Abstract
As a rule, Top 500 class supercomputers are extensively benchmarked as part of their acceptance testing process. However, barring publicly posted LINPACK / HPCG results, most benchmark results are often inaccessible outside the hosting institution. Moreover, these higher level benchmarks do not provide easy answers to common questions such as "What is the realizable memory bandwidth?"or "What is the launch latency on the accelerator?"To partially address these issues, we executed selected single-node micro-benchmarks - focused on latencies and memory bandwidth - on every US Department of Energy system above rank 150 of the June 2023 Top 500 list, excepting NERSC's Cori and ORNL's Frontier TDS (now decommissioned or repurposed). We hope to provide an easy "first stop"reference for users of current Top 500 systems and inspire users and administrators of other Top 500 systems to similarly compile and make available benchmark results for their systems.
Original language | English |
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Title of host publication | Proceedings of 2023 SC Workshops of the International Conference on High Performance Computing, Network, Storage, and Analysis, SC Workshops 2023 |
Publisher | Association for Computing Machinery |
Pages | 1298-1305 |
Number of pages | 8 |
ISBN (Electronic) | 9798400707858 |
DOIs | |
State | Published - Nov 12 2023 |
Event | 2023 International Conference on High Performance Computing, Network, Storage, and Analysis, SC Workshops 2023 - Denver, United States Duration: Nov 12 2023 → Nov 17 2023 |
Publication series
Name | ACM International Conference Proceeding Series |
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Conference
Conference | 2023 International Conference on High Performance Computing, Network, Storage, and Analysis, SC Workshops 2023 |
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Country/Territory | United States |
City | Denver |
Period | 11/12/23 → 11/17/23 |
Funding
This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. This research used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. This research made use of Idaho National Laboratory computing resources which are supported by the Office of Nuclear Energy of the U.S. Department of Energy and the Nuclear Science User Facilities under Contract No. DE-AC07-05ID14517. This research used resources of the Argonne Leadership Computing Facility, a DOE Office of Science User Facility supported under Contract No. DE-AC02-06CH11357. This research used resources of the Los Alamos National Laboratory, supported by the US Department of Energy under contract No 89233218CNA000001. The authors would also like to thank Christopher Knight of Argonne National Laboratory and James Elliott of Sandia National Laboratories for consulting on configuration and run options on various machines. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA-0003525. This written work is authored by an employee of NTESS. The employee, not NTESS, owns the right, title and interest in and to the written work and is responsible for its contents. Any subjective views or opinions that might be expressed in the written work do not necessarily represent the views of the U.S. Government. This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the U.S. Department of Energy. The publisher acknowledges that the U.S. Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this written work or allow others to do so, for U.S. Government purposes. The DOE will provide public access to results of federally sponsored research in accordance with the DOE Public Access Plan.
Keywords
- high performance computing
- micro-benchmarking
- supercomputing
- top500