KIT | KIT-Bibliothek | Impressum | Datenschutz

Exploring parallel MPI fault tolerance mechanisms for phylogenetic inference with RAxML-NG

Hübner, Lukas; Kozlov, Alexey M.; Hespe, Demian; Sanders, Peter; Stamatakis, Alexandros


Phylgenetic trees are now routinely inferred on large scale high performance computing 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 1.00 ± 0.04. The overall slowdown by using these recovery mechanisms in conjunction with a fault-tolerant Message Passing Interface implementation amounts to on average 1.7 ± 0.6 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. ... mehr

Verlagsausgabe §
DOI: 10.5445/IR/1000138066
Veröffentlicht am 29.09.2021
DOI: 10.1093/bioinformatics/btab399
Zitationen: 1
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Theoretische Informatik (ITI)
Publikationstyp Zeitschriftenaufsatz
Publikationsdatum 15.11.2021
Sprache Englisch
Identifikator ISSN: 1367-4803, 1460-2059
KITopen-ID: 1000138066
HGF-Programm 46.21.02 (POF IV, LK 01) Cross-Domain ATMLs and Research Groups
Erschienen in Bioinformatics
Verlag Oxford University Press (OUP)
Band 37
Heft 22
Seiten 4056–4063
Nachgewiesen in Dimensions
KIT – Die Forschungsuniversität in der Helmholtz-Gemeinschaft
KITopen Landing Page