FT-MPI: Fault Tolerant MPI, Supporting Dynamic Applications in a Dynamic World

  • Graham E. Fagg
  • Jack J. Dongarra
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1908)


Initial versions of MPI were designed to work efficiently on multiprocessors which had very little job control and thus static process models, subsequently forcing them to support dynamic process operations would have effected their performance. As current HPC systems increase in size with higher potential levels of individual node failure, the need rises for new fault tolerant systems to be developed. Here we present a new implementation of MPI called FT-MPI1 that allows the semantics and associated failure modes to be completely controlled by the application. Given is an overview of the FT-MPI semantics, design and some performance issues as well as the HARNESS g_hcore implementation it is built upon.


Virtual Machine Collective Communication Collective Operation Message Queue Mean Time Between Failure 
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.
    Beck, Dongarra, Fagg, Geist, Gray, Kohl, Migliardi, K. Moore, T. Moore, P. Papadopoulous, S. Scott, V. Sunderam, “HARNESS: a next generation distributed virtual machine”, Journal of Future Generation Computer Systems, (15), Elsevier Science B.V., 1999.Google Scholar
  2. 2.
    G. Stellner, “CoCheck: Checkpointing and Process Migration for MPI”, In Proceedings of the International Parallel Processing Symposium, pp 526–531, Honolulu, April 1996.Google Scholar
  3. 3.
    Adnan Agbaria and Roy Friedman, “Starfish: Fault-Tolerant Dynamic MPI Programs on Clusters of Workstations”, In the 8th IEEE International Symposium on High Performance Distributed Computing, 1999.Google Scholar
  4. 4.
    Graham E. Fagg, Keith Moore, Jack J. Dongarra, “Scalable networked information processing environment (SNIPE)”, Journal of Future Generation Computer Systems, (15), pp. 571–582, Elsevier Science B.V., 1999.CrossRefGoogle Scholar
  5. 5.
    Mauro Migliardi and Vaidy Sunderam, “PVM Emulation in the Harness MetaComputing System: A Plug-in Based Approach”, Lecture Notes in Computer Science (1697), pp 117–124, September 1999.Google Scholar
  6. 6.
    P. H. Worley, I. T. Foster, and B. Toonen, “Algorithm comparison and benchmarking using a parallel spectral transform shallow water model”, Proceedings of the Sixth Workshop on Parallel Processing in Meteorology, eds. G.-R. Hoffmann and N. Kreitz, World Scientific, Singapore, pp. 277–289, 1995.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Graham E. Fagg
    • 1
  • Jack J. Dongarra
    • 1
  1. 1.Department of Computer ScienceUniversity of TennesseeKnoxvilleUSA

Personalised recommendations