ExaStamp: A Parallel Framework for Molecular Dynamics on Heterogeneous Clusters

  • Emmanuel Cieren
  • Laurent Colombet
  • Samuel Pitoiset
  • Raymond Namyst
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8806)


Recent evolution of supercomputer architectures toward massively multi-cores nodes equipped with many-core accelerators is leading to make MPI-only applications less effective. To fully tap into the potential of these architectures, hybrid approaches – mixing MPI, threads and CUDA or OpenCL – usually meet performance expectations, but at the price of huge development and optimization efforts.

In this paper, we present a programming framework specialized for molecular dynamics simulations. This framework allows end-users to develop their computation kernels in the form of sequential-looking functions and generates multi-level parallelism combining vectorized and SIMD kernels, multi-threading and communications. We report on preliminary performance results obtained on different architectures with widely used force computation kernels.


Molecular dynamics MPI threads TBB vectorization OpenCL object-oriented design Lennard-Jones EAM 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Alder, B.J., Wainwright, T.E.: Phase Transition for a Hard Sphere System. The Journal of Chemical Physics 27(5), 1208–1209 (1957)CrossRefGoogle Scholar
  2. 2.
    Allen, M., Tildesley, D.: Computer Simulation of Liquids. Clarendon Press (1987)Google Scholar
  3. 3.
    Berendsen, H., van der Spoel, D., van Drunen, R.: GROMACS: A Message-passing Parallel Molecular Dynamics Implementation. Computer Physics Communications 91(1-3), 43–56 (1995)CrossRefGoogle Scholar
  4. 4.
    Contreras, G., Martonosi, M.: Characterizing and Improving the Performance of Intel Threading Building Blocks. In: IEEE International Symposium on Workload Characterization, IISWC 2008, pp. 57–66 (2008)Google Scholar
  5. 5.
    Coplien, J.O.: Curiously Recurring Template Patterns. C++ Rep. 7(2), 24–27 (1995)Google Scholar
  6. 6.
    Daw, M.S., Baskes, M.I.: Embedded-atom Method: Derivation and Application to Impurities, Surfaces, and other Defects in Metals. Phys. Rev. B 29, 6443–6453 (1984)CrossRefGoogle Scholar
  7. 7.
    Daw, M.S., Foiles, S.M., Baskes, M.I.: The embedded-atom Method: a Review of Theory and Applications. Materials Science Reports 9(7-8), 251–310 (1993)CrossRefGoogle Scholar
  8. 8.
    Foiles, S.M., Baskes, M.I., Daw, M.S.: Embedded-atom Method Functions for the FCC Metals Cu, Ag, Au, Ni, Pd, Pt, and their Alloys. Phys. Rev. B 33, 7983–7991 (1986)CrossRefGoogle Scholar
  9. 9.
    Germann, T.C., Kadau, K., Swaminarayan, S.: 369 Tflop/s Molecular Dynamics Simulations on the Petaflop Hybrid Supercomputer ‘Roadrunner’. Concurrency and Computation: Practice and Experience 21(17), 2143–2159 (2009)CrossRefGoogle Scholar
  10. 10.
    Greengard, L., Rokhlin, V.: A Fast Algorithm for Particle Simulations. Journal of Computational Physics 73(2), 325–348 (1987)CrossRefMathSciNetzbMATHGoogle Scholar
  11. 11.
    Hoover, W.G., De Groot, A.J., Hoover, C.G., Stowers, I.F., Kawai, T., Holian, B.L., Boku, T., Ihara, S., Belak, J.: Large-scale Elastic-plastic Indentation Simulations via Nonequilibrium Molecular Dynamics. Phys. Rev. A 42(10), 5844–5853 (1990)CrossRefGoogle Scholar
  12. 12.
    Johnson, R.A.: Alloy Models with the Embedded-atom Method. Phys. Rev. B 39, 12554–12559 (1989)CrossRefGoogle Scholar
  13. 13.
    Jones, J.E.: On the Determination of Molecular Fields. II. From the Equation of State of a Gas. Proceedings of the Royal Society of London. Series A 106(738), 463–477 (1924)CrossRefGoogle Scholar
  14. 14.
    Leimkuhler, B.J., Reich, S., Skeel, R.D.: Integration Methods for Molecular Dynamics. In: Mesirov, J.P., Schulten, K., Sumners, D.W. (eds.) Mathematical Approaches to Biomolecular Structure and Dynamics. The IMA Volumes in Mathematics and its Applications, vol. 82, pp. 161–185. Springer, New York (1996)CrossRefGoogle Scholar
  15. 15.
    Nelson, M.T., Humphrey, W., Gursoy, A., Dalke, A., Kalé, L.V., Skeel, R.D., Schulten, K.: NAMD: a Parallel, Object-oriented Molecular Dynamics Program. International Journal of High Performance Computing Applications 10(4), 251–268 (1996)CrossRefGoogle Scholar
  16. 16.
    Plimpton, S.: Fast Parallel Algorithms for Short-range Molecular Dynamics. Journal of Computational Physics 117(1), 1–19 (1995)CrossRefzbMATHGoogle Scholar
  17. 17.
    Sandia National Laboratories: LAMMPS User Manual (2014),
  18. 18.
    Soulard, L.: Molecular Dynamics Study of the Micro-spallation. The European Physical Journal D 50(3) (2008)Google Scholar
  19. 19.
    Stillinger, F.H., Weber, T.A.: Computer Simulation of Local Order in Condensed Phases of Silicon. Phys. Rev. B 31, 5262–5271 (1985)CrossRefGoogle Scholar
  20. 20.
    Sutton, A.P., Chen, J.: Long-range Finnis-Sinclair Potentials. Philosophical Magazine Letters 61(3), 139–146 (1990)CrossRefGoogle Scholar
  21. 21.
    Tersoff, J.: New Empirical Approach for the Structure and Energy of Covalent Systems. Phys. Rev. B 37, 6991–7000 (1988)CrossRefGoogle Scholar
  22. 22.
    Wolff, D., Rudd, W.G.: Tabulated Potentials in Molecular Dynamics Simulations. Computer Physics Communications 120(1), 20–32 (1999)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Emmanuel Cieren
    • 1
  • Laurent Colombet
    • 1
  • Samuel Pitoiset
    • 2
  • Raymond Namyst
    • 2
  1. 1.CEA, DAM, DIFArpajonFrance
  2. 2.Université de Bordeaux, INRIATalence CedexFrance

Personalised recommendations