Towards understanding HPC users and systems: A NERSC case study

Journal Article · Mon Jan 01 04:00:00 EST 2018 · Journal of Parallel and Distributed Computing

DOI:https://doi.org/10.1016/j.jpdc.2017.09.002· OSTI ID:1463670

Rodrigo, Gonzalo P. ^[1]; �stberg, P. -O. ^[2]; Elmroth, Erik ^[2]; Antypas, Katie ^[3]; Gerber, Richard ^[3]; Ramakrishnan, Lavanya ^[3]

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1. Umeå University, Umeå (Sweden). Department Computing Science; Lawrence Berkeley National Lab Berkeley. (LBNL), CA (United States).
2. Umeå University, Umeå (Sweden). Department Computing Science
3. Lawrence Berkeley National Lab Berkeley. (LBNL), CA (United States).

High performance computing (HPC) scheduling landscape currently faces new challenges due to thechanges in the workload. Previously, HPC centers were dominated by tightly coupled MPI jobs. HPCworkloads increasingly include high-throughput, data-intensive, and stream-processing applications. Asa consequence, workloads are becoming more diverse at both application and job levels, posing newchallenges to classical HPC schedulers. There is a need to understand the current HPC workloads andtheir evolution to facilitate informed future scheduling research and enable efficient scheduling in futureHPC 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 ofthree 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 thatincludes detailed information of a year of workload (2014) and evolution through the systems’ lifetime(2010–2014).

• Research Organization: Lawrence Berkeley National Laboratory, Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC).
• Sponsoring Organization: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
• DOE Contract Number: AC02-05CH11231
• OSTI ID: 1463670
• Journal Information: Journal of Parallel and Distributed Computing, Journal Name: Journal of Parallel and Distributed Computing Journal Issue: C Vol. 111; ISSN 0743-7315
• Country of Publication: United States
• Language: English