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Accelerating Wright-Fisher Forward Simulations on the Graphics Processing Unit.

David S Lawrie1

  • 1Los Angeles, California 90034 dlawrie@alumni.stanford.edu.

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Summary
This summary is machine-generated.

GPU Optimized Wright-Fisher simulations (GO Fish) accelerate population genetics modeling by over 250-fold. This enables faster analysis of complex demographic and selection scenarios using graphics processing units (GPUs).

Keywords:
GPUWright–Fisher modelpopulation geneticssimulation

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Area of Science:

  • Computational Biology
  • Population Genetics
  • Evolutionary Biology

Background:

  • Forward Wright-Fisher simulations are essential for modeling complex population genetics scenarios.
  • Traditional CPU-based simulations are computationally intensive and slow, limiting their application.
  • The parallelizable nature of the Wright-Fisher algorithm is well-suited for modern hardware acceleration.

Purpose of the Study:

  • To develop a significantly faster Wright-Fisher simulation algorithm using Graphics Processing Units (GPUs).
  • To enable efficient simulation of arbitrary selection and demographic scenarios.
  • To provide a template for accelerating other computationally intensive population genetics algorithms.

Main Methods:

  • Implementation of the single-locus Wright-Fisher forward algorithm on GPUs.
  • Leveraging the "embarrassingly parallel" nature of the computations for concurrent processing.
  • Development of the "GO Fish" software, part of the Parallel PopGen Package.

Main Results:

  • Achieved over 250-fold speedup compared to serial CPU-based simulations.
  • Demonstrated significant acceleration even on modest GPU hardware (over two orders of magnitude speedup).
  • Enabled rapid parametric bootstrapping and likelihood calculations against polymorphism data.

Conclusions:

  • GO Fish dramatically accelerates Wright-Fisher simulations, making complex evolutionary modeling more accessible.
  • The GPU-accelerated approach removes limitations on modeled scenarios and avoids approximations.
  • GO Fish serves as a foundational tool for future advancements in computational evolutionary biology.