Angelino, ElaineKohler, EdwardWaterland, AmosSeltzer, MargoAdams, Ryan Prescott2015-09-222014Angelino, Elaine, Eddie Kohler, Amos Waterland, Margo Seltzer, and Ryan P. Adams. 2014. "Accelerating MCMC with Parallel Predictive Prefetching." In Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, Quebec City, Canada, July 23-27, 2014: 22-31.9780974903910http://nrs.harvard.edu/urn-3:HUL.InstRepos:22772115Parallel predictive prefetching is a new frame- work for accelerating a large class of widely- used Markov chain Monte Carlo (MCMC) algorithms. It speculatively evaluates many potential steps of an MCMC chain in parallel while exploiting fast, iterative approximations to the target density. This can accelerate sampling from target distributions in Bayesian inference problems. Our approach takes advantage of whatever parallel resources are available, but produces results exactly equivalent to standard serial execution. In the initial burn-in phase of chain evaluation, we achieve speedup close to linear in the number of available cores.en-USConference Paper2015-09-22