| Issue |
EPJ Web Conf.
Volume 214, 2019
23rd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2018)
|
|
|---|---|---|
| Article Number | 03021 | |
| Number of page(s) | 8 | |
| Section | T3 - Distributed computing | |
| DOI | https://doi.org/10.1051/epjconf/201921403021 | |
| Published online | 17 September 2019 | |
https://doi.org/10.1051/epjconf/201921403021
ATLAS Grid Workflow Performance Optimization
1
Brookhaven National Laboratory,
Upton, NY,
USA
2
CERN,
Geneva,
Switzerland
3
Jozef Stefan Institute,
Ljubljana,
Slovenia
4
University of Sydney,
Sydney,
Australia
* e-mail: johannes.elmsheuser@cern.ch
Published online: 17 September 2019
The CERN ATLAS experiment grid workflow system manages routinely 250 to 500 thousand concurrently running production and analysis jobs to process simulation and detector data. In total more than 370 PB of data is distributed over more than 150 sites in the WLCG. At this scale small improvements in the software and computing performance and workflows can lead tosignificant resource usage gains. ATLAS is reviewing together with CERN IT experts several typical simulation and data processing workloads for potential performance improvements in terms of memory and CPU usage, disk and network I/O. All ATLASproduction and analysis grid jobs are instrumented to collect many performance metrics for detailed statistical studies using modern data analytics tools like ElasticSearch and Kibana. This presentation will review and explain the performance gains of several ATLAS simulation and data processing workflows and present analytics studies of the ATLAS grid workflows.
© The Authors, published by EDP Sciences, 2019
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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