Toward large-scale Hybrid Monte Carlo simulations of the Hubbard model on graphics processing units
Statistical Mechanics
2016-08-14 v1 Computational Physics
Abstract
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.
Keywords
Cite
@article{arxiv.1007.3432,
title = {Toward large-scale Hybrid Monte Carlo simulations of the Hubbard model on graphics processing units},
author = {Kyle A. Wendt and Joaquín E. Drut and Timo A. Lähde},
journal= {arXiv preprint arXiv:1007.3432},
year = {2016}
}
Comments
8 pages, 5 figures