Hübner, L. | bioRxiv Science

bioRxiv2022

The Free Lunch is not over yet - Systematic Exploration of Numerical Thresholds in Phylogenetic Inference

Maximum Likelihood (ML) is a widely used phylogenetic inference model. ML implementations heavily rely on numerical optimization routines that use internal numerical thresholds to determine convergence. We systematically analyze the impact of these threshold settings on the log-likelihood and runtimes for ML tree inferences with RAxML-NG, IQ-TREE, and FastTree on empirical datasets. We provide empirical evidence that we can substantially accelerate tree inferences with RAxML-NG and IQ-TREE by changing the default values of two such numerical thresholds. At the same time, altering these settings does not significantly impact the quality of the inferred trees. We further show that increasing both thresholds accelerates the RAxML-NG bootstrap without influencing the resulting support values. For RAxML-NG, increasing the likelihood thresholds{epsilon} LnL and{epsilon} brlen to 10 and 103 respectively results in an average tree inference speedup of 1.9 {+/-} 0.6 on Data collection 1, 1.8 {+/-} 1.1 on Data collection 2, and 1.9 {+/-} 0.8 on Data collection 2 for the RAxML-NG bootstrap. Increasing the likelihood threshold{epsilon} LnL to 10 in IQ-TREE results in an average tree inference speedup of 1.3 {+/-} 0.4 on Data collection 1 and 1.3 {+/-} 0.9 on Data collection 2.

bioinformatics↗

bioRxiv2021

Exploring Parallel MPI Fault Tolerance Mechanisms for Phylogenetic Inference with RAxML-NG

Phylogenetic trees are now routinely inferred on large scale HPC systems with thousands of cores as the parallel scalability of phylogenetic inference tools has improved over the past years to cope with the molecular data avalanche. Thus, the parallel fault tolerance of phylogenetic inference tools has become a relevant challenge. To this end, we explore parallel fault tolerance mechanisms and algorithms, the software modifications required, and the performance penalties induced via enabling parallel fault tolerance by example of RAxML-NG, the successor of the widely used RAxML tool for maximum likelihood based phylogenetic tree inference. We find that the slowdown induced by the necessary additional recovery mechanisms in RAxML-NG is on average 2%. The overall slowdown by using these recovery mechanisms in conjunction with a fault tolerant MPI implementation amounts to 8% on average for large empirical datasets. Via failure simulations, we show that RAxML-NG can successfully recover from multiple simultaneous failures, subsequent failures, failures during recovery, and failures during checkpointing. Recoveries are automatic and transparent to the user. The modified fault tolerant RAxML-NG code is available under GNU GPL at https://github.com/lukashuebner/ft-raxml-ng Contact: lukas.huebner@{kit.edu,h-its.org};, alexey.kozlov@h-its.org, hespe@kit.edu, sanders@kit.edu, alexandros.stamatakis@hits.org Supplementary information: Supplementary data are available at bioR{chi}iv.

bioinformatics↗