Two Parallel Swendsen-Wang Cluster Algorithms Using Message-Passing Paradigm

TL;DR

This paper introduces two parallel Swendsen-Wang cluster algorithms using message-passing interface, achieving significant speedups, and extends them to develop parallel probability changing cluster algorithms.

Contribution

It presents two novel parallel SWC algorithms based on different models and analyzes their efficiency, also proposing parallel PCC algorithms.

Findings

Speedup of 24 with 40 processors for DPM

Speedup of 16 with 37 processors for MSPM

Comparison of efficiency across temperature and system size

Abstract

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.