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Towards understandi...
Towards understanding HPC users and systems : a NERSC case study
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- Rodrigo, Gonzalo P., 1980- (author)
- Umeå universitet,Institutionen för datavetenskap,Distributed Systems
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- Östberg, Per-Olov (author)
- Umeå universitet,Institutionen för datavetenskap,Distributed Systems
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- Elmroth, Erik (author)
- Umeå universitet,Institutionen för datavetenskap,Distributed Systems
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- Antypas, Katie (author)
- Lawrence Berkeley National Lab, USA
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- Gerber, Richard (author)
- Lawrence Berkeley National Lab, USA
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- Ramakrishnan, Lavanya (author)
- Lawrence Berkeley National Lab, USA
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(creator_code:org_t)
- Elsevier, 2018
- 2018
- English.
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In: Journal of Parallel and Distributed Computing. - : Elsevier. - 0743-7315 .- 1096-0848. ; 111, s. 206-221
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Abstract
Subject headings
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- High performance computing (HPC) scheduling landscape currently faces new challenges due to the changes in the workload. Previously, HPC centers were dominated by tightly coupled MPI jobs. HPC workloads increasingly include high-throughput, data-intensive, and stream-processing applications. As a consequence, workloads are becoming more diverse at both application and job levels, posing new challenges to classical HPC schedulers. There is a need to understand the current HPC workloads and their evolution to facilitate informed future scheduling research and enable efficient scheduling in future HPC systems.In this paper, we present a methodology to characterize workloads and assess their heterogeneity, at a particular time period and its evolution over time. We apply this methodology to the workloads of three systems (Hopper, Edison, and Carver) at the National Energy Research Scientific Computing Center (NERSC). We present the resulting characterization of jobs, queues, heterogeneity, and performance that includes detailed information of a year of workload (2014) and evolution through the systems' lifetime (2010–2014).
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Keyword
- Workload analysis
- Supercomputer
- HPC
- Scheduling
- NERSC
- Heterogeneity
- k-means
- business data processing
- administrativ databehandling
Publication and Content Type
- ref (subject category)
- art (subject category)
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