ITP OpenIR  > SCI期刊论文
Navarro, CA; Huang, W; Deng, YJ; Navarro, CA (reprint author), Univ Austral Chile, Inst Informat, Valdivia, Chile.
Adaptive multi-GPU Exchange Monte Carlo for the 3D Random Field Ising Model
Source PublicationCOMPUTER PHYSICS COMMUNICATIONS
Language英语
KeywordGpu Computing Adaptive Temperatures Exchange Monte Carlo Algorithm Random Field Ising Model
AbstractThis work presents an adaptive multi-GPU Exchange Monte Carlo approach for the simulation of the 3D Random Field Ising Model (RFIM). The design is based on a two-level parallelization. The first level, spin-level parallelism, maps the parallel computation as optimal 3D thread-blocks that simulate blocks of spins in shared memory with minimal halo surface, assuming a constant block volume. The second level, replica-level parallelism, uses multi-GPU computation to handle the simulation of an ensemble of replicas. CUDA's concurrent kernel execution feature is used in order to fill the occupancy of each GPU with many replicas, providing a performance boost that is more notorious at the smallest values of L. In addition to the two-level parallel design, the work proposes an adaptive multi-GPU approach that dynamically builds a proper temperature set free of exchange bottlenecks. The strategy is based on mid-point insertions at the temperature gaps where the exchange rate is most compromised. The extra work generated by the insertions is balanced across the GPUs independently of where the mid-point insertions were performed. Performance results show that spin-level performance is approximately two orders of magnitude faster than a single-core CPU version and one order of magnitude faster than a parallel multi-core CPU version running on 16-cores. Multi-GPU performance is highly convenient under a weak scaling setting, reaching up to 99% efficiency as long as the number of GPUs and L increase together. The combination of the adaptive approach with the parallel multi-GPU design has extended our possibilities of simulation to sizes of L = 32, 64 for a workstation with two GPUs. Sizes beyond L = 64 can eventually be studied using larger multi-GPU systems. (C) 2016 Elsevier B.V. All rights reserved.
2016
Volume205Pages:48-60
Subject AreaComputer Science ; Physics
DOIhttp://dx.doi.org/10.1016/j.cpc.2016.04.007
Indexed BySCI
Funding OrganizationNvidia GPU Research Center at the Department of Computer Science (DCC) of University of Chile ; Nvidia GPU Research Center at the Department of Computer Science (DCC) of University of Chile ; Nvidia GPU Research Center at the Department of Computer Science (DCC) of University of Chile ; Nvidia GPU Research Center at the Department of Computer Science (DCC) of University of Chile ; Supercomputing Center of University of Science and Technology of China ; Supercomputing Center of University of Science and Technology of China ; Supercomputing Center of University of Science and Technology of China ; Supercomputing Center of University of Science and Technology of China ; FONDECYT [3160182] ; FONDECYT [3160182] ; FONDECYT [3160182] ; FONDECYT [3160182] ; CONICYT, Chile ; CONICYT, Chile ; CONICYT, Chile ; CONICYT, Chile ; National Science Foundation of China (NSFC) [11275185] ; National Science Foundation of China (NSFC) [11275185] ; National Science Foundation of China (NSFC) [11275185] ; National Science Foundation of China (NSFC) [11275185] ; Open Project Program of State Key Laboratory of Theoretical Physics, Institute of Theoretical Physics, Chinese Academy of Sciences, China [Y5KF191CJ1] ; Open Project Program of State Key Laboratory of Theoretical Physics, Institute of Theoretical Physics, Chinese Academy of Sciences, China [Y5KF191CJ1] ; Open Project Program of State Key Laboratory of Theoretical Physics, Institute of Theoretical Physics, Chinese Academy of Sciences, China [Y5KF191CJ1] ; Open Project Program of State Key Laboratory of Theoretical Physics, Institute of Theoretical Physics, Chinese Academy of Sciences, China [Y5KF191CJ1]
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Document Type期刊论文
Identifierhttp://ir.itp.ac.cn/handle/311006/21583
CollectionSCI期刊论文
Corresponding AuthorNavarro, CA (reprint author), Univ Austral Chile, Inst Informat, Valdivia, Chile.
Recommended Citation
GB/T 7714
Navarro, CA,Huang, W,Deng, YJ,et al. Adaptive multi-GPU Exchange Monte Carlo for the 3D Random Field Ising Model[J]. COMPUTER PHYSICS COMMUNICATIONS,2016,205:48-60.
APA Navarro, CA,Huang, W,Deng, YJ,&Navarro, CA .(2016).Adaptive multi-GPU Exchange Monte Carlo for the 3D Random Field Ising Model.COMPUTER PHYSICS COMMUNICATIONS,205,48-60.
MLA Navarro, CA,et al."Adaptive multi-GPU Exchange Monte Carlo for the 3D Random Field Ising Model".COMPUTER PHYSICS COMMUNICATIONS 205(2016):48-60.
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