Highly accelerated simulations of glassy dynamics using GPUs: caveats on limited floating-point precision
Arnold Sommerfeld Center for Theoretical Physics and Center for NanoScience (CeNS), Fakultat fur Physik, Ludwig-Maximilians-Universitat Munchen, Theresienstrasse 37, 80333 Munchen, Germany
Computer Physics Communications, arXiv:0912.3824v2 [physics.comp-ph] (17 Jan 2011)
@article{2009arXiv0912.3824C,
author={Colberg}, P.~H. and {H{“o}fling}, F.},
title={“{Highly accelerated simulations of glassy dynamics using GPUs: caveats on limited floating-point precision}”},
journal={ArXiv e-prints},
archivePrefix={“arXiv”},
eprint={0912.3824},
primaryClass={“physics.comp-ph”},
keywords={Physics – Computational Physics, Condensed Matter – Soft Condensed Matter, Computer Science – Distributed, Parallel, and Cluster Computing},
year={2009},
month={dec},
adsurl={http://adsabs.harvard.edu/abs/2009arXiv0912.3824C},
adsnote={Provided by the SAO/NASA Astrophysics Data System}
}
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.
January 18, 2011 by hgpu
