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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Acknowledgments
Anne Benoit and Yves Robert are with the Institut Universitaire de France (IUF). This work was supported in part by the ANR RESCUE project. The research of Padma Raghavan and Manu Shantharam was supported in part by the U.S. National Science Foundation through grants CCF 0963839, 1018881 and 1319448.
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Aupy, G., Shantharam, M., Benoit, A. et al. Co-scheduling algorithms for high-throughput workload execution. J Sched 19, 627–640 (2016). https://doi.org/10.1007/s10951-015-0445-x
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DOI: https://doi.org/10.1007/s10951-015-0445-x