| Issue |
EPJ Web Conf.
Volume 251, 2021
25th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2021)
|
|
|---|---|---|
| Article Number | 02055 | |
| Number of page(s) | 9 | |
| Section | Distributed Computing, Data Management and Facilities | |
| DOI | https://doi.org/10.1051/epjconf/202125102055 | |
| Published online | 23 August 2021 | |
https://doi.org/10.1051/epjconf/202125102055
Reaching new peaks for the future of the CMS HTCondor Global Pool
1 Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT), Madrid, Spain
2 Port d’Informació Cientifica (PIC), Barcelona, Spain
3 University of California San Diego, La Jolla, CA, USA
4 Fermi National Accelerator Laboratory, Batavia, IL, USA
5 National Centre for Physics, Islamabad, Pakistan
6 University of Notre Dame, Notre Dame, IN, USA
7 European Organization for Nuclear Research, Meyrin, Switzerland
* e-mail: aperez@pic.es
Published online: 23 August 2021
The CMS experiment at CERN employs a distributed computing infrastructure to satisfy its data processing and simulation needs. The CMS Submission Infrastructure team manages a dynamic HTCondor pool, aggregating mainly Grid clusters worldwide, but also HPC, Cloud and opportunistic resources. This CMS Global Pool, which currently involves over 70 computing sites worldwide and peaks at 350k CPU cores, is employed to successfully manage the simultaneous execution of up to 150k tasks. While the present infrastructure is sufficient to harness the current computing power scales, CMS latest estimates predict a noticeable expansion in the amount of CPU that will be required in order to cope with the massive data increase of the High-Luminosity LHC (HL-LHC) era, planned to start in 2027. This contribution presents the latest results of the CMS Submission Infrastructure team in exploring and expanding the scalability reach of our Global Pool, in order to preventively detect and overcome any barriers in relation to the HL-LHC goals, while maintaining high effciency in our workload scheduling and resource utilization.
© The Authors, published by EDP Sciences, 2021
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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