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Area of Science:

  • Evolutionary biology
  • Computational biology
  • Bioinformatics

Background:

  • Graphics processing units (GPUs) have revolutionized statistical phylogenetics by accelerating complex computations.
  • Advancements in GPU hardware require new algorithms to maximize performance gains in evolutionary analyses.

Purpose of the Study:

  • To introduce novel algorithms that leverage tensor cores on NVIDIA GPUs for faster phylogenetic inference.
  • To improve the calculation of likelihood and its gradient in evolutionary models.

Main Methods:

  • Developed three new algorithms for accelerating matrix multiplication using GPU tensor cores.
  • Implemented algorithms for continuous-time Markov chain models, focusing on amino acid and codon models.
  • Integrated algorithms into the open-source BEAGLE library (v4.0.0).

Main Results:

  • Achieved 2 to 3-fold performance improvements for amino acid and codon models compared to existing GPU algorithms.
  • Demonstrated a ~2-fold reduction in energy usage, contributing to lower carbon footprint in evolutionary computing.
  • Made algorithms publicly available through BEAGLE v4.0.0.

Conclusions:

  • The novel algorithms significantly enhance the speed and energy efficiency of phylogenetic inference on GPUs.
  • These advancements facilitate deeper insights into pathogen evolution, population dynamics, and ancient genomes.
  • The open-source release of these algorithms supports the broader scientific community in evolutionary computing.