From Heterogeneous Task Scheduling to Heterogeneous Mixed Parallel Scheduling

  • Frédéric Suter
  • Frédéric Desprez
  • Henri Casanova
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3149)


Mixed-parallelism, the combination of data- and task-parallelism, is a powerful way of increasing the scalability of entire classes of parallel applications on platforms comprising multiple compute clusters. While multi-cluster platforms are predominantly heterogeneous, previous work on mixed-parallel application scheduling targets only homogeneous platforms. In this paper we develop a method for extending existing scheduling algorithms for task-parallel applications on heterogeneous platforms to the mixed-parallel case.


Directed Acyclic Graph Task Graph Execution Cost Schedule Length Heterogeneous Platform 
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  1. 1.
    Boudet, V., Desprez, F., Suter, F.: One-Step Algorithm for Mixed Data and Task Parallel Scheduling Without Data Replication. In: Proc. of the 17th International Parallel and Distributed Processing Symposium (IPDPS 2003) (April 2003)Google Scholar
  2. 2.
    Chretienne, P.: Task Scheduling Over Distributed Memory Machines. In: Parallel and Distributed Algorithms, pp. 165–176. North-Holland, Amsterdam (1988)Google Scholar
  3. 3.
    Desprez, F., Dongarra, J., Petitet, A., Randriamaro, C., Robert, Y.: Scheduling Block-Cyclic Array Redistribution. IEEE TPDS 9(2), 192–205 (1998)Google Scholar
  4. 4.
    Foster, I., Kesselman, C. (eds.): The Grid: Blueprint for a New Computing Infrastructure. Morgan Kaufmann, San Francisco (1998) ISBN 1-55860-475-8Google Scholar
  5. 5.
    Legrand, A., Marchal, L., Casanova, H.: Scheduling Distributed Applications: The SimGrid Simulation Framework. In: Proc. of the 3rd IEEE Symposium on Cluster Computing and the Grid (CCGrid 2003), Tokyo, May 2003, pp. 138–145 (2003)Google Scholar
  6. 6.
    Maheswaran, M., Siegel, H.J.: A Dynamic Matching and Scheduling Algorithm for Heterogeneous Computing Systems. In: Proc. of the 7th Heterogeneous Computing Workshop (HCW 1998), pp. 57–69 (1998)Google Scholar
  7. 7.
    Oh, H., Ha, S.: A Static Scheduling Heuristic for Heterogeneous Processors. In: Fraigniaud, P., Mignotte, A., Robert, Y., Bougé, L. (eds.) Euro-Par 1996. LNCS, vol. 1124, pp. 573–577. Springer, Heidelberg (1996)CrossRefGoogle Scholar
  8. 8.
    Radulescu, A., Nicolescu, C., van Gemund, A., Jonker, P.: Mixed Task and Data Parallel Scheduling for Distributed Systems. In: Proc. of the 15th International Parallel and Distributed Processing Symposium (IPDPS), San Francisco (April 2001)Google Scholar
  9. 9.
    Ramaswany, S.: Simultaneous Exploitation of Task and Data Parallelism in Regular Scientific Applications. PhD thesis, Univ. of Illinois at Urbana-Champaign (1996)Google Scholar
  10. 10.
    Rauber, T., Rünger, G.: Compiler Support for Task Scheduling in Hierarchical Execution Models. Journal of Systems Architecture 45, 483–503 (1998)CrossRefGoogle Scholar
  11. 11.
    Sih, G., Lee, E.: A Compile-Time Scheduling Heuristic for Interconnection- Constrained Heterogeneous Processor Architectures. IEEE TPDS 4(2), 175–187Google Scholar
  12. 12.
  13. 13.
    Suter, F., Casanova, H., Desprez, F., Boudet, V.: From Heterogeneous Task Scheduling to Heterogeneous Mixed Data and Task Parallel Scheduling. Technical Report RR2003-52, Laboratoire de l’Informatique du Parallélisme (LIP) (November 2003)Google Scholar
  14. 14.
    Topcuoglu, H., Hariri, S., Wu, M.-Y.: Performance-Effective and Low- Complexity Task Scheduling for Heterogeneous Computing. IEEE TPDS 13(3), 260–274 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Frédéric Suter
    • 1
  • Frédéric Desprez
    • 2
  • Henri Casanova
    • 1
    • 3
  1. 1.Dept. of CSEUniv. of CaliforniaSan DiegoUSA
  2. 2.LIP ENS LyonUMR CNRS ENS Lyon UCB Lyon INRIAFrance
  3. 3.San Diego Supercomputer CenterUniv. of CaliforniaSan DiegoUSA

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