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Co-scheduling algorithms for high-throughput workload execution

Author

Listed:
  • Guillaume Aupy

    (LIP, ENS Lyon)

  • Manu Shantharam

    (San Diego Supercomputer Center)

  • Anne Benoit

    (LIP, ENS Lyon)

  • Yves Robert

    (LIP, ENS Lyon
    University of Tennessee)

  • Padma Raghavan

    (Pennsylvania State University)

Abstract

This paper investigates co-scheduling algorithms for processing a set of parallel applications. Instead of executing each application one by one, using a maximum degree of parallelism for each of them, we aim at scheduling several applications concurrently. We partition the original application set into a series of packs, which are executed one by one. A pack comprises several applications, each of them with an assigned number of processors, with the constraint that the total number of processors assigned within a pack does not exceed the maximum number of available processors. The objective is to determine a partition into packs, and an assignment of processors to applications, that minimize the sum of the execution times of the packs. We thoroughly study the complexity of this optimization problem, and propose several heuristics that exhibit very good performance on a variety of workloads, whose application execution times model profiles of parallel scientific codes. We show that co-scheduling leads to faster workload completion time (40 % improvement on average over traditional scheduling) and to faster response times (50 % improvement). Hence, co-scheduling increases system throughput and saves energy, leading to significant benefits from both the user and system perspectives.

Suggested Citation

  • Guillaume Aupy & Manu Shantharam & Anne Benoit & Yves Robert & Padma Raghavan, 2016. "Co-scheduling algorithms for high-throughput workload execution," Journal of Scheduling, Springer, vol. 19(6), pages 627-640, December.
    • Handle: RePEc:spr:jsched:v:19:y:2016:i:6:d:10.1007_s10951-015-0445-x
    DOI: 10.1007/s10951-015-0445-x
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    References listed on IDEAS

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