Speeding Up Ecological and Evolutionary Computations in R; Essentials of High Performance Computing for BiologistsPLoS Computational Biology
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AbstractComputation 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 (aprof) 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 (S1–S3 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.
RightsThis is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
Citation InformationMarco D. Visser, Sean M. McMahon, Cory Merow, Philip M. Dixon, et al.. "Speeding Up Ecological and Evolutionary Computations in R; Essentials of High Performance Computing for Biologists" PLoS Computational Biology Vol. 11 Iss. 3 (2015) p. 1 - 11
Available at: http://works.bepress.com/philip-dixon/30/