The Community for Technology Leaders
RSS Icon
Issue No.01 - Jan.-Feb. (2013 vol.15)
pp: 56-57
Michela Taufer , University of Delaware
Narayan Ganesan , Stevens Institute of Technology
Sandeep Patel , University of Delaware
GPU-enabled simulation of fully atomistic macromolecular systems is rapidly gaining momentum, enabled by massive parallelism and the parallelizability of various components of the underlying algorithms and methodologies. Here, we consider key aspects required for obtaining realistic macromolecular systems specifically adapted to GPUs; these aspects include realistic mathematical models and valid simulations.
Mathematical model, Biological system modeling, Computational modeling, Lattices, Graphics processing unit, Kernel, Adaptation models, scientific computing, GPU computing, molecular dynamics, biomacromolecular structure and function
Michela Taufer, Narayan Ganesan, Sandeep Patel, "GPU-Enabled Macromolecular Simulation: Challenges and Opportunities", Computing in Science & Engineering, vol.15, no. 1, pp. 56-57, Jan.-Feb. 2013, doi:10.1109/MCSE.2012.42
1. J.M. Haile, Molecular Dynamics Simulation: Elementary Methods, Wiley, 1997.
2. T. Darden, D. York, and L. Pedersen, “Particle Mesh Ewald: An N-log (N) Method for Ewald Sums in Large Systems,” J. Chemical Physics, vol. 98, 1993;
3. B.A. Bauer et al., “Molecular Dynamics Simulation of Aqueous Ions at the Liquid-Vapor Interface Accelerated Using Graphics Processors,” J. Computational Chemistry, vol. 32, no. 3, 2011, pp. 375–385.
4. S.L. Grand et al., “Amber 12—Nvidia GPU Acceleration Support,” user guide, Amber, 2012; http://ambermd.orggpus.
5. J.A. Anderson, C.D. Lorenz, and A. Travesset, “General Purpose Molecular Dynamics Simulations Fully Implemented on Graphics Processing Units,” J. Computational Physics, vol. 227, no. 10, 2008, pp. 5342–5359.
6. M. Friedrichs et al., “Accelerating Molecular Dynamic Simulation on Graphics Processing Units,” J. Computational Chemistry, vol. 30, no. 6, 2009, pp. 864–872.
7. M. Harvey, G. Giupponi, and G.D. Fabritiis, “ACEMD: Accelerating Biomolecular Dynamics in the Microsecond Time Scale,” J. Chemical Theory and Computation, vol. 5, no. 6, 2009, pp. 1632–1639.
8. M.P. Allen and D.J. Tildesley, Computer Simulation of Liquids, Clarendon Press, 1987.
9. B.R. Brooks et al., “CHARMM: A Program for Macromolecular Energy, Minimization, and Dynamics Calculations.” J. Computational Chemistry, vol. 4, no. 2, 1983, pp. 187–217.
10. U. Essmann et al., “A Smooth Particle Mesh Ewald Method,” J. Chemical Physics, vol. 103, no. 19, 1995, article no. 8577.
11. R.T. Sanderson, “An Interpretation of Bond Lengths and a Classification of Bonds,” Science, vol. 114, no. 2973, 1951, pp. 670–672.
12. J.P. Ryckaert, G. Ciccotti, and H.J.C. Berendsen, “Numerical Integration of the Cartesian Equations of Motion of a System with Constraints: Molecular Dynamics of n-Alkanes,” J. Computational Physics, vol. 23, no. 3, 1977, pp. 327–341.
13. H.C. Andersen, “Rattle: A Velocity Version of the Shake Algorithm for Molecular Dynamics Calculations,” J. Computational Physics, vol. 52, no. 1, 1983, pp. 24–34.
14. B. Hess et al., “Lincs: A Linear Constraint Solver for Molecular Simulations,” J. Computational Chemistry, vol. 18, no. 12, 1997, pp. 1463–1472.
15. J.B. Klauda et al., “Simulation-Based Methods for Interpreting X-Ray Data from Lipid Bilayers,” Biophysical J., vol. 90, no. 8, 2006, pp. 2796–2807.
16. N. Ganesan et al., “Structural, Dynamic, and Electrostatic Properties of Fully Hydrated DMPC Bilayers from Molecular Dynamics Simulations Accelerated with Graphical Processing Units (GPUs),” J. Computational Chemistry, vol. 32, no. 14, 2011, pp. 2958–2973.
17. J.P. Ulmschneider, M. Andersson, and M.B. Ulmschneider, “Determining Peptide Partitioning Properties via Computer Simulations,” J. Membrane Biology, vol. 239, nos. 1–2, 2011, pp. 15–26.
6 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool