Accelerating MCMC via Parallel Predictive Prefetching

Elaine Angelino Harvard University, Eddie Kohler Harvard University, Margo Seltzer Harvard University, Amos Waterland Harvard University, Ryan Adams Harvard
Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, PMLR R12:795-804, 2014.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR12-university14v, title = {Accelerating {MCMC} via Parallel Predictive Prefetching}, author = {University, Elaine Angelino Harvard and University, Eddie Kohler Harvard and University, Margo Seltzer Harvard and University, Amos Waterland Harvard and Harvard, Ryan Adams}, booktitle = {Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence}, pages = {795--804}, year = {2014}, editor = {Zhang, Nevin L. and Tian, Jin}, volume = {R12}, series = {Proceedings of Machine Learning Research}, month = {23--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r12/main/assets/university14v/university14v.pdf}, url = {https://proceedings.mlr.press/r12/university14v.html}, 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.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Accelerating MCMC via Parallel Predictive Prefetching %A Elaine Angelino Harvard University %A Eddie Kohler Harvard University %A Margo Seltzer Harvard University %A Amos Waterland Harvard University %A Ryan Adams Harvard %B Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2014 %E Nevin L. Zhang %E Jin Tian %F pmlr-vR12-university14v %I PMLR %P 795--804 %U https://proceedings.mlr.press/r12/university14v.html %V R12 %X 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. %Z Reissued by PMLR on 04 October 2026.
APA
University, E.A.H., University, E.K.H., University, M.S.H., University, A.W.H. & Harvard, R.A.. (2014). Accelerating MCMC via Parallel Predictive Prefetching. Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R12:795-804 Available from https://proceedings.mlr.press/r12/university14v.html. Reissued by PMLR on 04 October 2026.

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