Speeding up ecological and evolutionary computations in R; essentials of high performance computing for biologists.
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 revi...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2015-03-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1004140 |
| _version_ | 1818735071638585344 |
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| author | Marco D Visser Sean M McMahon Cory Merow Philip M Dixon Sydne Record Eelke Jongejans |
| author_facet | Marco D Visser Sean M McMahon Cory Merow Philip M Dixon Sydne Record Eelke Jongejans |
| author_sort | Marco D Visser |
| collection | DOAJ |
| description | 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 (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. |
| first_indexed | 2024-12-18T00:15:26Z |
| format | Article |
| id | doaj.art-a54439ee3ca840539244820b83aacd8b |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-18T00:15:26Z |
| publishDate | 2015-03-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-a54439ee3ca840539244820b83aacd8b2022-12-21T21:27:32ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582015-03-01113e100414010.1371/journal.pcbi.1004140Speeding up ecological and evolutionary computations in R; essentials of high performance computing for biologists.Marco D VisserSean M McMahonCory MerowPhilip M DixonSydne RecordEelke JongejansComputation 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.https://doi.org/10.1371/journal.pcbi.1004140 |
| spellingShingle | Marco D Visser Sean M McMahon Cory Merow Philip M Dixon Sydne Record Eelke Jongejans Speeding up ecological and evolutionary computations in R; essentials of high performance computing for biologists. PLoS Computational Biology |
| title | Speeding up ecological and evolutionary computations in R; essentials of high performance computing for biologists. |
| title_full | Speeding up ecological and evolutionary computations in R; essentials of high performance computing for biologists. |
| title_fullStr | Speeding up ecological and evolutionary computations in R; essentials of high performance computing for biologists. |
| title_full_unstemmed | Speeding up ecological and evolutionary computations in R; essentials of high performance computing for biologists. |
| title_short | Speeding up ecological and evolutionary computations in R; essentials of high performance computing for biologists. |
| title_sort | speeding up ecological and evolutionary computations in r essentials of high performance computing for biologists |
| url | https://doi.org/10.1371/journal.pcbi.1004140 |
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