Parallel power posterior analyses for fast computation of marginal likelihoods in phylogenetics
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Abstract
Motivation
In Bayesian phylogenetic inference, marginal likelihoods are estimated using either 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.
Results
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 11.4x 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.
Availability
The methods described here are implemented in our open-source software RevBayes which is available from http://www.RevBayes.com .
Contact
Sebastian.Hoehna@gmail.com
Supplementary information
Supplementary data are available at Bioinformatics online.
