Data and scripts for: Bayesian Phylogenetic Analysis on multi-core Compute Architectures: Implementation and evaluation of BEAGLE in RevBayes with MPI

Phylogenies are central to many research areas in biology and commonly
estimated using likelihood-based methods. Unfortunately, any
likelihood-based method, including Bayesian inference, can be
restrictively slow for large datasets–with many taxa and/or many sites in
the sequence alignment–or complex substitution models. The primary
limiting factor when using large datasets and/or complex models in
probabilistic phylogenetic analyses is the likelihood calculation, which
dominates the total computation time. To address this bottleneck, we
incorporated the high-performance phylogenetic library BEAGLE into
RevBayes, which enables multi-threading on multi-core CPUs and GPUs, as
well as hardware-specific vectorized instructions for faster likelihood
calculations. Our new implementation of RevBayes+BEAGLE retains the
flexibility and dynamic nature that users expect from vanilla RevBayes.
Additionally, we implemented a native parallelization within RevBayes
without an external library using the message passing interface (MPI);
RevBayes+MPI. We evaluated our new implementation of RevBayes+BEAGLE using
multi-threading on CPUs and a powerful NVidia Titan V GPU against our
native implementation of RevBayes+MPI. We found good improvements in
speedup when multiple cores were used with up to 20-fold speedup when
using multiple CPUs and over 90-fold speedup when using multiple GPU
cores. The improvement depended on the data type used, DNA or amino acids,
and the size of the alignment, but less on the size of the tree. We
additionally investigated the cost of rescaling partial likelihoods to
avoid numerical underflow and showed that unnecessarily frequent rescaling
can increase runtimes 2.5 to 3-fold. Finally, we presented and compared a
new approach to store partial likelihoods on branches instead of nodes
which can speed up computations but comes at twice the memory
requirements. Availability: The software described in the paper is
available at https://github.com/revbayes/revbayes with documentation and
tutorials found at https://revbayes.github.io.