Abstract
We perform a scaling and performance portability study of the electrostatic particle-in-cell scheme for plasma physics applications through a set of mini-apps we name “Alpine”, which can make use of exascale computing capabilities. The mini-apps are based on IPPL, a framework that is designed around performance portable and dimensionality independent particles and fields. We benchmark the simulations with varying parameters, such as grid resolutions (5123 to 20483) and number of simulation particles (109 to 1011), with the following mini-apps: weak and strong Landau damping, bump-on-tail and two-stream instabilities, and the dynamics of an electron bunch in a charge-neutral Penning trap. We show strong and weak scaling and analyze the performance of different components on several pre-exascale architectures, such as Piz-Daint, Cori, Summit, and Perlmutter. While the scaling and portability study helps to identify the performance critical components of the particle-in-cell scheme on the current state-of-the-art computing architectures, the mini-apps by themselves can be used to develop new algorithms and optimize their high performance implementations targeting exascale architectures.
| Original language | English |
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| Title of host publication | 2024 SIAM Conference on Parallel Processing for Scientific Computing, PP 2024 |
| Editors | Michael Bader, Anshu Dubey, Bethany Lusch |
| Publisher | Society for Industrial and Applied Mathematics Publications |
| Pages | 26-38 |
| Number of pages | 13 |
| ISBN (Electronic) | 9781713893479 |
| State | Published - 2024 |
| Event | 22nd SIAM Conference on Parallel Processing for Scientific Computing, PP 2024 - Baltimore, United States Duration: Mar 5 2024 → Mar 8 2024 |
Publication series
| Name | 2024 SIAM Conference on Parallel Processing for Scientific Computing, PP 2024 |
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Conference
| Conference | 22nd SIAM Conference on Parallel Processing for Scientific Computing, PP 2024 |
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| Country/Territory | United States |
| City | Baltimore |
| Period | 03/5/24 → 03/8/24 |
Funding
The authors would like to thank the Kokkos team for helping us with all the queries during the development of IPPL and Alpine. We would like to thank Sonali Mayani for many fruitful discussions during the course of this project. We would also like to thank Marc Caubet Serrabou from PSI for his help with all the installations during the development of IPPL. This project has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No. 701647 and from the United States National Science Foundation under Grant No. PHY-1820852. This research used resources of the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility located at Lawrence Berkeley National Laboratory, operated under Contract No. DE-AC02-05CH11231 using NERSC award ASCRERCAPM888. We acknowledge access to Piz Daint at the Swiss National Supercomputing Centre, Switzerland under the PSI’s share with the project IDs psi07 and psigpu. Finally, this research also 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.