Speeding Up Ecological and Evolutionary Computations in R; Essentials of High Performance Computing for Biologists

dc.contributor.author Visser, Marco
dc.contributor.author McMahon, Sean
dc.contributor.author Dixon, Philip
dc.contributor.author Merow, Cory
dc.contributor.author Dixon, Philip
dc.contributor.author Record, Sydne
dc.contributor.author Jongejans, Eelke
dc.contributor.department Statistics
dc.date 2018-02-17T11:30:20.000
dc.date.accessioned 2020-07-02T06:57:55Z
dc.date.available 2020-07-02T06:57:55Z
dc.date.issued 2015-01-01
dc.description.abstract <p>Computation has become a critical component of research in biology. A risk has emerged that computational and programming challenges may limit research scope, depth, and quality. We review various solutions to common computational efficiency problems in ecological and evolutionary research. Our review pulls together material that is currently scattered across many sources and emphasizes those techniques that are especially effective for typical ecological and environmental problems. We demonstrate how straightforward it can be to write efficient code and implement techniques such as profiling or parallel computing. We supply a newly developed R package (<em>aprof</em>) that helps to identify computational bottlenecks in R code and determine whether optimization can be effective. Our review is complemented by a practical set of examples and detailed Supporting Information material (<a href="http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004140#pcbi.1004140.s001">S1</a>–<a href="http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004140#pcbi.1004140.s003">S3</a> Texts) that demonstrate large improvements in computational speed (ranging from 10.5 times to 14,000 times faster). By improving computational efficiency, biologists can feasibly solve more complex tasks, ask more ambitious questions, and include more sophisticated analyses in their research.</p>
dc.description.comments <p>This is an article from <em>PLoS Computational Biology</em> 11 (2015):1, doi:<a href="http://dx.doi.org/10.1371/journal.pcbi.1004140" target="_blank">10.1371/journal.pcbi.1004140</a>. Posted with permission.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/stat_las_pubs/54/
dc.identifier.articleid 1048
dc.identifier.contextkey 8055330
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath stat_las_pubs/54
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/90653
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/stat_las_pubs/54/2015_Dixon_SpeedingUpEcological.pdf|||Sat Jan 15 00:53:00 UTC 2022
dc.source.uri 10.1371/journal.pcbi.1004140
dc.subject.disciplines Ecology and Evolutionary Biology
dc.subject.disciplines Statistics and Probability
dc.title Speeding Up Ecological and Evolutionary Computations in R; Essentials of High Performance Computing for Biologists
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isAuthorOfPublication 7b3eb8d2-a569-4aba-87a1-5d9c2d99fade
relation.isOrgUnitOfPublication 264904d9-9e66-4169-8e11-034e537ddbca
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