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High-Performance Phylogenetic Inference

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Bioinformatics and Phylogenetics

Part of the book series: Computational Biology ((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.

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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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Correspondence to David A. Bader .

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

    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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