Parallel multivariate slice sampling
Statistics and Computing
The CIPRES science gateway: a community resource for phylogenetic analyses
Proceedings of the 2011 TeraGrid Conference: Extreme Digital Discovery
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Fine-grain parallelism using multi-core, Cell/BE, and GPU Systems
Parallel Computing
Computational Biology and Chemistry
Massive Parallelization of Serial Inference Algorithms for a Complex Generalized Linear Model
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special Issue on Monte Carlo Methods in Statistics
High performance phylogenetic analysis on CUDA-compatible GPUs
ACM SIGARCH Computer Architecture News - ACM SIGARCH Computer Architecture News/HEART '12
Embedding CIPRES science gateway capabilities in phylogenetics software environments
Proceedings of the Conference on Extreme Science and Engineering Discovery Environment: Gateway to Discovery
Speeding-up codon analysis on the cloud with local MapReduce aggregation
Information Sciences: an International Journal
The Journal of Supercomputing
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Motivation: Statistical phylogenetics is computationally intensive, resulting in considerable attention meted on techniques for parallelization. Codon-based models allow for independent rates of synonymous and replacement substitutions and have the potential to more adequately model the process of protein-coding sequence evolution with a resulting increase in phylogenetic accuracy. Unfortunately, due to the high number of codon states, computational burden has largely thwarted phylogenetic reconstruction under codon models, particularly at the genomic-scale. Here, we describe novel algorithms and methods for evaluating phylogenies under arbitrary molecular evolutionary models on graphics processing units (GPUs), making use of the large number of processing cores to efficiently parallelize calculations even for large state-size models. Results: We implement the approach in an existing Bayesian framework and apply the algorithms to estimating the phylogeny of 62 complete mitochondrial genomes of carnivores under a 60-state codon model. We see a near 90-fold speed increase over an optimized CPU-based computation and a 140-fold increase over the currently available implementation, making this the first practical use of codon models for phylogenetic inference over whole mitochondrial or microorganism genomes. Availability and implementation: Source code provided in BEAGLE: Broad-platform Evolutionary Analysis General Likelihood Evaluator, a cross-platform/processor library for phylogenetic likelihood computation ( http://beagle-lib.googlecode.com/). We employ a BEAGLE-implementation using the Bayesian phylogenetics framework BEAST (http://beast.bio.ed.ac.uk/). Contact:msuchard@ucla.edu; a.rambaut@ed.ac.uk