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ATLAS grid workflow performance optimization
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 t...
Autores principales: | , , , , , , , |
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Lenguaje: | eng |
Publicado: |
2018
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Materias: | |
Acceso en línea: | https://dx.doi.org/10.1051/epjconf/201921403021 http://cds.cern.ch/record/2645396 |
_version_ | 1780960439113351168 |
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author | Elmsheuser, Johannes Di Girolamo, Alessandro Limosani, Antonio Schulz, Markus Sciaba, Andrea Valassi, Andrea Filipcic, Andrej Smith, David |
author_facet | Elmsheuser, Johannes Di Girolamo, Alessandro Limosani, Antonio Schulz, Markus Sciaba, Andrea Valassi, Andrea Filipcic, Andrej Smith, David |
author_sort | Elmsheuser, Johannes |
collection | CERN |
description | 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 to significant 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 ATLAS production 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. |
id | cern-2645396 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2018 |
record_format | invenio |
spelling | cern-26453962022-08-10T12:25:18Zdoi:10.1051/epjconf/201921403021http://cds.cern.ch/record/2645396engElmsheuser, JohannesDi Girolamo, AlessandroLimosani, AntonioSchulz, MarkusSciaba, AndreaValassi, AndreaFilipcic, AndrejSmith, DavidATLAS grid workflow performance optimizationParticle Physics - ExperimentThe 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 to significant 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 ATLAS production 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.ATL-SOFT-PROC-2018-014oai:cds.cern.ch:26453962018-10-31 |
spellingShingle | Particle Physics - Experiment Elmsheuser, Johannes Di Girolamo, Alessandro Limosani, Antonio Schulz, Markus Sciaba, Andrea Valassi, Andrea Filipcic, Andrej Smith, David ATLAS grid workflow performance optimization |
title | ATLAS grid workflow performance optimization |
title_full | ATLAS grid workflow performance optimization |
title_fullStr | ATLAS grid workflow performance optimization |
title_full_unstemmed | ATLAS grid workflow performance optimization |
title_short | ATLAS grid workflow performance optimization |
title_sort | atlas grid workflow performance optimization |
topic | Particle Physics - Experiment |
url | https://dx.doi.org/10.1051/epjconf/201921403021 http://cds.cern.ch/record/2645396 |
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