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The Journal of Supercomputing

, Volume 74, Issue 4, pp 1522–1533 | Cite as

Function portability of molecular dynamics on heterogeneous parallel architectures with OpenCL

  • Rene Halver
  • Wilhelm Homberg
  • Godehard Sutmann
Article

Abstract

Classical molecular dynamics simulation for atomistic systems is implemented in OpenCL and benchmarked on a variety of different hardware platforms. Modifying the number of particles and system size in the study provides insight into characteristics of parallel compute platforms, where latency, data transfer, memory access characteristics and compute intense work can be identified as fingerprints in benchmark runs. Data layouts are compared, for which the access of structure-of-arrays shows best performance in most cases. It is demonstrated that function portability can be achieved straightforwardly with OpenCL, while performance portability lacks behind as various architectures strongly depend on specific vectorisation optimisation.

Keywords

Molecular dynamics OpenCL Shared memory parallelisation Many-core architectures 

References

  1. 1.
    DOE (2017) Performance portability WS DOE. https://asc.llnl.gov/DOE-COE-Mtg-2016/
  2. 2.
    Frenkel D, Smit B (2002) Understanding molecular simulation. From algorithms to applications. Academic Press, San DiegozbMATHGoogle Scholar
  3. 3.
    Halver R, Sutmann G (2015) Multi-threaded construction of neighbour lists for particle systems in OpenMP. In: Parallel Processing and Applied Mathematics/Wyrzykowski, Roman (Editor), 11th International Conference on Parallel Processing and Applied Mathematics, Krakow (Poland), 6 Sept 2015–9 Sept 2015.  https://doi.org/10.1007/978-3-319-32152-3_15
  4. 4.
    Hockney RW, Eastwood JW (1981) Computer simulation using particles. McGraw-Hill, New YorkzbMATHGoogle Scholar
  5. 5.
  6. 6.
  7. 7.
  8. 8.
    Rapaport D (2001) The art of molecular dynamics simulation. Cambridge University Press, CambridgezbMATHGoogle Scholar
  9. 9.
    Sutmann G (2002) Classical molecular dynamics. In: Grotendorst J, Marx D, Muramatsu A (eds) Quantum simulations of many-body systems: from theory to algorithms, vol 10. NIC, Jülich, pp 211–254Google Scholar
  10. 10.
    Sutmann G, Stegailov V (2006) Optimization of neighbor list techniques in liquid matter simulations. J Mol Liq 125:197–203CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Jülich Supercomputing Centre (JSC), Institute for Advanced Simulation (IAS)Forschungszentrum Jülich (JSC)JülichGermany
  2. 2.ICAMS, Ruhr-University BochumBochumGermany

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