| Issue |
EPJ Web of Conf.
Volume 295, 2024
26th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2023)
|
|
|---|---|---|
| Article Number | 11009 | |
| Number of page(s) | 5 | |
| Section | Heterogeneous Computing and Accelerators | |
| DOI | https://doi.org/10.1051/epjconf/202429511009 | |
| Published online | 06 May 2024 | |
https://doi.org/10.1051/epjconf/202429511009
Evaluation of ARM CPUs for IceCube available through Google Kubernetes Engine
1 University of California San Diego, La Jolla, CA 92093, USA
2 University of Wisconsin–Madison, Madison, WI 53715, USA
* Corresponding author: isfiligoi@sdsc.edu
Published online: 6 May 2024
The IceCube experiment has substantial simulation needs and is in continuous search for the most cost-effective ways to satisfy them. The most CPU-intensive part relies on CORSIKA, a cosmic ray air shower simulation. Historically, IceCube relied exclusively on x86-based CPUs, like Intel Xeon and AMD EPYC, but recently server-class ARM-based CPUs are also becoming available, both on-prem and in the cloud. In this paper we present our experience in running a sample CORSIKA simulation on both ARM and x86 CPUs available through Google Kubernetes Engine (GKE). We used the production binaries for the x86 instances, but had to build the binaries for ARM instances from source code, which turned out to be mostly painless. Our benchmarks show that ARM-based CPUs in GKE were not only the most cost-effective but were also the fastest in absolute terms in all the tested configurations. While the advantage is not drastic, about 20% in cost-effectiveness and less than 10% in absolute terms, it is still large enough to warrant an investment in ARM support for IceCube.
© 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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