1101.1427 (Martin Weigel)

Performance potential for simulating spin models on GPU    [PDF]

Martin Weigel

Graphics processing units (GPUs) are recently being used to an increasing
degree for general computational purposes. This development is motivated by
their theoretical peak performance, which significantly exceeds that of broadly
available CPUs. For practical purposes, however, it is far from clear how much
of this theoretical performance can be realized in actual scientific
applications. As is discussed here for the case of studying classical spin
models of statistical mechanics by Monte Carlo simulations, only an explicit
tailoring of the involved algorithms to the specific architecture under
consideration allows to harvest the computational power of GPU systems. A
number of examples, ranging from Metropolis simulations of ferromagnetic Ising
models, over continuous Heisenberg and disordered spin-glass systems to
parallel-tempering simulations are discussed. Significant speed-ups by factors
of up to 1000 compared to serial CPU code as well as previous GPU
implementations are observed.

View original: http://arxiv.org/abs/1101.1427