Massively parallel Monte Carlo for many-particle simulations on GPUs

TL;DR

This paper introduces a massively parallel GPU-based Monte Carlo algorithm for simulating many-particle systems, achieving significant speedups and enabling large-scale studies of phase transitions in hard disk systems.

Contribution

The paper presents a novel parallel Monte Carlo method that maintains detailed balance and is optimized for GPU architectures, allowing large-scale simulations of hard disk systems.

Findings

GPU implementation executes over one billion trial moves per second

Achieves 148x speedup over a CPU core

Enables simulation of systems with up to one million particles

Abstract

Current trends in parallel processors call for the design of efficient massively parallel algorithms for scientific computing. Parallel algorithms for Monte Carlo simulations of thermodynamic ensembles of particles have received little attention because of the inherent serial nature of the statistical sampling. In this paper, we present a massively parallel method that obeys detailed balance and implement it for a system of hard disks on the GPU. We reproduce results of serial high-precision Monte Carlo runs to verify the method. This is a good test case because the hard disk equation of state over the range where the liquid transforms into the solid is particularly sensitive to small deviations away from the balance conditions. On a Tesla K20, our GPU implementation executes over one billion trial moves per second, which is 148 times faster than on a single Intel Xeon E5540 CPU core, enables 27 times better performance per dollar, and cuts energy usage by a factor of 13. With this improved performance we are able to calculate the equation of state for systems of up to one million hard disks. These large system sizes are required in order to probe the nature of the melting transition, which has been debated for the last forty years. In this paper we present the details of our computational method, and discuss the thermodynamics of hard disks separately in a companion paper.