Parallel biomolecular simulation: An overview and analysis of important algorithms

  • Gerald Löffler
  • Hellfried Schreiber
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1067)


We have presented important algorithms for serial MD simulations of biomedical systems and have analysed their impact on parallel performance. None of these algorithms can be neglected if we are interested in true gains in throughput and not just in good formal scalability numbers. This is especially true for the SHAKE algorithm, which due to its small contribution to the total runtime and due to its inherently serial character is often not included in reports on the parallelisation of MD programs. We have shown clearly that even a very modest speedup in this algorithm is essential for increased overall performance.


Water Molecule Molecular Dynamics Molecular Dynamics Simulation Total Runtime Force Calculation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    S. E. DeBolt and P. A. Kollman. J. Comp. Chem., 14(3):312–329, 1993.Google Scholar
  2. 2.
    D. Brown, J. H. R. Clarke, M. Okuda, and T. Yamazaki. Comput. Phys. Commun., 74:67–80, 1993.Google Scholar
  3. 3.
    W. Scott, A. Gunzinger, B. Bäumle, P. Kohler, U. A. Müller, H-R. Vonder Mühll, A. Eichenberger, W. Guggenbühl, N. Ironmonger, F. Miller-Plathe, and W. F. van Gunsteren. Comput. Phys. Commun., 75:65–86, 1993.Google Scholar
  4. 4.
    W. Smith and T. R. Forester. Comput. Phys. Commun., 79:52–62, 1994.Google Scholar
  5. 5.
    F. Hedman and A. Laaksonen. Mol. Sim., 14:235–244, 1995.Google Scholar
  6. 6.
    S. Fleischman. Mol. Sim., 14:209–233, 1995.Google Scholar
  7. 7.
    W. S. Young and C. L. Brooks III. J. Comp. Chem., 16(6):715–722, 1995.Google Scholar
  8. 8.
    E. Swanson and T. P. Lybrand. J. Comp. Chem., 16(9):1131–1140, 1995.Google Scholar
  9. 9.
    S. Plimpton and B. Hendrickson. Sandia Report, SAND94-1862, 1994.Google Scholar
  10. 10.
    R. L. Martino, C. A. Johnson, B. L. Suh, E. B. Trus, and E. B. Yap. Science, 265:902–908, 1994.Google Scholar
  11. 11.
    J. J. Vincent, M. Kenneth, and M. Merz Jr. J. Comp. Chem., 16(11):1420–1427, 1995.Google Scholar
  12. 12.
    G. Löffler and P. H. Maccallum. ParCo95 Conference, 1995.Google Scholar
  13. 13.
    H. Schreiber, O. Steinhauser, and P. Schuster. Parallel Computing, 18:557, 1992.Google Scholar
  14. 14.
    K. Esselink, B. Smit, and P. A. J. Hilbers. J. Comput. Phys., 106:101–107, 1993.Google Scholar
  15. 15.
    J. F. Janak and P. C. Pattnaik. J. Comp. Chem., 13(9):1098–1102, 1992.Google Scholar
  16. 16.
    R. C. Schweitzer and G. W. Small. J. Comp. Chem., 14(8):977–985, 1993.Google Scholar
  17. 17.
    J. E. Mertz, D. J. Tobias, C. L. Brooks III, and U. C. Singh. J. Comp. Chem., 12(10):1270–1277, 1991.Google Scholar
  18. 18.
    H. J. C. Berendsen, J. P. M. Postma, W. F. van Gunsteren, and J. Hermans. page 331. Reidel, Dordrecht, 1981.Google Scholar
  19. 19.
    W. Smith and T. R. Forester. Comput. Phys. Commun., 79:63–77, 1994.Google Scholar
  20. 20.
    H. Schreiber and O. Steinhauser, in preparation.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Gerald Löffler
    • 1
  • Hellfried Schreiber
    • 1
  1. 1.Institute for Theoretical Chemistry, Theoretical Biochemistry GroupUniversity of ViennaWienAustria

Personalised recommendations