An Efficient Cell List Implementation for Monte Carlo Simulation on GPUs

TL;DR

This paper introduces a novel GPU-optimized cell list algorithm that significantly enhances the performance of large-scale Monte Carlo molecular simulations, outperforming existing methods across various system sizes.

Contribution

A new cell list algorithm tailored for GPU architectures that improves efficiency and performance in large-scale Monte Carlo simulations.

Findings

Significant performance gains for large systems

No performance improvement for small systems with traditional methods

Our approach outperforms existing GPU implementations

Abstract

Maximizing the performance potential of the modern day GPU architecture requires judicious utilization of available parallel resources. Although dramatic reductions can often be obtained through straightforward mappings, further performance improvements often require algorithmic redesigns to more closely exploit the target architecture. In this paper, we focus on efficient molecular simulations for the GPU and propose a novel cell list algorithm that better utilizes its parallel resources. Our goal is an efficient GPU implementation of large-scale Monte Carlo simulations for the grand canonical ensemble. This is a particularly challenging application because there is inherently less computation and parallelism than in similar applications with molecular dynamics. Consistent with the results of prior researchers, our simulation results show traditional cell list implementations for Monte Carlo simulations of molecular systems offer effectively no performance improvement for small systems [5, 14], even when porting to the GPU. However for larger systems, the cell list implementation offers significant gains in performance. Furthermore, our novel cell list approach results in better performance for all problem sizes when compared with other GPU implementations with or without cell lists.