Grid Resource Selection by Application Benchmarking for Computational Haemodynamics Applications

  • Alfredo Tirado-Ramos
  • George Tsouloupas
  • Marios Dikaiakos
  • Peter Sloot
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3514)


Grid benchmarking for improved computational resource selection can shed a light for improving the performance of computationally intensive applications. In this paper we report on a number of experiments with a biomedical parallel application to investigate the levels of performance offered by hardware resources distributed across a pan-European computational Grid network. We provide a number of performance measurements based on the iteration time per processor and communication delay between processors, for a blood flow simulation benchmark based on the lattice Boltzmann method. We have found that the performance results obtained from real application benchmarking are much more useful for running our biomedical application on a highly distributed grid infrastructure than the regular resource information provided by standard Grid information services to resource brokers.


Europe Assure Peri 


  1. 1.
    Brune, M., Gehring, J., Keller, A., Monien, B., Reinefeld, A.: Specifying Resources and Services in Metacomputing Environments. In: Parallel Computing, 24th edn., pp. 1751–1776. Elsevier Science, Amsterdam (1998)Google Scholar
  2. 2.
    Chun, G., Dail, H., Casanova, H., Snavely, A.: Benchmark probes for grid assessment. Technical report, UCSD (2003)Google Scholar
  3. 3.
    Houstis, E.N., Rice, J.R., Weerwarna, S., Papachio, P., Yang, W.K., Gaitatzes, M.: Enabling Technologies for Computational Science Frameworks. In: Middleware and Environments, ch. 14, pp. 171–185. Kluwer Academic Publishers, Dordrecht (2000)Google Scholar
  4. 4.
    Tirado-Ramos, A., Sloot, P.M.A., Hoekstra, A.G., Bubak, M.: An Integrative Approach to High-Performance Biomedical Problem Solving Environments on the Grid. In: Huang, C.-H., Rajasekaran, S. (eds.) Parallel Computing (special issue on High-Performance Parallel Bio-computing), vol. 30(9-10), pp. 1037–1055 (2004)Google Scholar
  5. 5.
    Hoschek, W., Jaen-Martinez, J., Samar, A., Stockinger, H., Stockinger, K.: Data Management in an International Data Grid Project. In: IEEE/ACM International Workshop on Grid Computing Grid 2000, Bangalore, India, December 17-20, (2000) ”Distinguished Paper” AwardGoogle Scholar
  6. 6.
  7. 7.
    Artoli, A.M., Hoekstra, A.G., Sloot, P.M.A.: Simulation of a systolic cycle in a realistic artery with the Lattice Boltzmann BGK method. Int. J. Mod. Phys. B 17(1-2), 95–98 (2003)CrossRefGoogle Scholar
  8. 8.
    Succi, S.: The Lattice Boltzmann Equation for fluid dynamics and beyond. Oxford Science Publications, Clarendon Press (2001)Google Scholar
  9. 9.
    Tsouloupas, G., Dikaiakos, M.D.: GridBench: A Tool for Benchmarking Grids. In: Proceedings of the 4th International Workshop on Grid Computing (GRID 2003), Phoenix, AZ, November 2003, pp. 60–67. IEEE, Los Alamitos (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Alfredo Tirado-Ramos
    • 1
  • George Tsouloupas
    • 2
  • Marios Dikaiakos
    • 2
  • Peter Sloot
    • 1
  1. 1.Faculty of Sciences, Section Computational ScienceUniversity of AmsterdamAmsterdamThe Netherlands
  2. 2.Department of Computer ScienceUniversity of CyprusNicosiaCyprus

Personalised recommendations