On-Board Multi-GPU Molecular Dynamics

  • Marcos Novalbos
  • Jaime Gonzalez
  • Miguel Angel Otaduy
  • Alvaro Lopez-Medrano
  • Alberto Sanchez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8097)


Molecular dynamics simulations allow us to study the behavior of complex biomolecular systems. These simulations suffer a large computational complexity that leads to simulation times of several weeks in order to recreate just a few microseconds of a molecule’s motion even on high-performance computing platforms. In recent years, state-of-the-art molecular dynamics algorithms have benefited from the parallel computing capabilities of multicore systems, as well as GPUs used as co-processors. In this paper we present a parallel molecular dynamics algorithm for on-board multi-GPU architectures. We parallelize a state-of-the-art molecular dynamics algorithm at two levels. We employ a spatial partitioning approach to simulate the dynamics of one portion of a molecular system on each GPU, and we take advantage of direct communication between GPUs to transfer data among portions. We also parallelize the simulation algorithm to exploit the multi-processor computing model of GPUs. Most importantly, we present novel parallel algorithms to update the spatial partitioning and set up transfer data packages on each GPU. We demonstrate the feasibility and scalability of our proposal through a comparative study with NAMD, a well known parallel molecular dynamics implementation.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Barth, E., Schlick, T.: Extrapolation versus impulse in multiple-timestepping schemes. II. Linear analysis and applications to Newtonian and Langevin dynamics. Chemical Physics 109, 1633–1642 (1998)Google Scholar
  2. 2.
    Clark, T.W., McCammon, J.A.: Parallelization of a molecular dynamics non-bonded force algorithm for MIMD architecture. Computers & Chemistry, 219–224 (1990)Google Scholar
  3. 3.
    Grubmüller, H.: Dynamiksimulation sehr großer Makromoleküle auf einem Parallelrechner. Diplomarbeit, Technische Universität München, Physik-Department, T 30, James-Franck-Straße, 8046 Garching (1989)Google Scholar
  4. 4.
    Harvey, M.J., Giupponi, G., Fabritiis, G.D.: ACEMD: Accelerating Biomolecular Dynamics in the Microsecond Time Scale. Journal of Chemical Theory and Computation 5(6), 1632–1639 (2009)CrossRefGoogle Scholar
  5. 5.
    Hess, B., Kutzner, C., van der Spoel, D., Lindahl, E.: GROMACS 4: Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular Simulation. Journal of Chemical Theory and Computation 4(3), 435–447 (2008)CrossRefGoogle Scholar
  6. 6.
    Hwang, Y.S., Das, R., Saltz, J., Brooks, B., Scek, M.H.: Parallelizing molecular dynamics programs for distributed memory machines: An application of the chaos runtime support library (1994)Google Scholar
  7. 7.
  8. 8.
    Izaguirre, J.A., Matthey, T., Willcock, J., Ma, Q., Moore, B., Slabach, T., Viamontes, G.: A tutorial on the prototyping of multiple time stepping integrators for molecular dynamics (2001)Google Scholar
  9. 9.
    Kalé, L., Skeel, R., Bhandarkar, M., Brunner, R., Gursoy, A., Krawetz, N., Phillips, J., Shinozaki, A., Varadarajan, K., Schulten, K.: NAMD2: Greater scalability for parallel molecular dynamics. Journal of Computational Physics 151(1), 283–312 (1999)MATHCrossRefGoogle Scholar
  10. 10.
    Koehler, A.: Scalable Cluster Computing with NVIDIA GPUs (2012), http://www.hpcadvisorycouncil.com/events/2012/Switzerland-Workshop/Presentations/Day_3/3_NVIDIA.pdf
  11. 11.
    Kupka, S.: Molecular dynamics on graphics accelerators (2006)Google Scholar
  12. 12.
    van Meel, J., Arnold, A., Frenkel, D., Portegies Zwart, S., Belleman, R.: Harvesting graphics power for md simulations. Molecular Simulation 34(3), 259–266 (2008)CrossRefGoogle Scholar
  13. 13.
    NAMD on Biowulf GPU nodes, http://biowulf.nih.gov/apps/namd-gpu.html (accessed February 2013)
  14. 14.
    Plimpton, S.: Fast parallel algorithms for short-range molecular dynamics. Journal of Computational Physics 117, 1–19 (1995)MATHCrossRefGoogle Scholar
  15. 15.
    Podlozhnyuk, V.: CUDA Samples:: CUDA Toolkit documentation - NVidia’s GPU Merge-sort implementations, http://docs.nvidia.com/cuda/cuda-samples/index.html (accessed February 2013)
  16. 16.
    Rapaport, D.: Large-scale Molecular Dynamics Simulation Using Vector and Parallel Computers. North-Holland (1988)Google Scholar
  17. 17.
    Rodrigues, C.I., Hardy, D.J., Stone, J.E., Schulten, K., Hwu, W.M.W.: GPU acceleration of cutoff pair potentials for molecular modeling applications. In: Proceedings of the 5th Conference on Computing Frontiers, CF 2008, pp. 273–282 (2008)Google Scholar
  18. 18.
    Rustico, E., Bilotta, G., Gallo, G., Herault, A., Negro, C.D.: Smoothed particle hydrodynamics simulations on multi-GPU systems. In: Euromicro International Conference on Parallel, Distributed and Network-Based Processing (2012)Google Scholar
  19. 19.
    Sanz-Serna, J.M.: Mollified impulse methods for highly oscillatory differential equations. SIAM J. Numer. Anal. 46(2), 1040–1059 (2008)MathSciNetMATHCrossRefGoogle Scholar
  20. 20.
    Schlick, T.: Molecular Modeling and Simulation: An Interdisciplinary Guide. Springer-Verlag New York, Inc., Secaucus (2002)CrossRefGoogle Scholar
  21. 21.
    Schroeder, T.C.: Peer-to-Peer & Unified Virtual Addressing (2011), http://developer.download.nvidia.com/CUDA/training/cuda_webinars_GPUDirect_uva.pdf
  22. 22.
    Stone, J.E., Phillips, J.C., Freddolino, P.L., Hardy, D.J., Trabuco, L.G., Schulten, K.: Accelerating molecular modeling applications with graphics processors. Journal of Computational Chemistry 28(16), 2618–2640 (2007)CrossRefGoogle Scholar
  23. 23.
    Streett, W., Tildesley, D., Saville, G.: Multiple time-step methods in molecular dynamics. Molecular Physics 35(3), 639–648 (1978)CrossRefGoogle Scholar
  24. 24.
    Wang, P.: Short-Range Molecular Dynamics on GPU (GTC 2010) (September 2010)Google Scholar
  25. 25.
    Yang, J., Wang, Y., Chen, Y.: GPU accelerated molecular dynamics simulation of thermal conductivities. Journal of Computational Physics 221(2), 799–804 (2007)MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Marcos Novalbos
    • 1
  • Jaime Gonzalez
    • 2
  • Miguel Angel Otaduy
    • 1
  • Alvaro Lopez-Medrano
    • 2
  • Alberto Sanchez
    • 1
  1. 1.URJC MadridSpain
  2. 2.Plebiotic S.L.Spain

Personalised recommendations