COLLOQUIUM
Department of Computer Science and Engineering
University of South Carolina

 
Parallel Bayesian Phylogenetic Inference

Xizhou Feng

Department of Computer Science and Engineering 
University of South Carolina

Date: October 11, 2002 (Friday)
Time: 3:30-4:30PM
Place: Swearingen 1C01 (Amoco Hall)


Abstract

We describe MPI-based parallel computation approaches to Bayesian
phylogenetic inference using Markov chain Monte Carlo.  Bayesian
inference of phylogeny is based on the posterior probability of the
phylogenetic model.  A Markov chain can be constructed in which the
stationary distribution of the state space of parameters approximates
the posterior probability of the parameters of the phylogenetic model.
When applied to infer large phylogeny, Bayesian phylogenetic inference
is computational expensive both in computation time and memory
requirement.  Using MPI, we developed a parallel implementation to
speed up Bayesian phylogenetic inference and allow it to perform
inference in larger models. The implementation is evaluated on a
32-processor Beowulf cluster machine.

Xizhou Feng is a Ph. D. student in the Department of Computer Science
and Engineering at the University of South Carolina. He received his
B.S. degree in Electrical Engineering from China Textile University
and his M.S. degree in Engineering Thermophysics from Tsinghua
University. Before he came to the University of South Carolina, he was
an engineer at Shanghai Telecom Technological Research Institute, a
research division of China Telecom.  His current research, directed by
Dr. Duncan A. Buell, is in the areas of parallel and distributed
computation.