Hybrid-optimization strategy for the communication of large-scale Kinetic Monte Carlo simulation

doi:10.1016/j.cpc.2016.07.008

	Hybrid-optimization strategy for the communication of large-scale Kinetic Monte Carlo simulation
	Wu, Baodong 1,2; Li, Shigang 1; Zhang, Yunquan 1; Nie, Ningming 3
	2017-02-01
发表期刊	COMPUTER PHYSICS COMMUNICATIONS
ISSN	0010-4655
卷号	211 页码:113-123
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.
关键词	Kinetic Monte Carlo Communication aggregation Shared memory Neighborhood collectives
DOI	10.1016/j.cpc.2016.07.008
收录类别	SCI
语种	英语
资助项目	National High Technology Research and Development Program of China[2015AA01A303] ; National Natural Science of China[61502450] ; State Key Program of National Natural Science of China[61432018] ; State Key Program of National Natural Science of China[61133005] ; Foundation for Innovative Research Groups of the National Natural Science Foundation of China[61521092] ; National Natural Science Foundation of China[61272136]
WOS研究方向	Computer Science ; Physics
WOS类目	Computer Science, Interdisciplinary Applications ; Physics, Mathematical
WOS记录号	WOS:000390181300015
出版者	ELSEVIER SCIENCE BV
引用统计	被引频次：9[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/7740
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 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.