Accelerating micromagnetic and atomistic simulations using multiple GPUs

TL;DR

This paper demonstrates how multi-GPU algorithms can significantly accelerate micromagnetic and atomistic spin dynamics simulations, enabling larger simulations and better performance scaling despite data transfer bottlenecks.

Contribution

It introduces a multi-GPU convolution algorithm for spin simulations, achieving near-ideal scaling and enabling very large simulations on standard workstations.

Findings

Speedup factors up to 3.7 with 4 GPUs for micromagnetic simulations.

Large atomistic simulations with up to 1 billion spins are feasible on a 4-GPU workstation.

Performance scaling depends on inter-GPU data transfer rate and connection topology.

Abstract

It is shown micromagnetic and atomistic spin dynamics simulations can use multiple GPUs in order to reduce computation time, but also to allow for a larger simulation size than is possible on a single GPU. Whilst interactions which depend on neighbouring spins, such as exchange interactions, may be implemented efficiently by transferring data between GPUs using halo regions, or alternatively using direct memory accesses, implementing the long-range demagnetizing interaction is the main difficulty in achieving good performance scaling, where the data transfer rate between GPUs is a significant bottleneck. A multi-GPU convolution algorithm is developed here, which relies on single-GPU FFTs executed in parallel. It is shown that even for micromagnetic simulations where the demagnetizing interaction computation time dominates, good performance scaling may be achieved, with speedup factors up to 1.8, 2.5, and 3.1, for 2, 3, and 4 GPUs respectively. The code developed here can be used for any number of GPUs in parallel, with performance scaling strongly dependent on inter-GPU data transfer rate and connection topology. This is further improved in micromagnetic simulations which include a spin transport solver, obtaining speedup factors up to 1.96, 2.8, and 3.7, for 2, 3, and 4 GPUs respectively. The best case scenario is obtained for atomistic spin dynamics simulations, where the demagnetizing interaction is implemented with spin-averaged cells. Using a single workstation with 4 GPUs, it is shown atomistic spin dynamics simulations with up to 1 billion spins, and atomistic Monte Carlo simulations with up to 2 billion spins are possible, with a near-ideal performance scaling.