Toward large-scale Hybrid Monte Carlo simulations of the Hubbard model on graphics processing units
Department of Physics, The Ohio State University, Columbus, OH 432101117, USA
arXiv:1007.3432 [cond-mat.stat-mech] (20 Jul 2010)
@article{wendt2010toward,
title={Toward large-scale Hybrid Monte Carlo simulations of the Hubbard model on graphics processing units},
author={Wendt, K.A. and Drut, J.E. and L{\”a}hde, T.A.},
journal={Arxiv preprint arXiv:1007.3432},
year={2010}
}
The performance of the Hybrid Monte Carlo algorithm is determined by the speed of sparse matrix-vector multiplication within the context of preconditioned conjugate gradient iteration. We study these operations as implemented for the fermion matrix of the Hubbard model in d+1 space-time dimensions, and report a performance comparison between a 2.66 GHz Intel Xeon E5430 CPU and an NVIDIA Tesla C1060 GPU using double-precision arithmetic. We find speedup factors ranging between 30-350 for d = 1, and in excess of 40 for d = 3. We argue that such speedups are of considerable impact for large-scale simulational studies of quantum many-body systems.
November 11, 2010 by hgpu
