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MDSIMAID: automatic parameter optimization in fast electrostatic algorithms.

Michael S Crocker1, Scott S Hampton, Thierry Matthey

  • 1Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, Indiana 46556, USA.

Journal of Computational Chemistry
|May 11, 2005
PubMed
Summary
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MDSIMAID optimizes electrostatic solvers like Particle Mesh Ewald (PME) and multigrid (MG) methods. This system enhances computational speed and accuracy for scientific simulations, offering significant improvements over existing recommendations.

Area of Science:

  • Computational chemistry
  • Scientific computing
  • Algorithm optimization

Background:

  • Fast electrostatic solvers are crucial for molecular dynamics and materials science simulations.
  • Particle Mesh Ewald (PME) and multigrid (MG) methods are widely used but require careful parameter tuning for optimal performance.
  • Existing parameter recommendations may not achieve the best balance between speed, accuracy, and scalability.

Purpose of the Study:

  • To develop and present MDSIMAID, a recommender system for optimizing PME and MG electrostatic solvers.
  • To improve the running time and parallel scalability of these solvers within defined error tolerances.
  • To provide users with recommended parameters for enhanced computational efficiency.

Main Methods:

  • MDSIMAID utilizes semi-empirical performance models to guide a runtime-constrained search within the parameter spaces of PME and MG methods.

Related Experiment Videos

  • The system optimizes parameters to balance computational cost and accuracy.
  • Recommended parameters are generated and presented to users via a web portal.

    • Main Results:

    • MDSIMAID's optimization of MG solvers resulted in configurations up to 14 times faster or 17 times more accurate than previous recommendations.
    • Optimization of PME solvers demonstrated an improvement in parallel scalability, leading to a twofold speedup in parallel execution.
    • The system provides concrete parameter recommendations for improved solver performance.

    Conclusions:

    • MDSIMAID effectively optimizes fast electrostatic solvers, offering substantial performance gains in both speed and accuracy.
    • The recommender system provides valuable, optimized parameters for PME and MG methods, enhancing computational chemistry and physics simulations.
    • MDSIMAID and its source code are publicly available, facilitating broader adoption and further research in scientific computing.