Simulating spin models on GPU

TL;DR

This paper explores the potential of using GPU architectures to efficiently simulate spin models like the Ising model, highlighting the advantages over traditional CPU-based simulations.

Contribution

It demonstrates how to leverage GPU parallelism for spin model simulations, providing insights into performance benefits and implementation strategies.

Findings

GPU simulations outperform CPU in speed for spin models

Parallel processing significantly accelerates Ising model computations

GPU architecture enables scalable simulations of large spin systems

Abstract

Over the last couple of years it has been realized that the vast computational power of graphics processing units (GPUs) could be harvested for purposes other than the video game industry. This power, which at least nominally exceeds that of current CPUs by large factors, results from the relative simplicity of the GPU architectures as compared to CPUs, combined with a large number of parallel processing units on a single chip. To benefit from this setup for general computing purposes, the problems at hand need to be prepared in a way to profit from the inherent parallelism and hierarchical structure of memory accesses. In this contribution I discuss the performance potential for simulating spin models, such as the Ising model, on GPU as compared to conventional simulations on CPU.