Accelerating Markov chain Monte Carlo via parallel predictive prefetching

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Accelerating Markov chain Monte Carlo via parallel predictive prefetching

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Title: Accelerating Markov chain Monte Carlo via parallel predictive prefetching
Author: Angelino, Elaine Lee
Citation: Angelino, Elaine Lee. 2014. Accelerating Markov chain Monte Carlo via parallel predictive prefetching. Doctoral dissertation, Harvard University.
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Abstract: We present a general framework for accelerating a large class of widely used Markov chain Monte Carlo (MCMC) algorithms. This dissertation demonstrates that MCMC inference can be accelerated in a model of parallel computation that uses speculation to predict and complete computational work ahead of when it is known to be useful. By exploiting fast, iterative approximations to the target density, we can speculatively evaluate many potential future steps of the chain in parallel. In Bayesian inference problems, this approach can accelerate sampling from the target distribution, without compromising exactness, by exploiting subsets of data. It 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, it achieves speedup over serial evaluation that is close to linear in the number of available cores.
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:13070022
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