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Bioinformatics and Phylogenetics pp 39–45Cite as

High-Performance Phylogenetic Inference

Part of the Computational Biology book series (COBO,volume 29)

Abstract

Software tools based on the maximum likelihood method and Bayesian methods are widely used for phylogenetic tree inference. This article surveys recent research on parallelization and performance optimization of state-of-the-art tree inference tools. We outline advances in shared-memory multicore parallelization, optimizations for efficient Graphics Processing Unit (GPU) execution, as well as large-scale distributed-memory parallelization.

Keywords

  • Phylogenetic tree inference
  • Maximum likelihood
  • Bayesian inference
  • Parallel algorithms
  • Algorithm engineering

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Acknowledgements

This work is supported in part by the National Science Foundation awards #1339745, #1439057, and #1535058.

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Authors and Affiliations

  1. Georgia Institute of Technology, Atlanta, GA, USA

    David A. Bader

  2. Pennsylvania State University, University Park, PA, USA

    Kamesh Madduri

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  1. David A. Bader
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  2. Kamesh Madduri
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Correspondence to David A. Bader .

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  1. University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Prof. Tandy Warnow

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Bader, D.A., Madduri, K. (2019). High-Performance Phylogenetic Inference. In: Warnow, T. (eds) Bioinformatics and Phylogenetics. Computational Biology, vol 29. Springer, Cham. https://doi.org/10.1007/978-3-030-10837-3_3

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  • DOI: https://doi.org/10.1007/978-3-030-10837-3_3

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