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Towards Understanding Job Heterogeneity in HPC: A NERSC Case Study
Umeå University, Faculty of Science and Technology, Department of Computing Science.
Umeå University, Faculty of Science and Technology, Department of Computing Science.
Umeå University, Faculty of Science and Technology, Department of Computing Science.
Lawrence Berkeley National Laboratory, USA.
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2016 (English)In: Proceedings of CCGrid 2016 - The 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, 2016, 521-526 p.Conference paper (Refereed)
Abstract [en]

The high performance computing (HPC) scheduling landscape is changing. Increasingly, there are large scientific computations that include high-throughput, data-intensive, and stream-processing compute models. These jobs increase the workload heterogeneity, which presents challenges for classical tightly coupled MPI job oriented HPC schedulers. Thus, it is important to define new analyses methods to understand the heterogeneity of the workload, and its possible effect on the performance of current systems. In this paper, we present a methodology to assess the job heterogeneity in workloads and scheduling queues. We apply the method on the workloads of three current National Energy Research Scientific Computing Center (NERSC) systems in 2014. Finally, we present the results of such analysis, with an observation that heterogeneity might reduce predictability in the jobs’ wait time.

Place, publisher, year, edition, pages
2016. 521-526 p.
National Category
Computer Science
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:umu:diva-135843OAI: oai:DiVA.org:umu-135843DiVA: diva2:1106461
Conference
CCGrid 2016 - The 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing
Available from: 2017-06-07 Created: 2017-06-07 Last updated: 2017-06-07

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