gPGA: GPU Accelerated Population Genetics Analyses
The isolation with migration (IM) model is important for studies in population genetics and phylogeography. IM program applies the IM model to genetic data drawn from a pair of closely related populations or species based on Markov chain Monte Carlo (MCMC) simulations of gene genealogies. But comput...
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| Published in | PloS one Vol. 10; no. 8; p. e0135028 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
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Public Library of Science
06.08.2015
Public Library of Science (PLoS) |
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| Abstract | The isolation with migration (IM) model is important for studies in population genetics and phylogeography. IM program applies the IM model to genetic data drawn from a pair of closely related populations or species based on Markov chain Monte Carlo (MCMC) simulations of gene genealogies. But computational burden of IM program has placed limits on its application.
With strong computational power, Graphics Processing Unit (GPU) has been widely used in many fields. In this article, we present an effective implementation of IM program on one GPU based on Compute Unified Device Architecture (CUDA), which we call gPGA.
Compared with IM program, gPGA can achieve up to 52.30X speedup on one GPU. The evaluation results demonstrate that it allows datasets to be analyzed effectively and rapidly for research on divergence population genetics. The software is freely available with source code at https://github.com/chunbaozhou/gPGA. |
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| AbstractList | The isolation with migration (IM) model is important for studies in population genetics and phylogeography. IM program applies the IM model to genetic data drawn from a pair of closely related populations or species based on Markov chain Monte Carlo (MCMC) simulations of gene genealogies. But computational burden of IM program has placed limits on its application. With strong computational power, Graphics Processing Unit (GPU) has been widely used in many fields. In this article, we present an effective implementation of IM program on one GPU based on Compute Unified Device Architecture (CUDA), which we call gPGA. Compared with IM program, gPGA can achieve up to 52.30X speedup on one GPU. The evaluation results demonstrate that it allows datasets to be analyzed effectively and rapidly for research on divergence population genetics. The software is freely available with source code at https://github.com/chunbaozhou/gPGA. The isolation with migration (IM) model is important for studies in population genetics and phylogeography. IM program applies the IM model to genetic data drawn from a pair of closely related populations or species based on Markov chain Monte Carlo (MCMC) simulations of gene genealogies. But computational burden of IM program has placed limits on its application. With strong computational power, Graphics Processing Unit (GPU) has been widely used in many fields. In this article, we present an effective implementation of IM program on one GPU based on Compute Unified Device Architecture (CUDA), which we call gPGA. Compared with IM program, gPGA can achieve up to 52.30X speedup on one GPU. The evaluation results demonstrate that it allows datasets to be analyzed effectively and rapidly for research on divergence population genetics. The software is freely available with source code at https://github.com/chunbaozhou/gPGA. Background The isolation with migration (IM) model is important for studies in population genetics and phylogeography. IM program applies the IM model to genetic data drawn from a pair of closely related populations or species based on Markov chain Monte Carlo (MCMC) simulations of gene genealogies. But computational burden of IM program has placed limits on its application. Methodology With strong computational power, Graphics Processing Unit (GPU) has been widely used in many fields. In this article, we present an effective implementation of IM program on one GPU based on Compute Unified Device Architecture (CUDA), which we call gPGA. Conclusions Compared with IM program, gPGA can achieve up to 52.30X speedup on one GPU. The evaluation results demonstrate that it allows datasets to be analyzed effectively and rapidly for research on divergence population genetics. The software is freely available with source code at Background The isolation with migration (IM) model is important for studies in population genetics and phylogeography. IM program applies the IM model to genetic data drawn from a pair of closely related populations or species based on Markov chain Monte Carlo (MCMC) simulations of gene genealogies. But computational burden of IM program has placed limits on its application. Methodology With strong computational power, Graphics Processing Unit (GPU) has been widely used in many fields. In this article, we present an effective implementation of IM program on one GPU based on Compute Unified Device Architecture (CUDA), which we call gPGA. Conclusions Compared with IM program, gPGA can achieve up to 52.30X speedup on one GPU. The evaluation results demonstrate that it allows datasets to be analyzed effectively and rapidly for research on divergence population genetics. The software is freely available with source code at https://github.com/chunbaozhou/gPGA. The isolation with migration (IM) model is important for studies in population genetics and phylogeography. IM