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Stochastic Speculative Computation Method and its Application to Monte Carlo Molecular Simulation

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dc.contributor.author Iizuka, Yasuki
dc.contributor.author Hamada, Akira
dc.contributor.author Suzuki, Yosuke
dc.date.accessioned 2017-12-28T00:51:55Z
dc.date.available 2017-12-28T00:51:55Z
dc.date.issued 2018-01-03
dc.identifier.isbn 978-0-9981331-1-9
dc.identifier.uri http://hdl.handle.net/10125/50095
dc.description.abstract Monte Carlo (MC) molecular simulation has significant computational complexity, and parallel processing is considered effective for computation of problems with large complexity. In recent years, multicore or many-core processors have gained significant attention as they enable computation with a large degree of parallelism on desktop computers. However, in conventional parallel processing, processes must be synchronized frequently; thus, parallel computing is not necessarily efficient. In this study, we evaluate the effect of applying MultiStart-based speculative parallel computation to MC simulations. Using probability theory, we performed theoretical verification to determine if speculative computation is more effective than conventional parallel computation methods. The parameters obtained from the theoretical calculations were observed in experiments wherein the speculative method was applied to an MC molecular simulation. In this paper, we report the results of the theoretical verification and experiments, and we show that speculative computation can accelerate MC molecular simulations.
dc.format.extent 9 pages
dc.language.iso eng
dc.relation.ispartof Proceedings of the 51st Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Soft Computing: Methods and Applications
dc.subject speculative computing, parallel processing, molecular simulation, algorithm
dc.title Stochastic Speculative Computation Method and its Application to Monte Carlo Molecular Simulation
dc.type Conference Paper
dc.type.dcmi Text
dc.identifier.doi 10.24251/HICSS.2018.208
Appears in Collections: Soft Computing: Methods and Applications


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