GPU accelerated Monte Carlo simulations of lattice spin models

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.

5 pages, 4 figures, 1 table; Physics Procedia 15, 92 (2011)

References in corpus (2)

• Performance potential for simulating spin models on GPU
• Simulating spin models on GPU

Cited by in corpus (12)

• Massively parallel multicanonical simulations
• Connected component identification and cluster update on GPU
• Bimeron nanoconfined design
• Optimized GPU simulation of continuous-spin glass models
• Ageing at the Spin-Glass/Ferromagnet Transition: Monte Carlo Simulation using GPUs
• Monte Carlo simulation of the effects of higher order anisotropy on the spin reorientation transition in the two dimensional Heisenberg model with long range interactions
• Computation of magnetization, exchange stiffness, anisotropy, and susceptibilities in large-scale systems using GPU-accelerated atomistic parallel Monte Carlo algorithms
• Superdiffusion-like behavior in zero-temperature coarsening of the $d=3$ Ising model
• A High-Performance Implementation of Atomistic Spin Dynamics Simulations on x86 CPUs
• Dynamical heterogeneities as fingerprints of a backbone structure in Potts models
• A differentiable programming framework for spin models
• GPU Computing in Bayesian Inference of Realized Stochastic Volatility Model