Advertisement

From Heterogeneous Task Scheduling to Heterogeneous Mixed Parallel Scheduling

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

Abstract

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.

Keywords

Directed Acyclic Graph Task Graph Execution Cost Schedule Length Heterogeneous Platform 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  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

Personalised recommendations