Open AccessProceedings Article
Accelerating MCMC via parallel predictive prefetching
Elaine Angelino,Eddie Kohler,Amos Waterland,Margo Seltzer,Ryan P. Adams +4 more
- 23 Jul 2014
- pp 22-31
TL;DR: This work speculatively evaluates many potential steps of an MCMC chain in parallel while exploiting fast, iterative approximations to the target density, and achieves speedup close to linear in the number of available cores.
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Abstract: Parallel predictive prefetching is a new framework 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
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