Skip to main content
SpringerLink
Log in

Task scheduling for heterogeneous computing systems

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Efficient scheduling of tasks in heterogeneous computing systems is of primary importance for high-performance execution of programs. The programs are to be considered as multiple sequences of tasks that are presented as directed acyclic graphs (DAG). Each task has its own execution timeline that incorporates into multiple processors. Moreover, each edge on the graph represents constraints between the sequenced tasks. In this paper, we propose a new list–scheduling algorithm that schedules the tasks represented in the DAG to the processor that best minimizes the total execution time by taking into consideration the restriction of crossover between processors. This objective will be achieved in two major phases: (a) computing priorities of each task that will be executed, and (b) selecting the processor that will handle each task. The first phase, priorities computation, focuses on finding the best execution sequence that minimizes the makespan of the overall execution. In list-scheduling algorithm, the quality of the solution is very sensitive to the priority assigned to the tasks. Therefore, in this paper, we include an enhanced calculation of weight that is used in the ranking equation for determining the priority of tasks. The second phase, processor selection, primarily focuses on allocating a processor that is a best fit for each task to be executed. In this paper, we enhance the processor selection by introducing a randomized decision mechanism based on a threshold which decides whether the task be assigned to the processor with the lowest execution time or to the processor that produces the lowest finish time. This mechanism considers a balanced combination of the local and global optimal results to explore the search space efficiently to optimize the overall makespan. The proposed algorithm is evaluated on different randomly generated DAGs, and results are compared with the well-known existing approaches to show the effectiveness of the proposed algorithm in reducing the makespan of execution. The experiment’s results show improvement in the makespan that reaches up to 6–7%.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  1. International Data Corporation, Worldwide and regional public cloud services 2012–2016 forecast. http://www.idc.com. Accessed 10 Dec 2015

  2. Yang CH, Lee P, Chung YC (2007) Improving static task scheduling in heterogeneous and homogeneous computing systems. In: IEEE Parallel Processing, 2007. ICPP 2007. International Conference on, pp 45–45

  3. Arabnia HR, Oliver MA (1987) Arbitrary rotation of raster images with SIMD machine architectures. Int J Eurograph Assoc (Computer Graphics Forum) 6(1):3–12

    Article  Google Scholar 

  4. Arabnia HR, Oliver MA (1989) A transputer network for fast operations on digitised images. Int J Eurograph Assoc (Computer Graphics Forum) 8(1):3–11

    Article  Google Scholar 

  5. Arabnia HR (1990) A parallel algorithm for the arbitrary rotation of digitized images using process-and-data-decomposition approach. J Parallel Distrib Comput 10(2):188–193

    Article  Google Scholar 

  6. Bhandarkar SM, Arabnia HR, Smith JW (1995) A reconfigurable architecture for image processing and computer vision. Int J Pattern Recognit Artif Intell (IJPRAI) 9(2):201–229

    Article  Google Scholar 

  7. Bhandarkar SM, Arabnia HR (1995) The Hough transform on a reconfigurable multi-ring network. J Parallel Distrib Comput 24(1):107–114

    Article  Google Scholar 

  8. Bhandarkar SM, Arabnia HR (1995) The REFINE multiprocessor: theoretical properties and algorithms. Parallel Comput 21(11):1783–1806

    Article  Google Scholar 

  9. Arabnia HR, Bhandarkar SM (1996) Parallel stereocorrelation on a reconfigurable multi-ring network. J Supercomput 10(3):243–270

    Article  MATH  Google Scholar 

  10. Wani MA, Arabnia HR (2003) Parallel edge-region-based segmentation algorithm targeted at reconfigurable multi-ring network. J Supercomput 25(1):43–63

    Article  MATH  Google Scholar 

  11. Canon LC, Jeannot E, Sakellariou R, Zheng W (2008) Comparative evaluation of the robustness of dag scheduling heuristics. In: Grid Computing. Springer, US, pp 73–84

  12. Young CP, Chang BR, Qiu ZL (2012) Scheduling optimization for vector graphics acceleration on multiprocessor systems. J Inf Hiding Multimed Signal Process 3(3):248–278

    Google Scholar 

  13. Zhou H, Liu C (2014) Task mapping in heterogeneous embedded systems for fast completion time. In: ACM Proceedings of the 14th International Conference on Embedded Software, pp 1–10

  14. Arabnejad H, Barbosa JG (2014) List scheduling algorithm for heterogeneous systems by an optimistic cost table. IEEE Trans Parallel Distrib Syst 25(3):682–694

    Article  Google Scholar 

  15. Augonnet C, Thibault S, Namyst R, Wacrenier PA (2011) StarPU: a unified platform for task scheduling on heterogeneous multicore architectures. Concurr Comput Pract Exp 23(2):187–198

    Article  Google Scholar 

  16. Dai Y, Zhang X (2014) A synthesized heuristic task scheduling algorithm. Sci World J 2014:1–9

    Google Scholar 

  17. Kuchcinski K (2003) Constraints-driven scheduling and resource assignment. ACM Trans Design Automat Electron Syst (TODAES) 8(3):355–383

