Proceedings of the 2001 ACM/IEEE conference on Supercomputing
High-Performance Algorithm Engineering for Computational Phylogenetics
The Journal of Supercomputing - Special issue on computational issues in fluid dynamics optimization and simulation
Proceedings of the 2002 ACM/IEEE conference on Supercomputing
Parallel Metropolis-Coupled Markov Chain Monte Carlo for Bayesian
Parallel Metropolis-Coupled Markov Chain Monte Carlo for Bayesian
PBPI: a high performance implementation of Bayesian phylogenetic inference
Proceedings of the 2006 ACM/IEEE conference on Supercomputing
Modeling multigrain parallelism on heterogeneous multi-core processors: a case study of the cell BE
HiPEAC'08 Proceedings of the 3rd international conference on High performance embedded architectures and compilers
Parallel Bayesian inference of range and reflectance from LaDAR profiles
Journal of Parallel and Distributed Computing
The Journal of Supercomputing
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The combination of a Markov chain Monte Carlo (MCMC) method with likelihood-based assessment of phylogenies is becoming a popular alternative to direct likelihood optimization. However, MCMC, like maximum likelihood, is a computationally expensive method. To approximate the posterior distribution of phylogenies, a Markov chain is constructed, using the Metropolis algorithm, such that the chain has the posterior distribution of the parameters of phylogenies as its stationary distribution.This paper describes parallel algorithms and their MPI-based parallel implementation for MCMC-based Bayesian phylogenetic inference. Bayesian phylogenetic inference is computationally expensive both in time and in memory requirements. Our variations on MCMC and their implementation were done to permit the study of large phylogenetic problems. In our approach, we can distribute either entire chains or parts of a chain to different processors, since in current models the columns of the data are independent. Evaluations on a 32-node Beowulf cluster suggest the problem scales well. A number of important points are identified, including a superlinear speedup due to more effective cache usage and the point at which additional processors slow down the process due to communication overhead.