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Accelerating MCMC via Parallel Predictive Prefetching
Elaine Angelino, Eddie Kohler, Amos Waterland, Margo Seltzer, Ryan Adams
Abstract:
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.
Keywords:
Pages: 22-31
PS Link:
PDF Link: /papers/14/p22-angelino.pdf
BibTex:
@INPROCEEDINGS{Angelino14,
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)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2014",
PAGES = "22--31"
}
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