program applies the IM model to genetic data drawn from a pair of closely related populations or species based on Markov chain Monte Carlo (MCMC) simulations of gene genealogies. But computational burden of IM program has placed limits on its application.BACKGROUNDThe isolation with migration (IM) model is important for studies in population genetics and phylogeography. IM program applies the IM model to genetic data drawn from a pair of closely related populations or species based on Markov chain Monte Carlo (MCMC) simulations of gene genealogies. But computational burden of IM program has placed limits on its application.With strong computational power, Graphics Processing Unit (GPU) has been widely used in many fields. In this article, we present an effective implementation of IM program on one GPU based on Compute Unified Device Architecture (CUDA), which we call gPGA.METHODOLOGYWith strong computational power, Graphics Processing Unit (GPU) has been widely used in many fields. In this article, we present an effective implementation of IM program on one GPU based on Compute Unified Device Architecture (CUDA), which we call gPGA.Compared with IM program, gPGA can achieve up to 52.30X speedup on one GPU. The evaluation results demonstrate that it allows datasets to be analyzed effectively and rapidly for research on divergence population genetics. The software is freely available with source code at https://github.com/chunbaozhou/gPGA.CONCLUSIONSCompared with IM program, gPGA can achieve up to 52.30X speedup on one GPU. The evaluation results demonstrate that it allows datasets to be analyzed effectively and rapidly for research on divergence population genetics. The software is freely available with source code at https://github.com/chunbaozhou/gPGA. Background The isolation with migration (IM) model is important for studies in population genetics and phylogeography. IM program applies the IM model to genetic data drawn from a pair of closely related populations or species based on Markov chain Monte Carlo (MCMC) simulations of gene genealogies. But computational burden of IM program has placed limits on its application. Methodology With strong computational power, Graphics Processing Unit (GPU) has been widely used in many fields. In this article, we present an effective implementation of IM program on one GPU based on Compute Unified Device Architecture (CUDA), which we call gPGA. Conclusions Compared with IM program, gPGA can achieve up to 52.30X speedup on one GPU. The evaluation results demonstrate that it allows datasets to be analyzed effectively and rapidly for research on divergence population genetics. The software is freely available with source code at https://github.com/chunbaozhou/gPGA. |
| Audience | Academic |
| Author | Lang, Xianyu Zhu, Chaodong Wang, Yangang Zhou, Chunbao |
| AuthorAffiliation | 1 Supercomputing Center, Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100190, China Pennsylvania State University, UNITED STATES 2 Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China |
| AuthorAffiliation_xml | – name: Pennsylvania State University, UNITED STATES – name: 2 Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China – name: 1 Supercomputing Center, Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100190, China |
| Author_xml | – sequence: 1 givenname: Chunbao surname: Zhou fullname: Zhou, Chunbao – sequence: 2 givenname: Xianyu surname: Lang fullname: Lang, Xianyu – sequence: 3 givenname: Yangang surname: Wang fullname: Wang, Yangang – sequence: 4 givenname: Chaodong surname: Zhu fullname: Zhu, Chaodong |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/26248314$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1534_g3_117_300103 crossref_primary_10_1016_j_ecoinf_2024_102486 crossref_primary_10_1093_molbev_msaa305 |
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| Copyright | COPYRIGHT 2015 Public Library of Science 2015 Zhou et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2015 Zhou et al 2015 Zhou et al |
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| DOI | 10.1371/journal.pone.0135028 |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Conceived and designed the experiments: CBZ XYL YGW CDZ. Performed the experiments: CBZ YGW. Analyzed the data: CBZ XYL. Wrote the paper: CBZ XYL YGW CDZ. Competing Interests: The authors have declared that no competing interests exist. |
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| Snippet | The isolation with migration (IM) model is important for studies in population genetics and phylogeography. IM program applies the IM model to genetic data... Background The isolation with migration (IM) model is important for studies in population genetics and phylogeography. IM program applies the IM model to... Background The isolation with migration (IM) model is important for studies in population genetics and phylogeography. IM program applies the IM model to... |
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| SubjectTerms | Analysis Computation Computer applications Computer Graphics Computer simulation Data processing Divergence Genetics Genetics, Population - statistics & numerical data Graphics processing units Humans Markov analysis Markov Chains Markov processes Migration Monte Carlo Method Monte Carlo methods Monte Carlo simulation Mutation Phylogenetics Population genetics Population studies Software |
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| Title | gPGA: GPU Accelerated Population Genetics Analyses |
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