dadi.CUDA: Accelerating Population Genetics Inference with Graphics Processing Units
AuthorGutenkunst, Ryan N
AffiliationDepartment of Molecular and Cellular Biology, University of Arizona
MetadataShow full item record
PublisherOxford University Press
CitationGutenkunst, R. N. (2021). Dadi.CUDA: Accelerating Population Genetics Inference with Graphics Processing Units. Molecular Biology and Evolution, 38(5), 2177–2178.
JournalMolecular Biology and Evolution
Rights© The Author(s) 2021. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/ licenses/by/4.0/).
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at email@example.com.
Abstractdadi is a popular but computationally intensive program for inferring models of demographic history and natural selection from population genetic data. I show that running dadi on a Graphics Processing Unit can dramatically speed computation compared with the CPU implementation, with minimal user burden. Motivated by this speed increase, I also extended dadi to four- and five-population models. This functionality is available in dadi version 2.1.0, https://bitbucket.org/gutenkunstlab/dadi/. © 2021 The Author(s).
NoteOpen access article
VersionFinal published version
Except where otherwise noted, this item's license is described as © The Author(s) 2021. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/ licenses/by/4.0/).
- Accelerating Wright-Fisher Forward Simulations on the Graphics Processing Unit.
- Authors: Lawrie DS
- Issue date: 2017 Sep 7
- High performance computing for deformable image registration: towards a new paradigm in adaptive radiotherapy.
- Authors: Samant SS, Xia J, Muyan-Ozcelik P, Owens JD
- Issue date: 2008 Aug
- Accelerating epistasis analysis in human genetics with consumer graphics hardware.
- Authors: Sinnott-Armstrong NA, Greene CS, Cancare F, Moore JH
- Issue date: 2009 Jul 24
- Fully 3D list-mode time-of-flight PET image reconstruction on GPUs using CUDA.
- Authors: Cui JY, Pratx G, Prevrhal S, Levin CS
- Issue date: 2011 Dec
- Inference of dynamic spatial GRN models with multi-GPU evolutionary computation.
- Authors: Mousavi R, Konuru SH, Lobo D
- Issue date: 2021 Apr 8