Data and code from: Tensor cores unlock efficient and lower-energy massive parallelization on phylogenetic trees

Massively parallel algorithms leveraging graphics processing units (GPUs)
have significantly accelerated inference in statistical phylogenetics,
with applications in understanding pathogen evolution, population
dynamics, natural selection, and evolutionary timescales using ancient
genomes. Continued advancements in GPU hardware necessitate innovative
algorithms to fully exploit their potential. Here, we introduce
three novel algorithms that accelerate matrix multiplication operations
using tensor cores on NVIDIA GPUs to calculate the observed sequence data
likelihood and the gradient of the log-likelihood with respect to
branch-length-specific parameters under continuous-time Markov chain
models of evolution. The algorithms presented in this paper
deliver 2 to 3-fold gains in performance for amino acid and codon models
compared to existing GPU-based massively parallel
algorithms. Notably, these performance gains are accompanied by a
~2-fold reduction in energy usage, demonstrating the potential of these
algorithms to lower the carbon footprint of evolutionary computing. We
make our new algorithms available to the broader phylogenetics community
through the high-performance, open source library BEAGLE v4.0.0.