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Abstract:
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Joint inference of phylogenetic trees and codon-based substitution models of sequence evolution remains largely computationally impractical. Parallelizing data likelihood calculations is an obvious strategy; however, across a cluster-computer, this scales with the total number of processing cores, incurring considerable cost to achieve reasonable run-time. To solve this problem, we describe many-core computing algorithms that harness inexpensive graphics processing units (GPUs) for calculation of the likelihood under continuous-time Markov chain models of evolution. High-end GPUs containing hundreds of cores and are low-cost. Our novel algorithms are particularly efficient for large state-spaces, including codon models, and large data sets, such as full genome alignments where we demonstrate up to 150-fold speed-up.
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