Communications of the ACM
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
An Investigation of Phylogenetic Likelihood Methods
BIBE '03 Proceedings of the 3rd IEEE Symposium on BioInformatics and BioEngineering
Parallel algorithms for Bayesian phylogenetic inference
Journal of Parallel and Distributed Computing - High-performance computational biology
High performance, bayesian-based phylogenetic inference framework
High performance, bayesian-based phylogenetic inference framework
A comparison of programming models for multiprocessors with explicitly managed memory hierarchies
Proceedings of the 14th ACM SIGPLAN symposium on Principles and practice of parallel programming
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
Proceedings of the 2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis
A new hybrid parallel algorithm for mrbayes
ICA3PP'10 Proceedings of the 10th international conference on Algorithms and Architectures for Parallel Processing - Volume Part I
Fine-grain parallelism using multi-core, Cell/BE, and GPU Systems
Parallel Computing
The Journal of Supercomputing
Hi-index | 0.00 |
This paper describes the implementation and performance of PBPI, a parallel implementation of Bayesian phylogenetic inference method for DNA sequence data. By combining the Markov Chain Monte Carlo (MCMC) method with likelihood-based assessment of phylogenies, Bayesian phylogenetic inferences can incorporate complex statistic models into the process of phylogenetic tree estimation. However, Bayesian analyses are extremely computationally expensive. PBPI uses algorithmic improvements and parallel processing to achieve significant performance improvement over comparable Bayesian phylogenetic inference programs. We evaluated the performance and accuracy of PBPI using a simulated dataset on System X, a terascale supercomputer at Virginia Tech. Our results show that PBPI identifies equivalent tree estimates 1424 times faster on 256 processors than a widely-used, best-available (albeit sequential), Bayesian phylogenetic inference program. PBPI also achieves linear speedup with the number of processors for large problem sizes. Most importantly, the PBPI framework enables Bayesian phylogenetic analysis of large datasets previously impracticable.