neighborhood collective operations. we demonstrate the scalability and high performance of our hybrid optimization strategy by both theoretical and experimental analysis. results show that the optimized kmc algorithm exhibits better performance and scalability than the well-known open-source library-spparks. on 32-node xeon e5-2680 cluster (total 640 cores), the optimized algorithm reduces the communication time by 24.8% compared with spparks. (c) 2016 elsevier b.v. all rights reserved.
WOS关键词	GROWTH
WOS研究方向	Computer Science ; Physics
WOS类目	Computer Science, Interdisciplinary Applications ; Physics, Mathematical
语种	英语
出版者	ELSEVIER SCIENCE BV
WOS记录号	WOS:000390181300015
内容类型	期刊论文
URI标识	http://www.corc.org.cn/handle/1471x/2374191
专题	计算机网络信息中心
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 Chinese Acad Sci, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Comp Network Informat Ctr, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Wu, Baodong,Li, Shigang,Zhang, Yunquan,et al. Hybrid-optimization strategy for the communication of large-scale kinetic monte carlo simulation[J]. Computer physics communications,2017,211:113-123.
APA	Wu, Baodong,Li, Shigang,Zhang, Yunquan,&Nie, Ningming.(2017).Hybrid-optimization strategy for the communication of large-scale kinetic monte carlo simulation.Computer physics communications,211,113-123.
MLA	Wu, Baodong,et al."Hybrid-optimization strategy for the communication of large-scale kinetic monte carlo simulation".Computer physics communications 211(2017):113-123.

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