Parallelized Stochastic Cutoff Method for Long-Range Interacting Systems
Abstract
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 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.
Cite
@article{arxiv.1503.03295,
title = {Parallelized Stochastic Cutoff Method for Long-Range Interacting Systems},
author = {Eishin Endo and Yuta Toga and Munetaka Sasaki},
journal= {arXiv preprint arXiv:1503.03295},
year = {2015}
}
Comments
8 pages, 10 figures; 2 figures are added