Issue |
EPJ Web Conf.
Volume 245, 2020
24th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2019)
|
|
---|---|---|
Article Number | 01027 | |
Number of page(s) | 7 | |
Section | 1 - Online and Real-time Computing | |
DOI | https://doi.org/10.1051/epjconf/202024501027 | |
Published online | 16 November 2020 |
https://doi.org/10.1051/epjconf/202024501027
The ALICE O2 data quality control system
1
CERN, Experimental Physics Department, Geneva, Switzerland
2
Faculty of Electrical Engineering, Automatics, Computer Science and Electronics, AGH University of Science and Technology, Cracow, Poland
* e-mail: piotr.konopka@cern.ch
** e-mail: barthelemy.von.haller@cern.ch
Published online: 16 November 2020
The ALICE Experiment at CERN LHC (Large Hadron Collider) is undertaking a major upgrade during LHC Long Shutdown 2 in 2019–2021. The raw data input from the ALICE detectors will then increase a hundredfold, up to 3.5 TB/s. In order to cope with such a large amount of data, a new online-offline computing system, called O2, will be deployed.
One of the key software components of the O2 system will be the data Quality Control (QC) that replaces the existing online Data Quality Monitoring and offline Quality Assurance. It involves the gathering, the analysis by user-defined algorithms and the visualization of monitored data, in both the synchronous and asynchronous parts of the O2 system.
This paper presents the architecture and design, as well as the latest and upcoming features, of the ALICE O2 QC. The results of the extensive benchmarks which have been carried out for each component of the system are later summarized. Finally, the adoption of this tool amongst the ALICE Collaboration and the measures taken to develop, in synergy with their respective teams, efficient monitoring modules for the detectors, are discussed.
© The Authors, published by EDP Sciences, 2020
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.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.