  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.
