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Lagrange-NG: The next generation of Lagrange

Bettisworth, Ben; Smith, Stephen A.; Stamatakis, Alexandros 1
1 Institut für Theoretische Informatik (ITI), Karlsruher Institut für Technologie (KIT)

Abstract:

Computing ancestral ranges via the Dispersion Extinction and Cladogensis (DEC) model of biogeography is characterized by an exponential number of states relative to the number of regions considered. This is because the DEC model requires computing a large matrix exponential, which typically accounts for up to 80% of overall runtime. Therefore, the kinds of biogeographical analyses that can be conducted under the DEC model are limited by the number of regions under consideration. In this work, we present a completely redesigned efficient version of the popular tool Lagrange which is up to 49 times faster with multithreading enabled, and is also 26 times faster when using only one thread. We call this new version Lagrange-NG (Lagrange-Next Generation). The increased computational efficiency allows Lagrange-NG to analyze datasets with a large number of regions in a reasonable amount of time, up to 12 regions in approximately 18 min. We achieve these speedups using a relatively new method of computing the matrix exponential based on Krylov subspaces. In order to validate the correctness of Lagrange-NG, we also introduce a novel metric on range distributions for trees so that researchers can assess the difference between any two range inferences. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000160413
Veröffentlicht am 11.07.2023
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Theoretische Informatik (ITI)
Publikationstyp Zeitschriftenaufsatz
Publikationsdatum 19.05.2023
Sprache Englisch
Identifikator ISSN: 1063-5157, 1076-836X
KITopen-ID: 1000160413
Erschienen in Systematic Biology
Verlag Oxford University Press (OUP)
Band 72
Heft 1
Seiten 242–248
Vorab online veröffentlicht am 27.01.2023
Nachgewiesen in Web of Science
Dimensions
Scopus
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