Highly accelerated simulations of glassy dynamics using GPUs: caveats on limited floating-point precision

TL;DR

This paper demonstrates how GPUs can significantly accelerate large-scale molecular dynamics simulations, highlights the importance of floating-point precision for accuracy, and discusses the trade-offs between speed and numerical stability.

Contribution

It introduces GPU-based MD simulation techniques with double-single precision to ensure stability and compares GPU performance with CPU and distributed CPU implementations.

Findings

GPU simulations achieve up to 80x speedup over CPU

Double-single precision maintains energy conservation over 10^8 steps

Single precision can lead to physically incorrect results in slow dynamics

Abstract

Modern graphics processing units (GPUs) provide impressive computing resources, which can be accessed conveniently through the CUDA programming interface. We describe how GPUs can be used to considerably speed up molecular dynamics (MD) simulations for system sizes ranging up to about 1 million particles. Particular emphasis is put on the numerical long-time stability in terms of energy and momentum conservation, and caveats on limited floating-point precision are issued. Strict energy conservation over 10^8 MD steps is obtained by double-single emulation of the floating-point arithmetic in accuracy-critical parts of the algorithm. For the slow dynamics of a supercooled binary Lennard-Jones mixture, we demonstrate that the use of single-floating point precision may result in quantitatively and even physically wrong results. For simulations of a Lennard-Jones fluid, the described implementation shows speedup factors of up to 80 compared to a serial implementation for the CPU, and a single GPU was found to compare with a parallelised MD simulation using 64 distributed cores.