Abstract
The assessment of application performance is a fundamental task in high-performance computing (HPC). The OpenSHMEM Benchmark (OSB) suite is a collection of micro-benchmarks and mini-applications/compute kernels that have been ported to use OpenSHMEM. Some, like the NPB OpenSHMEM benchmarks, have been published before while most others have been used for evaluations but never formally introduced or discussed. This suite puts them together and is useful for assessing the performance of different use cases of OpenSHMEM. This offers system implementers a useful means of measuring performance and assessing the effects of new features as well as implementation strategies. The suite is also useful for application developers to assess the performance of the growing number of OpenSHMEM implementations that are emerging. In this paper, we describe the current set of codes available within the OSB suite, how they are intended to be used, and, where possible, a snapshot of their behavior on one of the OpenSHMEM implementations available to us. We also include detailed descriptions of every benchmark and kernel, focusing on how OpenSHMEM was used. This includes details on the enhancements we made to the benchmarks to support multithreaded variants. We encourage the OpenSHMEM community to use, review, and provide feedback on the benchmarks.
Original language | English |
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Title of host publication | OpenSHMEM and Related Technologies. OpenSHMEM in the Era of Extreme Heterogeneity - 5th Workshop, OpenSHMEM 2018, Revised Selected Papers |
Editors | Swaroop Pophale, Neena Imam, Ferrol Aderholdt, Manjunath Gorentla Venkata |
Publisher | Springer Verlag |
Pages | 202-216 |
Number of pages | 15 |
ISBN (Print) | 9783030049171 |
DOIs | |
State | Published - 2019 |
Event | 5th Workshop on OpenSHMEM and Related Technologies, 2018 - Baltimore, United States Duration: Aug 21 2018 → Aug 23 2018 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 11283 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 5th Workshop on OpenSHMEM and Related Technologies, 2018 |
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Country/Territory | United States |
City | Baltimore |
Period | 08/21/18 → 08/23/18 |
Funding
Acknowledgements. This research was supported by the United States Department of Defense (DoD) and Computational Research and Development Programs at Oak Ridge National Laboratory. This work was sponsored by the U.S. Department of Energy’s Office of Advanced Scientific Computing Research. 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 work was sponsored by the U.S. Department of Energy’s Office of Advanced Scientific Computing Research. This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/ downloads/doe-public-access-plan). 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 work was sponsored by the U.S. Department of Energy’s Office of Advanced Scientific Computing Research. This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/ downloads/doe-public-access-plan). 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 was supported by the United States Department of Defense (DoD) and Computational Research and Development Programs at Oak Ridge National Laboratory. This work was sponsored by the U.S. Department of Energy’s Office of Advanced Scientific Computing Research. 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.