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.