Abstract
Radio frequency field and particle interaction is of critical importance in modern synchrotrons. Accelerator Physics Emulation System (APES) is a C++ code written with the purpose of simulating the particle dynamics in ring-shaped accelerators. During the tracking process, the particles interact with each other indirectly through the EM field excited by the charged particles in the RF cavity. This a hot spot in the algorithm that takes up roughly 90% of the execution time. We show how a set of well-known code restructuring and algorithmic changes coupled with advancements in modern compiler technology can bring down the Ninja gap to provide more than 7x performance improvements. These changes typically require low programming effort, as compared to the very high effort in producing Ninja code.
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References
Zotter, B.W., Kheifets, S.: Impedances and Wakes in High Energy Particle Accelerators. ISBN 978-981-02-2626-8
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http://www.nersc.gov/users/computational-systems/cori/configuration
Acknowledgements
This work is supported by Brookhaven Science Associates, LLC under contract No. DE-AC02-98CH10886 with the US DOE. Part of this work was performed at the 2018 Intel Knights Landing Hack-a-thon hosted by Brookhaven National Laboratory (BNL), which was partially supported by the HEP Center for Computational Excellence (KA24001022), the SOLLVE Exascale Computing Project (17-SC-20-SC), and the Office of Science of the U.S. Department of Energy under Contract No. DE-SC0012704. Authors would like to thank the Intel engineers who helped during the hack-a-thon. This work is partially supported by the ASCAR Office in the DOE, Office of Science, under contract number DE-AC02-05CH11231, and used the resources of National Energy Scientific Computing Center (NERSC).
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Xin, T. et al. (2018). Performance Tuning to Close Ninja Gap for Accelerator Physics Emulation System (APES) on Intel\(^{\textregistered }\) Xeon Phi\(^{\mathrm{TM}}\) Processors. In: de Supinski, B., Valero-Lara, P., Martorell, X., Mateo Bellido, S., Labarta, J. (eds) Evolving OpenMP for Evolving Architectures. IWOMP 2018. Lecture Notes in Computer Science(), vol 11128. Springer, Cham. https://doi.org/10.1007/978-3-319-98521-3_11
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DOI: https://doi.org/10.1007/978-3-319-98521-3_11
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