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)...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2021-11-01
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| Series: | PeerJ |
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| Online Access: | https://peerj.com/articles/12438.pdf |
| _version_ | 1797420921079726080 |
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| author | Sebastian Höhna Michael J. Landis John P. Huelsenbeck |
| author_facet | Sebastian Höhna Michael J. Landis John P. Huelsenbeck |
| author_sort | Sebastian Höhna |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T07:08:52Z |
| format | Article |
| id | doaj.art-f0d5f15a6f444068a4bc767018c0774d |
| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T07:08:52Z |
| publishDate | 2021-11-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ |
| spelling | doaj.art-f0d5f15a6f444068a4bc767018c0774d2023-12-03T09:18:23ZengPeerJ Inc.PeerJ2167-83592021-11-019e1243810.7717/peerj.12438Parallel power posterior analyses for fast computation of marginal likelihoods in phylogeneticsSebastian Höhna0Michael J. Landis1John P. Huelsenbeck2GeoBio-Center, Ludwig-Maximilians-Universität München, Munich, GermanyDepartment of Biology, Washington University in St. Louis, St. Louis, United States of AmericaDepartment of Integrative Biology, University of California,, Berkeley, United States of AmericaIn 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.https://peerj.com/articles/12438.pdfBayes factorParallelizationPhylogenetics |
| spellingShingle | Sebastian Höhna Michael J. Landis John P. Huelsenbeck Parallel power posterior analyses for fast computation of marginal likelihoods in phylogenetics PeerJ Bayes factor Parallelization Phylogenetics |
| title | 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_short | 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 |
| topic | Bayes factor Parallelization Phylogenetics |
| url | https://peerj.com/articles/12438.pdf |
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