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MOIL-opt: Energy-Conserving Molecular Dynamics on a GPU/CPU system.

A Peter Ruymgaart1, Alfredo E Cardenas, Ron Elber

  • 1Institute for Computational Engineering and Sciences, Department of Chemistry and Biochemistry, University of Texas at Austin, Austin Texas 78712.

Journal of Chemical Theory and Computation
|February 14, 2012
PubMed
Summary

An optimized molecular dynamics program, MOIL, now runs efficiently on shared memory systems with Graphics Processing Units (GPUs), achieving high performance and accuracy for long simulations.

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Area of Science:

  • Computational Chemistry
  • Molecular Dynamics Simulations
  • High-Performance Computing

Background:

  • Molecular dynamics (MD) simulations are crucial for understanding biological systems at an atomic level.
  • Long-timescale simulations are essential for capturing complex biological processes but are computationally demanding.
  • Ensuring accuracy and stability, particularly energy conservation, is critical for reliable MD results over extended periods.

Purpose of the Study:

  • To report an optimized version of the MOIL molecular dynamics program for heterogeneous computing systems (CPU/GPU).
  • To enhance computational performance while maintaining high accuracy and stability in long-duration simulations.
  • To investigate methods for minimizing energy drift in explicit-solvent, atomically-detailed models.

Main Methods:

  • Implementation of MOIL on shared memory systems utilizing OpenMP and Graphics Processing Units (GPUs).
  • Employing double precision for SHAKE, Ewald summation, and real-space non-bonded interactions to improve energy conservation.
  • Evaluating different programming models, neighbor list strategies, and interpolation methods (quadratic vs. linear) for CPU/GPU implementations.

Main Results:

  • Achieved undetectable energy drift in 10ns simulations of solvated Dihydrofolate reductase (DHFR) using optimized settings (1fs timestep, all bond constraints, double precision).
  • Demonstrated well-behaved simulations with drifts < 1 kcal/mol/ns using faster options (e.g., constraining only hydrogen-bonded atoms).
  • Reported typical speedups of approximately 10x compared to single-core, single-precision code, with atomic neighbor lists and quadratic interpolation being most efficient.

Conclusions:

  • The optimized MOIL program offers a cost-effective solution for laboratory settings, delivering significant performance gains on heterogeneous CPU/GPU systems.
  • Careful implementation of numerical precision (double precision) and algorithmic choices is key to achieving accurate and stable long-timescale molecular dynamics.
  • The developed methods enable reliable and efficient exploration of complex biological systems through extended molecular dynamics simulations.