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BEAGLE: An Application Programming Interface and High-Performance Computing Library for Statistical Phylogenetics

Phylogenetic inference is fundamental to our understanding of most aspects of the origin and evolution of life, and in recent years, there has been a concentration of interest in statistical approaches such as Bayesian inference and maximum likelihood estimation. Yet, for large data sets and realist...

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Autores principales: Ayres, Daniel L., Darling, Aaron, Zwickl, Derrick J., Beerli, Peter, Holder, Mark T., Lewis, Paul O., Huelsenbeck, John P., Ronquist, Fredrik, Swofford, David L., Cummings, Michael P., Rambaut, Andrew, Suchard, Marc A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3243739/
https://www.ncbi.nlm.nih.gov/pubmed/21963610
http://dx.doi.org/10.1093/sysbio/syr100
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author Ayres, Daniel L.
Darling, Aaron
Zwickl, Derrick J.
Beerli, Peter
Holder, Mark T.
Lewis, Paul O.
Huelsenbeck, John P.
Ronquist, Fredrik
Swofford, David L.
Cummings, Michael P.
Rambaut, Andrew
Suchard, Marc A.
author_facet Ayres, Daniel L.
Darling, Aaron
Zwickl, Derrick J.
Beerli, Peter
Holder, Mark T.
Lewis, Paul O.
Huelsenbeck, John P.
Ronquist, Fredrik
Swofford, David L.
Cummings, Michael P.
Rambaut, Andrew
Suchard, Marc A.
author_sort Ayres, Daniel L.
collection PubMed
description Phylogenetic inference is fundamental to our understanding of most aspects of the origin and evolution of life, and in recent years, there has been a concentration of interest in statistical approaches such as Bayesian inference and maximum likelihood estimation. Yet, for large data sets and realistic or interesting models of evolution, these approaches remain computationally demanding. High-throughput sequencing can yield data for thousands of taxa, but scaling to such problems using serial computing often necessitates the use of nonstatistical or approximate approaches. The recent emergence of graphics processing units (GPUs) provides an opportunity to leverage their excellent floating-point computational performance to accelerate statistical phylogenetic inference. A specialized library for phylogenetic calculation would allow existing software packages to make more effective use of available computer hardware, including GPUs. Adoption of a common library would also make it easier for other emerging computing architectures, such as field programmable gate arrays, to be used in the future. We present BEAGLE, an application programming interface (API) and library for high-performance statistical phylogenetic inference. The API provides a uniform interface for performing phylogenetic likelihood calculations on a variety of compute hardware platforms. The library includes a set of efficient implementations and can currently exploit hardware including GPUs using NVIDIA CUDA, central processing units (CPUs) with Streaming SIMD Extensions and related processor supplementary instruction sets, and multicore CPUs via OpenMP. To demonstrate the advantages of a common API, we have incorporated the library into several popular phylogenetic software packages. The BEAGLE library is free open source software licensed under the Lesser GPL and available from http://beagle-lib.googlecode.com. An example client program is available as public domain software.
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spelling pubmed-32437392011-12-21 BEAGLE: An Application Programming Interface and High-Performance Computing Library for Statistical Phylogenetics Ayres, Daniel L. Darling, Aaron Zwickl, Derrick J. Beerli, Peter Holder, Mark T. Lewis, Paul O. Huelsenbeck, John P. Ronquist, Fredrik Swofford, David L. Cummings, Michael P. Rambaut, Andrew Suchard, Marc A. Syst Biol Software for Systematics and Evolution Phylogenetic inference is fundamental to our understanding of most aspects of the origin and evolution of life, and in recent years, there has been a concentration of interest in statistical approaches such as Bayesian inference and maximum likelihood estimation. Yet, for large data sets and realistic or interesting models of evolution, these approaches remain computationally demanding. High-throughput sequencing can yield data for thousands of taxa, but scaling to such problems using serial computing often necessitates the use of nonstatistical or approximate approaches. The recent emergence of graphics processing units (GPUs) provides an opportunity to leverage their excellent floating-point computational performance to accelerate statistical phylogenetic inference. A specialized library for phylogenetic calculation would allow existing software packages to make more effective use of available computer hardware, including GPUs. Adoption of a common library would also make it easier for other emerging computing architectures, such as field programmable gate arrays, to be used in the future. We present BEAGLE, an application programming interface (API) and library for high-performance statistical phylogenetic inference. The API provides a uniform interface for performing phylogenetic likelihood calculations on a variety of compute hardware platforms. The library includes a set of efficient implementations and can currently exploit hardware including GPUs using NVIDIA CUDA, central processing units (CPUs) with Streaming SIMD Extensions and related processor supplementary instruction sets, and multicore CPUs via OpenMP. To demonstrate the advantages of a common API, we have incorporated the library into several popular phylogenetic software packages. The BEAGLE library is free open source software licensed under the Lesser GPL and available from http://beagle-lib.googlecode.com. An example client program is available as public domain software. Oxford University Press 2012-01 2011-10-01 /pmc/articles/PMC3243739/ /pubmed/21963610 http://dx.doi.org/10.1093/sysbio/syr100 Text en © The Author(s) 2011. Published by Oxford University Press on behalf of the Society of Systematic Biologists. http://creativecommons.org/licenses/by-nc/3.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Software for Systematics and Evolution
Ayres, Daniel L.
Darling, Aaron
Zwickl, Derrick J.
Beerli, Peter
Holder, Mark T.
Lewis, Paul O.
Huelsenbeck, John P.
Ronquist, Fredrik
Swofford, David L.
Cummings, Michael P.
Rambaut, Andrew
Suchard, Marc A.
BEAGLE: An Application Programming Interface and High-Performance Computing Library for Statistical Phylogenetics
title BEAGLE: An Application Programming Interface and High-Performance Computing Library for Statistical Phylogenetics
title_full BEAGLE: An Application Programming Interface and High-Performance Computing Library for Statistical Phylogenetics
title_fullStr BEAGLE: An Application Programming Interface and High-Performance Computing Library for Statistical Phylogenetics
title_full_unstemmed BEAGLE: An Application Programming Interface and High-Performance Computing Library for Statistical Phylogenetics
title_short BEAGLE: An Application Programming Interface and High-Performance Computing Library for Statistical Phylogenetics
title_sort beagle: an application programming interface and high-performance computing library for statistical phylogenetics
topic Software for Systematics and Evolution
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3243739/
https://www.ncbi.nlm.nih.gov/pubmed/21963610
http://dx.doi.org/10.1093/sysbio/syr100
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