Accelerating molecular modeling applications with graphics processors

Authors

  • John E. Stone,

    1. Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, Illinois, 61801
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    • The authors contributed equally

  • James C. Phillips,

    1. Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, Illinois, 61801
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    • The authors contributed equally

  • Peter L. Freddolino,

    1. Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, Illinois, 61801
    2. Center for Biophysics and Computational Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, 61801
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    • The authors contributed equally

  • David J. Hardy,

    1. Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, Illinois, 61801
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    • The authors contributed equally

  • Leonardo G. Trabuco,

    1. Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, Illinois, 61801
    2. Center for Biophysics and Computational Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, 61801
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  • Klaus Schulten

    Corresponding author
    1. Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, Illinois, 61801
    2. Center for Biophysics and Computational Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, 61801
    3. Department of Physics, University of Illinois at Urbana-Champaign, Urbana, Illinois, 61801
    • Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, Illinois, 61801
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Abstract

Molecular mechanics simulations offer a computational approach to study the behavior of biomolecules at atomic detail, but such simulations are limited in size and timescale by the available computing resources. State-of-the-art graphics processing units (GPUs) can perform over 500 billion arithmetic operations per second, a tremendous computational resource that can now be utilized for general purpose computing as a result of recent advances in GPU hardware and software architecture. In this article, an overview of recent advances in programmable GPUs is presented, with an emphasis on their application to molecular mechanics simulations and the programming techniques required to obtain optimal performance in these cases. We demonstrate the use of GPUs for the calculation of long-range electrostatics and nonbonded forces for molecular dynamics simulations, where GPU-based calculations are typically 10–100 times faster than heavily optimized CPU-based implementations. The application of GPU acceleration to biomolecular simulation is also demonstrated through the use of GPU-accelerated Coulomb-based ion placement and calculation of time-averaged potentials from molecular dynamics trajectories. A novel approximation to Coulomb potential calculation, the multilevel summation method, is introduced and compared with direct Coulomb summation. In light of the performance obtained for this set of calculations, future applications of graphics processors to molecular dynamics simulations are discussed. © 2007 Wiley Periodicals, Inc. J Comput Chem, 2007

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