Abstract
Due to immense computational requirements Phylogenetic inference is considered to be a grand challenge in Bioinformatics. The increasing popularity of multi-gene alignments in biological studies, which typically provide a stable topological signal due to a more favorable ratio of the number of base pairs to the number of sequences, coupled with rapid accumulation of sequence data in general, poses new challenges for high performance computing. In this paper, we present a parallelization strategy for RAxML, which is currently among the fastest and most accurate programs for phylogenetic inference under the ML criterion. We simultaneously exploit coarse-grained and fine-grained parallelism that is inherent in every ML-based biological analysis. Our experimental results indicate that our approach scales very well on supercomputer architectures like the IBM BlueGene/L or SGI Altix, as well as on common Linux clusters with high-speed interconnects.
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Ott, M., Zola, J., Aluru, S., Stamatakis, A. (2009). ParBaum: Large-Scale Maximum Likelihood-Based Phylogenetic Analyses. In: Wagner, S., Steinmetz, M., Bode, A., Brehm, M. (eds) High Performance Computing in Science and Engineering, Garching/Munich 2007. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69182-2_9
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DOI: https://doi.org/10.1007/978-3-540-69182-2_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69181-5
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