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Efficient approximation algorithms for scheduling moldable tasks

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  • Wu, Xiaohu
  • Loiseau, Patrick

Abstract

Moldable tasks allow schedulers to determine the number of processors assigned to each task, thus enabling efficient use of large-scale parallel processing systems. We consider the problem of scheduling independent moldable tasks on processors and propose a new perspective of the existing speedup models: as the number p of processors assigned to a task increases, the speedup is linear if p is small and becomes sublinear after p exceeds a threshold. Based on this, we propose an efficient approximation algorithm to minimize the makespan. As a by-product, we also propose an approximation algorithm to maximize the sum of values of tasks completed by a deadline; this scheduling objective is considered for moldable tasks for the first time while similar works have been done for other types of parallel tasks.

Suggested Citation

  • Wu, Xiaohu & Loiseau, Patrick, 2023. "Efficient approximation algorithms for scheduling moldable tasks," European Journal of Operational Research, Elsevier, vol. 310(1), pages 71-83.
    • Handle: RePEc:eee:ejores:v:310:y:2023:i:1:p:71-83
    DOI: 10.1016/j.ejor.2023.02.044
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    References listed on IDEAS

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    1. Guo, Shouwei & Kang, Liying, 2010. "Online scheduling of malleable parallel jobs with setup times on two identical machines," European Journal of Operational Research, Elsevier, vol. 206(3), pages 555-561, November.
    2. Jacek Błażewicz & Maciej Machowiak & Jan Węglarz & Mikhail Kovalyov & Denis Trystram, 2004. "Scheduling Malleable Tasks on Parallel Processors to Minimize the Makespan," Annals of Operations Research, Springer, vol. 129(1), pages 65-80, July.
    3. Havill, Jessen T. & Mao, Weizhen, 2008. "Competitive online scheduling of perfectly malleable jobs with setup times," European Journal of Operational Research, Elsevier, vol. 187(3), pages 1126-1142, June.
    4. Wu, Fangfang & Zhang, Xiandong & Chen, Bo, 2023. "An improved approximation algorithm for scheduling monotonic moldable tasks," European Journal of Operational Research, Elsevier, vol. 306(2), pages 567-578.
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