A Parallel Cluster Labeling Method for Monte Carlo Dynamics

TL;DR

This paper introduces a parallel clustering algorithm for Monte Carlo simulations that efficiently utilizes MIMD supercomputers, enabling large-scale 2D Ising model simulations with high efficiency and speed.

Contribution

It presents a novel parallel cluster labeling method combining local procedures and neighbor relaxation, optimized for large systems on parallel architectures.

Findings

Achieved simulation of 2D Ising systems up to 27808x27808 sites.

Obtained updating times around 82 nanoseconds per site.

Achieved efficiencies greater than 90% on 256 processors.

Abstract

We present an algorithm for cluster dynamics to efficiently simulate large systems on MIMD parallel computers with large numbers of processors. The method divides physical space into rectangular cells which are assigned to processors and combines a serial local procedure with a nearest neighbor relaxation process. By controlling overhead and reducing inter-processor communication this method attains good performance and speed-up. The complexity and scaling properties of the algorithm are analyzed. The algorithm has been used to simulate large 2d Ising systems (up to 27808 X 27808 sites) with Swendsen-Wang dynamics. Typical updating times on the order of 82 nanosecs/site and efficiencies larger than 90% have been obtained using 256 processors on the CM-5 supercomputer.