A model for efficient programming of dynamic applications on distributed memory multiprocessors

  • A. Erzmann
  • M. Hadeler
  • C. Müller-Schloer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 966)


We present the TDC programming model which aims to ease the efficient implementation of dynamic applications on distributed memory multiprocessors. This model is based on task descriptors, data objects and capabilities which reside in distinct, globally accessible domains. Dynamic load balancing will be done by the system software and is completely transparent to the user. This often leads to a significant reduction of code complexity. Our prototype of the TDC model on an 128 node nCUBE2 uses a distributed diffusion scheme to balance load dynamically. We have developed a task selection strategy which reduces the load balancing overhead. Measuring and simulation results for a parallel implementation of a block matching algorithm indicate that runtime efficiency close to the optimum can be achieved with the TDC model even for highly parallel systems.


Programming Model Dynamic Load Balancing Block Matching Distributed Memory Multiprocessors 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    Ludwig, T.: Lastverwaltungsverfahren für Mehrprozessorsysteme mit verteiltem Speicher, Dissertation, Institut für Informatik, TU München, 1993Google Scholar
  2. [2]
    Gelernter, D.; Ahuja, S.; Carriero, N.: Linda and Friends, Computer, Vol. 19, No. 8, Aug. 1986, pp 26–34Google Scholar
  3. [3]
    Tärnvik, E.: Dynamo — a portable tool for dynamic load balancing on distributed memory multicomputers, Concurrency: Pratice and Experience, Vol. 6, No. 8, Dec. 1994Google Scholar
  4. [4]
    nCUBE Cooperation: nCUBE 2 Programmer's Guide, PN 102294, 1992Google Scholar
  5. [5]
    ISO/IEC 11172-2, Information technology — Coding of moving pictures and associated audio for digital storage media at up to about 1.5 MBit/s — Part 2: Video, Annex D.6.2, pp 78–85 Motion estimation and compensationGoogle Scholar
  6. [6]
    Lin, F.; Keller, R.: The Gradient Model Load Balancing Method, IEEE Transactions on Software Engineering, Vol. SE-13, No. 1, Jan. 1987Google Scholar
  7. [7]
    Cybenko, G.: Dynamic Load Balancing for Distributed Memory Multiprocessors, J. Parallel and Distributed Computing, Vol. 7, pp 279–301, October 1989Google Scholar
  8. [8]
    Willebeek-LeMair, M.H.; Reeves, A.P.: Strategies for Dynamic Load Balancing on Highly Parallel Computers, IEEE Transactions on Parallel and Distributed Systems, Vol. 4, No. 9, Sep. 1993Google Scholar
  9. [9]
    Gerogiannis, D.; Orphanoudakis, S.C.: Load Balancing Requirements in Parallel Implementations of Image Feature Extraction Tasks, IEEE Transactions on Parallel and Distributed Systems, Vol. 4, No. 9, Sep. 1993Google Scholar
  10. [10]
    Erzmann, A.; Müller-Schloer, C.: Zur Beurteilung dynamischer Lastausgleichsverfahren, PARS Mitteilungen, Nr. 13, November 1994Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • A. Erzmann
    • 1
  • M. Hadeler
    • 1
  • C. Müller-Schloer
    • 1
  1. 1.Institut für Rechnerstrukturen und Betriebssysteme Universität HannoverHannoverGermany

Personalised recommendations