GPU accelerated Monte Carlo simulations of lattice spin models

TL;DR

This paper demonstrates that GPU acceleration can dramatically speed up Monte Carlo simulations of lattice spin models, achieving 100-1000x faster performance than traditional CPU methods across various algorithms.

Contribution

It provides a comprehensive evaluation of GPU-based Monte Carlo methods for spin models, highlighting their efficiency and scope compared to CPU implementations.

Findings

Speed-ups of two to three orders of magnitude over CPU implementations

Effective acceleration across multiple algorithms including Metropolis, cluster, and Wang-Landau

Demonstrates GPU suitability for large-scale statistical mechanics simulations

Abstract

We consider Monte Carlo simulations of classical spin models of statistical mechanics using the massively parallel architecture provided by graphics processing units (GPUs). We discuss simulations of models with discrete and continuous variables, and using an array of algorithms ranging from single-spin flip Metropolis updates over cluster algorithms to multicanonical and Wang-Landau techniques to judge the scope and limitations of GPU accelerated computation in this field. For most simulations discussed, we find significant speed-ups by two to three orders of magnitude as compared to single-threaded CPU implementations.