Supporting Efficient Execution of MPI Applications Across Multiple Sites

  • Enol Fernández
  • Elisa Heymann
  • Miquel Àngel Senar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4128)


One of the main goals of the CrossGrid Project [1] is to provide explicit support to parallel and interactive compute- and data- intensive applications. The CrossBroker job manager provides services as part of the CrossGrid middleware and allows execution of parallel MPI applications on Grid resources in a transparent and automatic way. This document describes the design and implementation of the key components responsible for an efficient and reliable execution of MPI jobs splitted over multiple Grid sites, executed either in an on-line or batch manner. We also provide details on the overheads introduced by our system, as well as an experimental study showing that our system is well-suited for embarrassingly parallel applications.


Virtual Machine Parallel Application Grid Resource Grid Environment Application Execution 
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  1. 1.
    EU-CrossGrid (2004),
  2. 2.
    Schopt, J.M.: Ten Actions When Grid Scheduling. In: Grid Resource Management - State of the Art and Future Trends, Kluwer Academic Publishers, Dordrecht (2003)Google Scholar
  3. 3.
    Karonis, N.T., et al.: Mpich-g2: A grid-enabled implementation of the message passing interface. J. Parallel Distrib. Comput. 63(5), 551–563 (2003)MATHCrossRefGoogle Scholar
  4. 4.
    Bucur, A., Epema, D.: The performance of processor co-allocation in multicluster. In: 11th Int. Symp on High Perf. Distr. Comp. (2002)Google Scholar
  5. 5.
    Wang, L., et al.: Resource co-allocation for parallel tasks in computational grids. In: Int. Workshop on Challenges of Large Apps. in Dist. Env. (2003)Google Scholar
  6. 6.
    Czajkowski, K., Foster, I., Kesselman, C.: Resource co-allocation in computational grids. In: Proceedings of the HPDC-8, pp. 219–228 (1999)Google Scholar
  7. 7.
    Lindner, P., et al.: Gcm: a grid configuration manager for heterogeneous grid enviromnents. Int. J. Grid and Utility Computing 1(1), 4–12 (2005)CrossRefGoogle Scholar
  8. 8.
    Gabriel, E., et al.: Distributed computing in a heterogenous computing environment. In: EuroPVMMPI 1998 (1998)Google Scholar
  9. 9.
    Heymann, E., et al.: Managing mpi applications in grid environments. In: European Across Grids Conference, pp. 42–50 (2004)Google Scholar
  10. 10.
    Pazini, F.: Jdl attibutes. Technical report, European Datagrid Project (2001)Google Scholar
  11. 11.
    Raman, R., et al.: Matchmaking: Distributed resource management for high throughput computing. In: HPDC-7, Chicago, IL (1998)Google Scholar
  12. 12.
    Thain, D., et al.: Condor and the grid. In: Grid Computing: Making the Global Infrastructure a Reality, John Wiley & Sons Inc., Chichester (2003)Google Scholar
  13. 13.
    Gutiérrez, A., et al.: Parallelization of a neural net training program in a grid environment. In: PDP 2004, pp. 258–265 (2004)Google Scholar
  14. 14.
    Cameron, D., et al.: Replica management services in the european datagrid project. In: UK e-Science All Hands Conference (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Enol Fernández
    • 1
  • Elisa Heymann
    • 1
  • Miquel Àngel Senar
    • 1
  1. 1.Departament d’Arquitectura de Computadors i Sistemes OperatiusUniversitat Autònoma de BarcelonaBarcelonaSpain

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