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STOCHSIMGPU: parallel stochastic simulation for the Systems Biology Toolbox 2 for MATLAB.

Guido Klingbeil1, Radek Erban, Mike Giles

  • 1Centre for Mathematical Biology, Oxford-Man Institute of Quantitative Finance, University of Oxford, Oxford OX1 3LB, UK. klingbeil@maths.ox.ac.uk

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STOCHSIMGPU software accelerates biological simulations by using graphics processing units (GPUs). This parallel processing significantly speeds up stochastic simulations, offering an 85x improvement over traditional central processing unit (CPU) methods.

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Area of Science:

  • Computational biology
  • Systems biology
  • Bioinformatics

Background:

  • Stochasticity is crucial in biological systems, necessitating efficient simulation software.
  • Existing computational methods for stochastic simulations are often slow.
  • STOCHSIMGPU addresses the need for faster, biologically realistic stochastic simulations.

Purpose of the Study:

  • To introduce STOCHSIMGPU, a novel software tool for accelerating stochastic simulations.
  • To demonstrate the efficiency gains of using graphics processing units (GPUs) for these simulations.
  • To provide an open-source, integrated solution for MATLAB users.

Main Methods:

  • Developed STOCHSIMGPU, a software tool leveraging GPUs for parallel processing.
  • Implemented the Gillespie stochastic simulation algorithm (SSA), logarithmic direct method (LDM), and next reaction method (NRM) on GPUs.
  • Integrated STOCHSIMGPU with MATLAB and Systems Biology Toolbox 2 (SBTOOLBOX2).

Main Results:

  • Achieved an approximately 85-fold speedup compared to sequential central processing unit (CPU) implementations.
  • Demonstrated significant efficiency gains in parallel stochastic simulations.
  • Ensured seamless integration by acting as a direct replacement for existing SBTOOLBOX2 stochastic simulation software.

Conclusions:

  • STOCHSIMGPU offers substantial performance improvements for stochastic simulations.
  • The software requires no model modifications, facilitating easy adoption.
  • Open-source availability promotes wider use and development in computational biology.