Uncertainty in Artificial Intelligence
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Accelerating MCMC via Parallel Predictive Prefetching
Elaine Angelino, Eddie Kohler, Amos Waterland, Margo Seltzer, Ryan Adams
Parallel predictive prefetching is a new frame- work for accelerating a large class of widely- used Markov chain Monte Carlo (MCMC) algo- rithms. It speculatively evaluates many potential steps of an MCMC chain in parallel while ex- ploiting fast, iterative approximations to the tar- get density. This can accelerate sampling from target distributions in Bayesian inference prob- lems. Our approach takes advantage of whatever parallel resources are available, but produces re- sults exactly equivalent to standard serial execu- tion. In the initial burn-in phase of chain evalu- ation, we achieve speedup close to linear in the number of available cores.
Pages: 22-31
PS Link:
PDF Link: /papers/14/p22-angelino.pdf
AUTHOR = "Elaine Angelino and Eddie Kohler and Amos Waterland and Margo Seltzer and Ryan Adams",
TITLE = "Accelerating MCMC via Parallel Predictive Prefetching",
BOOKTITLE = "Proceedings of the Thirtieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-14)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2014",
PAGES = "22--31"

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