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Toward large-scale Hybrid Monte Carlo simulations of the Hubbard model on graphics processing units

Kyle A. Wendt, Joaquin E. Drut, Timo A. Lahde
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}

}

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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.
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