Adaptive Multi-GPU Exchange Monte Carlo for the 3D Random Field Ising Model

TL;DR

This paper introduces an adaptive multi-GPU Exchange Monte Carlo method for efficiently simulating the 3D Random Field Ising Model, achieving significant speedups and scalability across multiple GPUs.

Contribution

It presents a novel adaptive two-level parallelization scheme that enhances performance and scalability of Monte Carlo simulations on multi-GPU systems.

Findings

Achieves 10-100x speedup over CPU implementations.

Maintains approximately 99% efficiency in multi-GPU scaling.

Enables simulations of larger system sizes (L=32, 64) on workstation hardware.

Abstract

We present an adaptive multi-GPU Exchange Monte Carlo method designed for the simulation of the 3D Random Field Model. The algorithm design is based on a two-level parallelization scheme that allows the method to scale its performance in the presence of faster and GPUs as well as multiple GPUs. The set of temperatures is adapted according to the exchange rate observed from short trial runs, leading to an increased exchange rate at zones where the exchange process is sporadic. Performance results show that parallel tempering is an ideal strategy for being implemented on the GPU, and runs between one to two orders of magnitude with respect to a single-core CPU version, with multi-GPU scaling being approximately $99\%$ efficient. The results obtained extend the possibilities of simulation to sizes of $L = 32, 64$ for a workstation with two GPUs.