Toward large-scale Hybrid Monte Carlo simulations of the Hubbard model on graphics processing units
arXiv:1007.3432 · doi:10.1016/j.cpc.2011.04.014
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.
8 pages, 5 figures
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