  We present a case-study on the utility of graphics cards to perform massively
parallel simulation of advanced Monte Carlo methods. Graphics cards, containing
multiple Graphics Processing Units (GPUs), are self-contained parallel
computational devices that can be housed in conventional desktop and laptop
computers. For certain classes of Monte Carlo algorithms they offer massively
parallel simulation, with the added advantage over conventional distributed
multi-core processors that they are cheap, easily accessible, easy to maintain,
easy to code, dedicated local devices with low power consumption. On a
canonical set of stochastic simulation examples including population-based
Markov chain Monte Carlo methods and Sequential Monte Carlo methods, we find
speedups from 35 to 500 fold over conventional single-threaded computer code.
Our findings suggest that GPUs have the potential to facilitate the growth of
statistical modelling into complex data rich domains through the availability
of cheap and accessible many-core computation. We believe the speedup we
observe should motivate wider use of parallelizable simulation methods and
greater methodological attention to their design.
