Parallelized stochastic cutoff method for long-range interacting systems

Eishin Endo, Yuta Toga, Munetaka Sasaki

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

We present a method of parallelizing 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 a lattice into noninteracting interpenetrating sublattices. This subdivision enables us to parallelize the 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. This method was applied to a two-dimensional magnetic dipolar system on an L x 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.

Original languageEnglish
Article number074002
Journaljournal of the physical society of japan
Volume84
Issue number7
DOIs
Publication statusPublished - 2015 Jul 15

ASJC Scopus subject areas

  • Physics and Astronomy(all)

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