On the parallelisation of MCMC-based image processing

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 found a wide variety of applications in fields including computational biology and physics, financial econometrics, machine learning and image processing. Whilst divide and conquer is an obvious means to simplify image processing tasks, "naively" dividing an image into smaller images to be processed separately results in anomalies and breaks the statistical validity of the MCMC algorithm. We present a method of grouping the spatially local moves and temporarily partitioning the image to allow those moves to be processed in parallel, reducing the runtime whilst conserving the properties of the MCMC method. We calculat...

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