Parallelized Stochastic Cutoff Method for Long-Range Interacting Systems

We present a method to parallelize the stochastic cutoff (SCO) method, which is a Monte-Carlo method for long-range interacting systems. After interactions are eliminated by the SCO method, we subdivide the lattice into non-interacting interpenetrating sublattices. This subdivision enables us to par...

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Bibliographic Details
Published inarXiv.org
Main Authors Endo, Eishin, Toga, Yuta, Sasaki, Munetaka
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 26.06.2015
Subjects
Algorithms
Computation
Graph coloring
Monte Carlo simulation
Parallel processing
Physics - Statistical Mechanics
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Summary:We present a method to parallelize the stochastic cutoff (SCO) method, which is a Monte-Carlo method for long-range interacting systems. After interactions are eliminated by the SCO method, we subdivide the lattice into non-interacting interpenetrating sublattices. This subdivision enables us to parallelize Monte-Carlo calculation in the SCO method. Such subdivision is found by numerically solving the vertex coloring of a graph created by the SCO method. We use an algorithm proposed by Kuhn and Wattenhofer to solve the vertex coloring by parallel computation. The present method was applied to a two-dimensional magnetic dipolar system on an \(L\times L\) square lattice to examine its parallelization efficiency. The result showed that, in the case of L=2304, the speed of computation increased about 102 times by parallel computation with 288 processors.
ISSN:2331-8422
DOI:10.48550/arxiv.1503.03295