High-Performance Computing of BEAST/BEAGLE in Bayesian Phylogenetics using SDumont Hybrid Resources - Ocaña, Kary; Coelho, Micaella; Freire, Guilherme; Osthoff, Carla (Anais do Brazilian e-Science Workshop (BreSci), 2020)
Bayesian phylogenetic algorithms are computationally intensive. BEAST 1.10 inferences made use of the BEAGLE 3 high-performance library for efficient likelihood computations. The strategy allows phylogenetic inference and dating in current knowledge for SARS-CoV-2 transmission. Follow-up simulations on hybrid resources of Santos Dumont supercomputer using four phylogenomic data sets, we characterize the scaling performance behavior of BEAST 1.10. Our results provide insight into the species tree and MCMC chain length estimation, identifying preferable requirements to improve the use of high-performance computing resources. Ongoing steps involve analyzes of SARS-CoV-2 using BEAST 1.8 in multi-GPUs.