Parallelized stochastic cutoff method for long-range interacting systems

Eishin Endo, Yuta Toga, Munetaka Sasaki

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


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
Issue number7
Publication statusPublished - 2015 Jul 15

ASJC Scopus subject areas

  • Physics and Astronomy(all)


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