Accelerating MCMC via parallel predictive prefetching

Elaine Angelino, Eddie Kohler, Amos Waterland, Margo Seltzer, Ryan P. Adams

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

Parallel predictive prefetching is a new framework for accelerating a large class of widelyused 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.

Original languageEnglish (US)
Title of host publicationUncertainty in Artificial Intelligence - Proceedings of the 30th Conference, UAI 2014
EditorsNevin L. Zhang, Jin Tian
PublisherAUAI Press
Pages22-31
Number of pages10
ISBN (Electronic)9780974903910
StatePublished - Jan 1 2014
Externally publishedYes
Event30th Conference on Uncertainty in Artificial Intelligence, UAI 2014 - Quebec City, Canada
Duration: Jul 23 2014Jul 27 2014

Publication series

NameUncertainty in Artificial Intelligence - Proceedings of the 30th Conference, UAI 2014

Other

Other30th Conference on Uncertainty in Artificial Intelligence, UAI 2014
CountryCanada
CityQuebec City
Period7/23/147/27/14

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

Fingerprint Dive into the research topics of 'Accelerating MCMC via parallel predictive prefetching'. Together they form a unique fingerprint.

  • Cite this

    Angelino, E., Kohler, E., Waterland, A., Seltzer, M., & Adams, R. P. (2014). Accelerating MCMC via parallel predictive prefetching. In N. L. Zhang, & J. Tian (Eds.), Uncertainty in Artificial Intelligence - Proceedings of the 30th Conference, UAI 2014 (pp. 22-31). (Uncertainty in Artificial Intelligence - Proceedings of the 30th Conference, UAI 2014). AUAI Press.