两种基于消息传递模式的并行Swendsen-Wang聚类算法
Two Parallel Swendsen-Wang Cluster Algorithms Using Message-Passing Paradigm
摘要：
在这篇文章中，我们提出了两个不同的并行Swendsen-Wang集群（SWC）算法使用消息传递接口（MPI）。一种是基于主从式并行模型（MSPM），另一种是基于数据并行模型（DSPM）。在40个处理器的情况下，采用该算法的加速比为24;在37个处理器的情况下，采用MSPM算法的加速比为16。在不同的温度和系统规模的两种算法的加速比仔细检查实验和理论，并比较它们的效率。最后，在这两种并行SWC算法的基础上，提出了两种并行概率变化聚类（PCC）算法。
英文摘要：
In this article, we present two different parallel Swendsen-Wang Cluster(SWC) algorithms using message-passing interface(MPI). One is based on Master-Slave Parallel Model(MSPM) and the other is based on Data-Parallel Model(DPM). A speedup of 24 with 40 processors and 16 with 37 processors is achieved with the DPM and MSPM respectively. The speedup of both algorithms at different temperature and system size is carefully examined both experimentally and theoretically, and a comparison of their efficiency is made. In the last section, based on these two parallel SWC algorithms, two parallel probability changing cluster(PCC) algorithms are proposed.
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