Parallel Metropolis-Coupled Markov Chain Monte Carlo for Bayesian Phylogenetic Inference

Abstract

Bayesian estimation of phylogeny is based on the posterior probability distribution of trees. Currently, the only numerical method that can effectively approximate posterior probabilities of trees is Markov Chain Monte Carlo (MCMC). Standard implementations of MCMC can be prone to entrapment in local optima. A variant of MCMC, known as Metropolis-Coupled MCMC, allows multiple peaks in the landscape of trees to be more readily explored, but at the cost of increased execution time. This paper presents a parallel algorithm for Metropolis-Coupled MCMC. The proposed parallel algorithm retains the ability to explore multiple peaks in the posterior distribution of trees while maintaining a fast execution time. The algorithm has been implemented using two parallel programming models: the Message Passing Interface (MPI) and the Cashmere software distributed shared memory protocol. Performance results indicate nearly linear speed improvement in both programming models for small and large data sets. (MrBayes v3.0 is available at http://morphbank.ebc.uu.se/mrbayes/.