On the parallelisation of MCMC by speculative chain execution

Abstract--The increasing availability of multi-core and multiprocessor architectures provides new opportunities for improving the performance of many computer simulations. Markov Chain Monte Carlo (MCMC) simulations are widely used for approximate counting problems, Bayesian inference and as a means for estimating very high-dimensional integrals. As such MCMC has had a wide variety of applications in fields including computational biology and physics, financial econometrics, machine learning and image processing. One method for improving the performance of Markov Chain Monte Carlo simulations is to use SMP machines to perform `speculative moves', reducing the runtime whilst producing statistically identical results to conventional sequential implementations. In this paper we examine the circumstances under which the original speculative moves method performs poorly, and consider how some of the situations can be addressed by refining the implementation. We extend the technique to ...

• Type: Journal
• Year: 2010
• Where: IPPS
• Authors: Jonathan M. R. Byrd, Stephen A. Jarvis, Abhir H. Bhalerao
• Keywords: Distributed And Parallel Computing, IPPS 2010, Markov Chain Monte Carlo, Original Speculative Moves, Speculative Moves