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Disallowing Same-program Co-schedules to Improve Efficiency in Quad-core Servers
University West, Department of Engineering Science, Division of Computer, Electrical and Surveying Engineering.ORCID iD: 0000-0001-7232-0079
University West, Department of Engineering Science, Division of Computer, Electrical and Surveying Engineering.ORCID iD: 0000-0003-0589-8086
2017 (English)In: Proceedings of the Joined Workshops COSH 2017 and VisorHPC 2017 / [ed] Clauss, Carsten; Lankes, Stefan; Trinitis, Carsten; Weidendorfer, Josef, 2017, p. 1-7Conference paper, Published paper (Refereed)
Abstract [en]

Programs running on different cores in a multicore server are often forced to share resources like off-chip memory,caches, I/O devices, etc. This resource sharing often leads to degraded performance, a slowdown, for the program sthat share the resources. A job scheduler can improve performance by co-scheduling programs that use different resources on the same server. The most common approachto solve this co-scheduling problem has been to make job schedulers resource aware, finding ways to characterize and quantify a program’s resource usage. We have earlier suggested a simple, program and resource agnostic, scheme as a stepping stone to solving this problem: Avoid Terrible Twins, i.e., avoid co-schedules that contain several instances from the same program. This scheme showed promising results when applied to dual-core servers. In this paper, we extend the analysis and evaluation to also cover quad-core servers. We present a probabilistic model and empirical data that show that execution slowdowns get worse as the number of instances of the same program increases. Our scheduling simulations show that if all co-schedules containing multiple instances of the same program are removed, the average slowdown is decreased from 54% to 46% and that the worst case slowdown is decreased from 173% to 108%.

Place, publisher, year, edition, pages
2017. p. 1-7
Keywords [en]
Co-scheduling; Same Process;Scheduling; Allocation; Multicore; Slowdown; Cluster; Cloud
National Category
Computer Systems
Research subject
ENGINEERING, Computer engineering
Identifiers
URN: urn:nbn:se:hv:diva-10620DOI: 10.14459/2017md1344414ISBN: 978-3-00-055564-0 (print)OAI: oai:DiVA.org:hv-10620DiVA, id: diva2:1066747
Conference
HIPEAC 2017, 2st COSH Workshop on Co-Scheduling of HPC Applications
Available from: 2017-01-19 Created: 2017-01-19 Last updated: 2019-01-04Bibliographically approved

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de Blanche, AndreasLundqvist, Thomas

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