Parallel power posterior analyses for fast computation of marginal likelihoods in phylogenetics

In Bayesian phylogenetic inference, marginal likelihoods can be estimated using several different methods, including the path-sampling or stepping-stone-sampling algorithms. Both algorithms are computationally demanding because they require a series of power posterior Markov chain Monte Carlo (MCMC)...

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Autores principales:	Sebastian Höhna, Michael J. Landis, John P. Huelsenbeck
Formato:	article
Lenguaje:	EN
Publicado:	PeerJ Inc. 2021
Materias:	Bayes factor Parallelization Phylogenetics Medicine R
Acceso en línea:	https://doaj.org/article/f0d5f15a6f444068a4bc767018c0774d
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spelling	oai:doaj.org-article:f0d5f15a6f444068a4bc767018c0774d2021-11-04T15:05:35ZParallel power posterior analyses for fast computation of marginal likelihoods in phylogenetics10.7717/peerj.124382167-8359https://doaj.org/article/f0d5f15a6f444068a4bc767018c0774d2021-11-01T00:00:00Zhttps://peerj.com/articles/12438.pdfhttps://peerj.com/articles/12438/https://doaj.org/toc/2167-8359In Bayesian phylogenetic inference, marginal likelihoods can be estimated using several different methods, including the path-sampling or stepping-stone-sampling algorithms. Both algorithms are computationally demanding because they require a series of power posterior Markov chain Monte Carlo (MCMC) simulations. Here we introduce a general parallelization strategy that distributes the power posterior MCMC simulations and the likelihood computations over available CPUs. Our parallelization strategy can easily be applied to any statistical model despite our primary focus on molecular substitution models in this study. Using two phylogenetic example datasets, we demonstrate that the runtime of the marginal likelihood estimation can be reduced significantly even if only two CPUs are available (an average performance increase of 1.96x). The performance increase is nearly linear with the number of available CPUs. We record a performance increase of 13.3x for cluster nodes with 16 CPUs, representing a substantial reduction to the runtime of marginal likelihood estimations. Hence, our parallelization strategy enables the estimation of marginal likelihoods to complete in a feasible amount of time which previously needed days, weeks or even months. The methods described here are implemented in our open-source software RevBayes which is available from http://www.RevBayes.com.Sebastian HöhnaMichael J. LandisJohn P. HuelsenbeckPeerJ Inc.articleBayes factorParallelizationPhylogeneticsMedicineRENPeerJ, Vol 9, p e12438 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Bayes factor Parallelization Phylogenetics Medicine R
spellingShingle	Bayes factor Parallelization Phylogenetics Medicine R Sebastian Höhna Michael J. Landis John P. Huelsenbeck Parallel power posterior analyses for fast computation of marginal likelihoods in phylogenetics
description	In Bayesian phylogenetic inference, marginal likelihoods can be estimated using several different methods, including the path-sampling or stepping-stone-sampling algorithms. Both algorithms are computationally demanding because they require a series of power posterior Markov chain Monte Carlo (MCMC) simulations. Here we introduce a general parallelization strategy that distributes the power posterior MCMC simulations and the likelihood computations over available CPUs. Our parallelization strategy can easily be applied to any statistical model despite our primary focus on molecular substitution models in this study. Using two phylogenetic example datasets, we demonstrate that the runtime of the marginal likelihood estimation can be reduced significantly even if only two CPUs are available (an average performance increase of 1.96x). The performance increase is nearly linear with the number of available CPUs. We record a performance increase of 13.3x for cluster nodes with 16 CPUs, representing a substantial reduction to the runtime of marginal likelihood estimations. Hence, our parallelization strategy enables the estimation of marginal likelihoods to complete in a feasible amount of time which previously needed days, weeks or even months. The methods described here are implemented in our open-source software RevBayes which is available from http://www.RevBayes.com.
format	article
author	Sebastian Höhna Michael J. Landis John P. Huelsenbeck
author_facet	Sebastian Höhna Michael J. Landis John P. Huelsenbeck
author_sort	Sebastian Höhna
title	Parallel power posterior analyses for fast computation of marginal likelihoods in phylogenetics
title_short	Parallel power posterior analyses for fast computation of marginal likelihoods in phylogenetics
title_full	Parallel power posterior analyses for fast computation of marginal likelihoods in phylogenetics
title_fullStr	Parallel power posterior analyses for fast computation of marginal likelihoods in phylogenetics
title_full_unstemmed	Parallel power posterior analyses for fast computation of marginal likelihoods in phylogenetics
title_sort	parallel power posterior analyses for fast computation of marginal likelihoods in phylogenetics
publisher	PeerJ Inc.
publishDate	2021
url	https://doaj.org/article/f0d5f15a6f444068a4bc767018c0774d
work_keys_str_mv	AT sebastianhohna parallelpowerposterioranalysesforfastcomputationofmarginallikelihoodsinphylogenetics AT michaeljlandis parallelpowerposterioranalysesforfastcomputationofmarginallikelihoodsinphylogenetics AT johnphuelsenbeck parallelpowerposterioranalysesforfastcomputationofmarginallikelihoodsinphylogenetics
_version_	1718444809135325184

Parallel power posterior analyses for fast computation of marginal likelihoods in phylogenetics

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