Advertisement

A Generalized Critical Task Anticipation Technique for DAG Scheduling

  • Ching-Hsien Hsu
  • Chih-Wei Hsieh
  • Chao-Tung Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4494)

Abstract

The problem of scheduling a weighted directed acyclic graph (DAG) representing an application to a set of heterogeneous processors to minimize the completion time has been recently studied. The NP-completeness of the problem has instigated researchers to propose different heuristic algorithms. In this paper, we present a Generalized Critical-task Anticipation (GCA) algorithm for DAG scheduling in heterogeneous computing environment. The GCA scheduling algorithm employs task prioritizing technique based on CA algorithm and introduces a new processor selection scheme by considering heterogeneous communication costs among processors for adapting grid and scalable computing. To evaluate the performance of the proposed technique, we have developed a simulator that contains a parametric graph generator for generating weighted directed acyclic graphs with various characteristics. We have implemented the GCA algorithm along with the CA and HEFT scheduling algorithms on the simulator. The GCA algorithm is shown to be effective in terms of speedup and low scheduling costs.

Keywords

Schedule Algorithm Direct Acyclic Graph Critical Score Data Transfer Rate Exit Node 
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.
    Bajaj, R., Agrawal, D.P.: Improving Scheduling of Tasks in a Heterogeneous Environment. IEEE Trans. on PDS 15(2), 107–118 (2004)Google Scholar
  2. 2.
    Behrooz, S., Wang, M., Pathak, G.: Analysis and Evaluation of Heuristic Methods for Static Task Scheduling. Jounal of Parallel and Distributed Computing 10, 222–232 (1990)CrossRefGoogle Scholar
  3. 3.
    Gary, M.R., Johnson, D.S.: Computers and Interactability: A guide to the Theory of NP-Completeness. W.H. Freeman and Co (1979)Google Scholar
  4. 4.
    Hagras, T., Janecek, J.: A High Performance, Low Complexity Algorithm for Compile-Time Task Scheduling in Heterogeneous Systems. Parallel Computing 31(7), 653–670 (2005)CrossRefGoogle Scholar
  5. 5.
    Hsu, C.-H., Weng, M.-Y.: An Improving Critical-Task Anticipation Scheduling Algorithm for Heterogeneous Computing Systems. In: Jesshope, C., Egan, C. (eds.) ACSAC 2006. LNCS, vol. 4186, pp. 97–110. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  6. 6.
    Ilavarasan, E., Thambidurai, P., Mahilmannan, R.: Performance Effective Task Scheduling Algorithm for Heterogeneous Computing System. In: IEEE Proceedings of IPDPS, pp. 28–38 (2005)Google Scholar
  7. 7.
    Ranaweera, S., Agrawal, D.P.: A Task Duplication Based Scheduling Algorithm for Heterogeneous Systems. In: IEEE Proceedings of IPDPS, pp. 445–450 (2000)Google Scholar
  8. 8.
    Sakellariou, R., Zhao, H.: A Hybrid Heuristic for DAG Scheduling on Heterogeneous Systems. In: Proc. of the IEEE IPDPS Workshop 1 (2004)Google Scholar
  9. 9.
    Topcuoglu, H., Hariri, S., Min-You, W.: Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing. IEEE Transactions on PDS 13(3), 260–274 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Ching-Hsien Hsu
    • 1
  • Chih-Wei Hsieh
    • 1
  • Chao-Tung Yang
    • 2
  1. 1.Department of Computer Science and Information Engineering, Chung Hua University, Hsinchu,300 TaiwanR.O.C.
  2. 2.High-Performance Computing Laboratory, Department of Computer Science and Information Engineering, Tunghai University, Taichung City, 40704, TaiwanR.O.C.

Personalised recommendations