远程相互作用系统的并行随机截止方法
Parallelized Stochastic Cutoff Method for Long-Range Interacting Systems
摘要：
我们提出了一种方法来并行随机截断（SCO）方法，这是一个远程相互作用系统的蒙特卡罗方法。通过SCO方法消除相互作用后，我们将晶格细分为互不相互作用的互穿子晶格。这种细分使我们能够并行Monte-Carlo计算的SCO方法。这种细分是通过数值求解由SCO方法创建的图的顶点着色来找到的。我们使用Kuhn和Wattenhofer提出的算法来解决顶点着色的并行计算。将该方法应用于$L\times L$正方形格子上的二维磁偶极系统，考察其并行化效率。结果表明，在L=2304的情况下，用288个处理器并行计算，计算速度提高了约102倍。
英文摘要：
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.
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