| Issue |
EPJ Web Conf.
Volume 337, 2025
27th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2024)
|
|
|---|---|---|
| Article Number | 01210 | |
| Number of page(s) | 8 | |
| DOI | https://doi.org/10.1051/epjconf/202533701210 | |
| Published online | 07 October 2025 | |
https://doi.org/10.1051/epjconf/202533701210
Optimization of distributed compute resources utilization in the CMS Global Pool
1 University of California San Diego, La Jolla, CA, USA
2 Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT), Madrid, Spain
3 Port d’Informació Cientifica (PIC), Barcelona, Spain
4 Fermi National Accelerator Laboratory, Batavia, IL, USA
5 European Organization for Nuclear Research (CERN), Geneva, Switzerland
* e-mail: marco.mascheroni@cern.ch
** e-mail: aperez@pic.es
Published online: 7 October 2025
The CMS Submission Infrastructure is the primary system for managing computing resources for CMS workflows, including data processing, simulation, and analysis. It integrates geographically distributed resources from Grid, HPC, and cloud providers into federated pools managed by HTCondor and Glidein- WMS, for a total of around 500k CPU cores. This system dynamically manages workloads based on priorities defined by the collaboration. Additionally, CMS scheduling strategies must be flexible to handle multiple concurrent workloads while considering changing processing demands and resource availability from various providers.
Efficient utilization of vast amounts of distributed compute resources is a key element for the success of the scientific programs of the LHC experiments. Optimizing the system is essential to maximize resource efficiency and fully utilize the distributed computing power. The CMS Submission Infrastructure team thus systematically investigates sources of inefficiency in workload scheduling to reduce their impact. In addition, a strategy of pilot overloading has been introduced to compensate for other inefficiency sources, thereby optimizing resource utilization and enhancing computational throughput.
© The Authors, published by EDP Sciences, 2025
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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