    Article  Google Scholar 

  18. Shetti KR, Fahmy SA, Bretschneider T (2013) Optimization of the HEFT Algorithm for a CPU-GPU Environment. In: IEEE Parallel and Distributed Computing, Applications and Technologies (PDCAT), 2013 International Conference on, pp 212–218

  19. Topcuoglu H, Hariri S, Wu MY (2002) Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Trans Parallel Distrib Syst 13(3):260–274

    Article  Google Scholar 

  20. Ullman JD (1975) NP-complete scheduling problems. J Comput Syst Sci 10(3):384–393

    Article  MathSciNet  MATH  Google Scholar 

  21. Arabnejad H (2013) List based task scheduling algorithms on heterogeneous systems-an overview. https://paginas.fe.up.pt/~prodei/dsie12/papers/paper_30.pdf. Accessed 5 Jun 2016

  22. Sakellariou R, Zhao H (2004) A hybrid heuristic for DAG scheduling on heterogeneous systems. In: IEEE Parallel and Distributed Processing Symposium, 2004. Proceedings. 18th International, p 111

  23. Kwok YK, Ahmad I (1999) Static scheduling algorithms for allocating directed task graphs to multiprocessors. ACM Comput Surv 31(4):406–471

    Article  Google Scholar 

  24. Boveiri HR (2015) An efficient task priority measurement for list-scheduling in multiprocessor environments. Int J Softw Eng Appl 9(5):233–246

    Google Scholar 

  25. N’Takpé T, Suter F, Casanova H (2007) A comparison of scheduling approaches for mixed-parallel applications on heterogeneous platforms. In: IEEE Parallel and Distributed Computing, 2007. ISPDC’07. Sixth International Symposium on, pp 35–35

  26. Venugopalan S, Sinnen O (2012) Optimal linear programming solutions for multiprocessor scheduling with communication delays. In: Algorithms and architectures for parallel processing. Springer, Berlin Heidelberg, pp 129–138

  27. Hagras T, Janecek J (2003) A simple scheduling heuristic for heterogeneous computing environments. In: IEEE International Symposium on Parallel and Distributed Computing, pp 104–110

  28. Ilavarasan E, Thambidurai P, Mahilmannan R (2005) High performance task scheduling algorithm for heterogeneous computing system. In: Distributed and Parallel Computing. Springer Berlin Heidelberg, pp 193–203

  29. Ilavarasan E, Thambidurai P (2007) Low complexity performance effective task scheduling algorithm for heterogeneous computing environments. J Comput Sci 3(2):94–103

    Article  Google Scholar 

  30. Bittencourt LF, Sakellariou R, Madeira ER (2010) Dag scheduling using a lookahead variant of the heterogeneous earliest finish time algorithm. In: IEEE In Parallel, Distributed and Network-Based Processing (PDP), 2010 18th Euromicro International Conference on, pp 27–34

  31. Falzon G, Li M (2012) Enhancing list scheduling heuristics for dependent job scheduling in grid computing environments. J Supercomput 59(1):104–130

    Article  Google Scholar 

  32. Dhodhi MK, Ahmad I, Yatama A, Ahmad I (2002) An integrated technique for task matching and scheduling onto distributed heterogeneous computing systems. J Parallel Distrib Comput 62(9):1338–1361

    Article  MATH  Google Scholar 

  33. Herrmann J, Marchal L, Robert Y (2014) Memory-aware list scheduling for hybrid platforms. In: 2014 IEEE International In Parallel & Distributed Processing Symposium Workshops (IPDPSW), pp 689–698. doi:10.1109/IPDPSW.2014.80

  34. Su S, Li J, Huang Q, Huang X, Shuang K, Wang J (2013) Cost-efficient task scheduling for executing large programs in the cloud. Parallel Comput 39(4):177–188

    Article  Google Scholar 

  35. Wang Y, Li K, Chen H, He L, Li K (2014) Energy-aware data allocation and task scheduling on heterogeneous multiprocessor systems with time constraints. IEEE Trans Emerg Topics Comput 2(2):134–148

    Article  Google Scholar 

  36. Huang KC, Tsai YL, Liu HC (2015) Task ranking and allocation in list-based workflow scheduling on parallel computing platform. J Supercomput 71(1):217–240

    Article  Google Scholar 

Download references

Acknowledgments

Thanks are extended to anonymous reviewers for their constructive comments which improved overall quality of the manuscript.

Author information

Authors and Affiliations

  1. Computer Engineering Department, Kuwait University, P.O. Box 5969, Safat, 1306, Kuwait

    Shaikhah AlEbrahim & Imtiaz Ahmad

Authors
  1. Shaikhah AlEbrahim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Imtiaz Ahmad
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Imtiaz Ahmad.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

AlEbrahim, S., Ahmad, I. Task scheduling for heterogeneous computing systems. J Supercomput 73, 2313–2338 (2017). https://doi.org/10.1007/s11227-016-1917-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-016-1917-2

Keywords

Search

Navigation