Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Accelerating Wright-Fisher Forward Simulations on the Graphics Processing Unit

David S. Lawrie
doi: https://doi.org/10.1101/042622
David S. Lawrie
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

Abstract

Forward Wright-Fisher simulations are powerful in their ability to model complex demography and selection scenarios, but suffer from slow execution on the CPU, thus limiting their usefulness. The single-locus Wright-Fisher forward algorithm is, however, exceedingly parallelizable, with many steps which are so-called embarrassingly parallel, consisting of a vast number of individual computations that are all independent of each other and thus capable of being performed concurrently. The rise of modern Graphics Processing Units (GPUs) and programming languages designed to leverage the inherent parallel nature of these processors have allowed researchers to dramatically speed up many programs that have such high arithmetic intensity and intrinsic concurrency. The presented GPU Optimized Wright-Fisher simulation, or GO Fish for short, can be used to simulate arbitrary selection and demographic scenarios while running over 340-fold faster than its serial counterpart on the CPU. Even modest GPU hardware can achieve an impressive speedup of well over two orders of magnitude. With simulations so accelerated, one can not only do quick parametric bootstrapping of previously estimated parameters, but also use simulated results to calculate the likelihoods and summary statistics of demographic and selection models against real polymorphism data-all without restricting the demographic and selection scenarios that can be modeled or requiring approximations to the single-locus forward algorithm for efficiency. Further, as many of the parallel programming techniques used in this simulation can be applied to other computationally intensive algorithms important in population genetics, GO Fish serves as an exciting template for future research into accelerating computation in evolution. Code available (soon) at: https://github.com/DL42/GOFish

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
Back to top
PreviousNext
Posted March 07, 2016.
Download PDF

Supplementary Material

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Accelerating Wright-Fisher Forward Simulations on the Graphics Processing Unit
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Accelerating Wright-Fisher Forward Simulations on the Graphics Processing Unit
David S. Lawrie
bioRxiv 042622; doi: https://doi.org/10.1101/042622
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
Accelerating Wright-Fisher Forward Simulations on the Graphics Processing Unit
David S. Lawrie
bioRxiv 042622; doi: https://doi.org/10.1101/042622

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Evolutionary Biology
Subject Areas
All Articles
  • Animal Behavior and Cognition (4113)
  • Biochemistry (8816)
  • Bioengineering (6519)
  • Bioinformatics (23463)
  • Biophysics (11791)
  • Cancer Biology (9209)
  • Cell Biology (13324)
  • Clinical Trials (138)
  • Developmental Biology (7439)
  • Ecology (11410)
  • Epidemiology (2066)
  • Evolutionary Biology (15152)
  • Genetics (10438)
  • Genomics (14044)
  • Immunology (9171)
  • Microbiology (22155)
  • Molecular Biology (8812)
  • Neuroscience (47570)
  • Paleontology (350)
  • Pathology (1428)
  • Pharmacology and Toxicology (2491)
  • Physiology (3730)
  • Plant Biology (8081)
  • Scientific Communication and Education (1437)
  • Synthetic Biology (2221)
  • Systems Biology (6038)
  • Zoology (1253)