NDLI: Co-scheduling algorithms for high-throughput workload execution

Content Provider	Springer Nature Link
Author	Aupy, Guillaume Shantharam, Manu Beit, Anne Robert, Yves Raghavan, Padma
Copyright Year	2015
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.
Starting Page	627
Ending Page	640
Page Count	14
File Format	PDF
ISSN	10946136
Journal	Journal of Scheduling
Volume Number	19
Issue Number	6
e-ISSN	10991425
Language	English
Publisher	Springer US
Publisher Date	2015-08-30
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Operation Research/Decision Theory Calculus of Variations and Optimal Control; Optimization Optimization Artificial Intelligence (incl. Robotics) Supply Chain Management
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Management Science and Operations Research Engineering Software

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