| Issue |
EPJ Web of Conf.
Volume 295, 2024
26th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2023)
|
|
|---|---|---|
| Article Number | 07002 | |
| Number of page(s) | 8 | |
| Section | Facilities and Virtualization | |
| DOI | https://doi.org/10.1051/epjconf/202429507002 | |
| Published online | 06 May 2024 | |
https://doi.org/10.1051/epjconf/202429507002
Accelerating science: The usage of commercial clouds in ATLAS Distributed Computing
1 University of Texas at Arlington, Arlington, TX, USA
2 University of Iowa, Iowa City, IA, USA
3 Brookhaven National Laboratory, Upton, NY, USA
4 CERN, Geneva, Switzerland
5 Ludwig-Maximilians-Universitaet Muenchen, Munich, Germany
6 Max-Planck-Institut für Physik, Munich, Germany
7 Lawrence Berkeley National Laboratory, Berkeley, CA, USA
8 Port d’Informació Científica, Barcelona, Spain
9 University of Massachusetts at Amherst, Amherst, MA, USA
10 DESY, Hamburg, Germany
11 California State University at Fresno, Fresno, CA, USA
* e-mail: fernando.barreiro[at]uta.edu
Published online: 6 May 2024
The ATLAS experiment at CERN is one of the largest scientific machines built to date and will have ever growing computing needs as the Large Hadron Collider collects an increasingly larger volume of data over the next 20 years. ATLAS is conducting R&D projects on Amazon Web Services and Google Cloud as complementary resources for distributed computing, focusing on some of the key features of commercial clouds: lightweight operation, elasticity and availability of multiple chip architectures.
The proof of concept phases have concluded with the cloud-native, vendoragnostic integration with the experiment’s data and workload management frameworks. Google Cloud has been used to evaluate elastic batch computing, ramping up ephemeral clusters of up to O(100k) cores to process tasks requiring quick turnaround. Amazon Web Services has been exploited for the successful physics validation of the Athena simulation software on ARM processors.
We have also set up an interactive facility for physics analysis allowing endusers to spin up private, on-demand clusters for parallel computing with up to 4 000 cores, or run GPU enabled notebooks and jobs for machine learning applications.
The success of the proof of concept phases has led to the extension of the Google Cloud project, where ATLAS will study the total cost of ownership of a production cloud site during 15 months with 10k cores on average, fully integrated with distributed grid computing resources and continue the R&D projects.
© The Authors, published by EDP Sciences, 2024
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