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Co-scheduling algorithms for high-throughput workload execution

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.
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Submitted on : Thursday, January 7, 2016 - 2:55:58 PM
Last modification on : Thursday, September 29, 2022 - 2:58:07 PM
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Guillaume Aupy, Manu Shantharam, Anne Benoit, Yves Robert, Padma Raghavan. Co-scheduling algorithms for high-throughput workload execution. Journal of Scheduling, Springer Verlag, 2016, Journal of Scheduling, 19 (6), pp.627-640. ⟨10.1007/s10951-015-0445-x⟩. ⟨hal-01252366⟩



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