Parallel inference of a 10.000-taxon phylogeny with maximum likelihood Inference of large phylogenetic trees with statistical methods is computationally intensive. We recently introduced simple heuristics which yield accurate trees for synthetic as well as real data and are implemented in a sequential program called RAxML. We have demonstrated that RAxML outperforms the currently fastest statistical phylogeny programs (MrBayes, PHYML) in terms of speed and likelihood values on real data. In this paper we present a non-deterministic parallel implementation of our algorithm which in some cases yields super-linear speedups for an analysis of 1.000 organisms on a LINUX cluster. In addition, we use RAxML to infer a 10.000-taxon phylogenetic tree containing representative organisms from the three domains: Eukarya, Bacteria and Archaea. Finally, we compare the sequential speed and accuracy of RAxML and PHYML on 8 synthetic alignments comprising 4.000 sequences.
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References in zbMATH (referenced in 7 articles )
Showing results 1 to 7 of 7.
- Schrempf, Dominik; Minh, Bui Quang; De Maio, Nicola; von Haeseler, Arndt; Kosiol, Carolin: Reversible polymorphism-aware phylogenetic models and their application to tree inference (2016)
- Lopez, M.Graham; Horton, Mitchel D.: Batch matrix exponentiation (2014)
- Pratas, Frederico; Trancoso, Pedro; Sousa, Leonel; Stamatakis, Alexandros; Shi, Guochun; Kindratenko, Volodymyr: Fine-grain parallelism using multi-core, cell/BE, and GPU systems (2012) ioport
- Brinkmeyer, Malte; Griebel, Thasso; Böcker, Sebastian: Polynomial supertree methods revisited (2011) ioport
- Wehe, André; Chang, Wen-Chieh; Eulenstein, Oliver; Aluru, Srinivas: A scalable parallelization of the gene duplication problem (2010)
- Stamatakis, Alexandros; Ludwig, Thomas; Meier, Harald: RAxML-II: a program for sequential, parallel and distributed inference of large phylogenetic trees (2005) ioport
- Stamatakis, Alexandros; Ludwig, Thomas; Meier, Harald: Parallel inference of a 10.000-taxon phylogeny with maximum likelihood (2004)
Further publications can be found at: http://www.exelixis-lab.org/