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ParBaum: Large-Scale Maximum Likelihood-Based Phylogenetic Analyses

In: High Performance Computing in Science and Engineering, Garching/Munich 2007

Author

Listed:
  • Michael Ott

    (Technical University of Munich, Department of Computer Science)

  • Jaroslaw Zola

    (Iowa State University, Department of Electrical and Computer Engineering)

  • Srinivas Aluru

    (Iowa State University, Department of Electrical and Computer Engineering)

  • Alexandros Stamatakis

    (School of Computer and Communication Sciences and Swiss Institute of Bioinformatics, École Polytechnique Fédérale de Lausanne)

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.

Suggested Citation

  • Michael Ott & Jaroslaw Zola & Srinivas Aluru & Alexandros Stamatakis, 2009. "ParBaum: Large-Scale Maximum Likelihood-Based Phylogenetic Analyses," Springer Books, in: Siegfried Wagner & Matthias Steinmetz & Arndt Bode & Matthias Brehm (ed.), High Performance Computing in Science and Engineering, Garching/Munich 2007, pages 111-125, Springer.
    • Handle: RePEc:spr:sprchp:978-3-540-69182-2_9
    DOI: 10.1007/978-3-540-69182-2_9
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