Issue |
EPJ Web of Conf.
Volume 295, 2024
26th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2023)
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Article Number | 11016 | |
Number of page(s) | 8 | |
Section | Heterogeneous Computing and Accelerators | |
DOI | https://doi.org/10.1051/epjconf/202429511016 | |
Published online | 06 May 2024 |
https://doi.org/10.1051/epjconf/202429511016
Parallelizing Air Shower Simulation for Background Characterization in IceCube
1 Dept. of Physics and Wisconsin IceCube Particle Astrophysics Center, University of Wisconsin, Madison, WI 53706, USA.
2 Deutsches Elektronen-Synchrotron DESY, Platanenallee 6, 15738 Zeuthen, Germany.
* e-mail: kjmeagher@wisc.edu
Published online: 6 May 2024
The IceCube Neutrino Observatory is a cubic kilometer neutrino telescope located at the Geographic South Pole. For every observed neutrino event, there are over 106 background events caused by cosmic ray air shower muons. In order to properly separate signal from background, it is necessary to produce Monte Carlo simulations of these air showers. Although to-date, IceCube has produced large quantities of background simulation, these studies still remain statistics limited. The first stage of simulation requires heavy CPU usage while the second stage requires heavy GPU usage. Processing both of these stages on the same node will result in an underutilized GPU but using different nodes will encounter bandwidth bottlenecks. Furthermore, due to the power-law energy spectrum of cosmic rays, the memory footprint of the detector response often exceeded the limit in unpredictable ways. This proceeding presents new client–server code which parallelizes the first stage onto multiple CPUs on the same node and then passes it on to the GPU for photon propagation. This results in GPU utilization of greater than 90% as well as more predictable memory usage and an overall factor of 20 improvement in speed over previous techniques.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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