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dc.contributor.authorAyres, Daniel L.
dc.contributor.authorDarling, Aaron
dc.contributor.authorZwickl, Derrick J.
dc.contributor.authorBeerli, Peter
dc.contributor.authorHolder, Mark T.
dc.contributor.authorLewis, Paul O.
dc.contributor.authorHuelsenbeck, John P.
dc.contributor.authorRonquist, Fredrik
dc.contributor.authorSwofford, David L.
dc.contributor.authorCummings, Michael P.
dc.contributor.authorRambaut, Andrew
dc.date.accessioned2014-04-15T13:54:23Z
dc.date.available2014-04-15T13:54:23Z
dc.date.issued2011-10-01
dc.identifier.citationAyres, Daniel L, Aaron Darling, Derrick J Zwickl, Peter Beerli, Mark T Holder, Paul O Lewis, John P Huelsenbeck, et al. 2012. “BEAGLE: An Application Programming Interface and High-Performance Computing Library for Statistical Phylogenetics.” Systematic Biology 61 (1): 170–73. http://dx.doi.org/10.1093/sysbio/syr100
dc.identifier.urihttp://hdl.handle.net/1808/13485
dc.description.abstractPhylogenetic 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.
dc.description.sponsorshipThis work was supported by the National Science Foundation [grant numbers DBI-0755048, DEB-0732920, DEB-1036448, DMS-0931642, EF-0331495, EF-0905606, EF-0949453]; the National Institutes of Health [grant numbers R01-HG006139, R01-GM037841, R01-GM078985, R01-GM086887, R01-NS063897]; the Biotechnology and Biological Sciences Research Council [grant number BB/H011285/1]; the Wellcome Trust [grant number WT092807MA]; and Google Summer of Code.
dc.publisherOxford University Press
dc.rightsThis 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.
dc.rights.urihttp://creativecommons.org/licenses/by-nc/3.0
dc.subjectBayesian phylogenetics
dc.subjectGPU
dc.subjectmaximum likelihood
dc.subjectparallel computing
dc.titleBEAGLE: An Application Programming Interface and High-Performance Computing Library for Statistical Phylogenetics
dc.typeArticle
kusw.kuauthorZwickl, Derrick J.
kusw.kuauthorHolder, Mark T.
kusw.kudepartmentDepartment of Ecology and Evolutionary Biology
kusw.oastatusfullparticipation
dc.identifier.doi10.1093/sysbio/syr100
kusw.oaversionScholarly/refereed, publisher version
kusw.oapolicyThis item meets KU Open Access policy criteria.
dc.rights.accessrightsopenAccess


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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.
Except where otherwise noted, this item's license is described as: 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.