Skip to main content
Log in

Scheduling chained multiprocessor tasks onto large multiprocessor system

  • Published:
Computing Aims and scope Submit manuscript

Abstract

In this paper, we proposed an effective approach for scheduling of multiprocessor unit time tasks with chain precedence on to large multiprocessor system. In this work, we considered splitable and non-splitable multiprocessor tasks, which is a new and interesting dimension to the generalized scheduling problem. The proposed longest chain maximum processor scheduling algorithm is proved to be optimal for uniform chains and monotone (non-increasing/non-decreasing) chains for both splitable and non-splitable multiprocessor unit time tasks chain. Scheduling arbitrary chains of non-splitable multiprocessor unit time tasks is proved to be NP-complete problem. But scheduling arbitrary chains of splitable multiprocessor unit time tasks is still an open problem to be proved whether it is NP-complete or can be solved in polynomial time. We have used three heuristics (a) maximum criticality first, (b) longest chain maximum criticality first and (c) longest chain maximum processor first for scheduling of arbitrary chains. We have also compared the performance of all three scheduling heuristics and found out that the proposed longest chain maximum processor first performs better in most of the cases. Also we have evaluated the performance of the mentioned heuristics by scheduling scientific work-flows on real multi-processor server platform and analyzed power and performance trade-off of the same scheduling policies.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Amrutlal D Sueresh, Sahu A (2014) Scheduling of multi-phase applications on to mesh multicore architecture. IEEE Indicon

  2. Banerjee S, Surendra G, Nandy SK (2008) On the effectiveness of phase based regression models to trade power and performance using dynamic processor adaptation. J Syst Archit 54(8):797–815

    Article  Google Scholar 

  3. Bharathi S, Chervenak A, Deelman E, Mehta G, Su MH, Vahi K (2008) Characterization of scientific workflows 3rd workshop on workflows in support of large-scale science, Austin. TX, pp 1–10

  4. Blazewicz J, Weglarz J, Drabowski M (1984) Scheduling independent 2-processor tasks to minimize schedule length. Info Proc Lett 18(4):267–273

    Article  MathSciNet  MATH  Google Scholar 

  5. Blazewicz J, Drozdowski M, Schmidt G, De Werra D (1990) Scheduling independent 2-processor tasks on a uniform duo-processor system. Dis App Math 28(1):11–20

    Article  MATH  Google Scholar 

  6. Blazewicz J et al (1994) Scheduling independent multiprocessor tasks on a uniform k-processor system. Parallel Comput 20:15–28

    Article  MathSciNet  MATH  Google Scholar 

  7. Blazewicz J, Kobler D (2002) Review of properties of different precedence graphs for scheduling problems. Eur J Op Res 142:435–443

    Article  MathSciNet  MATH  Google Scholar 

  8. Blazewicz J, Liu Z (2002) Linear and quadratic algorithm for scheduling chains and opposite chains. Eur J Op Res 137(2):248–264

    Article  MathSciNet  MATH  Google Scholar 

  9. Blazewicz J, Weglarz J, Drabowski M (1986) Scheduling multiprocessor tasks to minimize schedule length. IEEE Trans Comput C-35(5):389–393

  10. Blazewicz J, Zhen L (1996) Scheduling multiprocessor tasks with chain constraint. Eur J Op Res (final version)

  11. Brucker P (2006) Scheduling algorithms, 5th edn. Springer, Berlin

    MATH  Google Scholar 

  12. Cho CB, Li T (2006) Complexity-based program phase analysis and classification. In: Proceedings of PACT, pp 105–113

  13. Coffman EG, Graham RL (1972) Optimal scheduling for two-processor systems. Acta Inform 1:200–213

    Article  MathSciNet  MATH  Google Scholar 

  14. Deelman E, Singh G et al (2005) Pegasus: a framework for mapping complex scientific workflows onto distributed systems. Sci Progr 13(3):219–237

    Google Scholar 

  15. Gonzalez TF, Johnson DB (1980) A new algorithm for preemptive scheduling of trees. J ACM 27:287–312

    Article  MathSciNet  MATH  Google Scholar 

  16. Gonzalez TF, Johnson DB (1980) A new algorithm for preemptive scheduling of trees. J ACM 27(2):287–312

    Article  MathSciNet  MATH  Google Scholar 

  17. Hu TC (1961) Parallel sequencing and assembly line problems. Op Res 19(6):841–848

    Article  MathSciNet  Google Scholar 

  18. Kwokand Y-K, Ahmad I (1999) Static scheduling algorithms for allocating directed task graphs to multiprocessors. ACM Comput Surv (CSUR) 31(4):406–471

    Article  Google Scholar 

  19. Li Q, Ruan Y (2003) An optimal scheduling algorithm for fork-join task graphs, Par and Dist Comp, Appl and Tech

  20. McNaughton R (1959) Scheduling with deadline and loss functions. Manag Sci 6:1–12

    Article  MathSciNet  MATH  Google Scholar 

  21. Papadimitriou CH, Yannakakis M (1987) Scheduling interval-ordered tasks. SIAM J Comput 8(3):405–409

    Article  MathSciNet  MATH  Google Scholar 

  22. Prasanna GN Srinivasa, Musicus BR (1994) Generalized multiprocessor scheduling for directed acyclic graphs. In: Proceedings of IEEE supercomputing, 237–246

  23. Sherwood T, Sair S, Calder B (2003) Phase tracking and prediction. ACM SIGARCH Comput Arch News 31(2):336–349

    Article  Google Scholar 

  24. Treibig J, Hager G, Wellein G (2010) LIKWID: a lightweight performance-oriented tool suite for x86 multicore environments. In: Proceedings of IEEE international conference on parallel processing workshops (ICPPW ’10), 207–216

  25. Ullman J (1975) NP-complete scheduling problems. J Comput Sys Sci 10:384–393

    Article  MathSciNet  MATH  Google Scholar 

  26. Wieczorek M, Prodan R, Fahringer T (2005) Scheduling of scientific workflows in the ASKALON grid environment. SIGMOD Rec 34(3):56–62

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Aryabartta Sahu.

Additional information

Older arxiv version at http://arxiv.org/abs/1508.03236.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Agrawal, T.K., Sahu, A., Ghose, M. et al. Scheduling chained multiprocessor tasks onto large multiprocessor system. Computing 99, 1007–1028 (2017). https://doi.org/10.1007/s00607-017-0543-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00607-017-0543-z

Keywords

Mathematics Subject Classification

Navigation