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

文献类型：期刊论文

作者Wu, Baodong1,2; Li, Shigang1; Zhang, Yunquan1; Nie, Ningming3
刊名COMPUTER PHYSICS COMMUNICATIONS
出版日期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
DOI10.1016/j.cpc.2016.07.008
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.
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
语种英语
WOS记录号WOS:000390181300015
出版者ELSEVIER SCIENCE BV
源URL[http://119.78.100.204/handle/2XEOYT63/7739]  
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.

入库方式： OAI收割

来源：计算技术研究所

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