ArticlePloS one2015
gPGA: GPU Accelerated Population Genetics Analyses.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- dadi.CUDA: Accelerating Population Genetics Inference with Graphics Processing Units.Molecular biology and evolution · 2021Article
- Accelerating Wright-Fisher Forward Simulations on the Graphics Processing Unit.G3 (Bethesda, Md.) · 2017Article
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Authors and funding
4 authors.
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Abstract
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. 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.
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.
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