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Frameworks to monitor and predict rates and resource usage in the ATLAS High Level Trigger

The ATLAS High Level Trigger Farm consists of around 40,000 CPU cores which filter events at an input rate of up to 100 kHz. A costing framework is built into the high level trigger thus enabling detailed monitoring of the system and allowing for data-driven predictions to be made utilising speciali...

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Autor principal: Martin, Tim
Lenguaje:eng
Publicado: 2016
Materias:
Acceso en línea:https://dx.doi.org/10.1088/1742-6596/898/3/032007
http://cds.cern.ch/record/2239816
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author Martin, Tim
author_facet Martin, Tim
author_sort Martin, Tim
collection CERN
description The ATLAS High Level Trigger Farm consists of around 40,000 CPU cores which filter events at an input rate of up to 100 kHz. A costing framework is built into the high level trigger thus enabling detailed monitoring of the system and allowing for data-driven predictions to be made utilising specialist datasets. An overview is presented in to how ATLAS collects in-situ monitoring data on CPU usage during the trigger execution, and how these data are processed to yield both low level monitoring of individual selection-algorithms and high level data on the overall performance of the farm. For development and prediction purposes, ATLAS uses a special ‘Enhanced Bias’ event selection. This mechanism is explained along with how it is used to profile expected resource usage and output event rate of new physics selections, before they are executed on the actual high level trigger farm.
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institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2016
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spelling cern-22398162019-10-15T15:18:41Zdoi:10.1088/1742-6596/898/3/032007http://cds.cern.ch/record/2239816engMartin, TimFrameworks to monitor and predict rates and resource usage in the ATLAS High Level TriggerParticle Physics - ExperimentThe ATLAS High Level Trigger Farm consists of around 40,000 CPU cores which filter events at an input rate of up to 100 kHz. A costing framework is built into the high level trigger thus enabling detailed monitoring of the system and allowing for data-driven predictions to be made utilising specialist datasets. An overview is presented in to how ATLAS collects in-situ monitoring data on CPU usage during the trigger execution, and how these data are processed to yield both low level monitoring of individual selection-algorithms and high level data on the overall performance of the farm. For development and prediction purposes, ATLAS uses a special ‘Enhanced Bias’ event selection. This mechanism is explained along with how it is used to profile expected resource usage and output event rate of new physics selections, before they are executed on the actual high level trigger farm.ATL-DAQ-PROC-2016-044oai:cds.cern.ch:22398162016-12-23
spellingShingle Particle Physics - Experiment
Martin, Tim
Frameworks to monitor and predict rates and resource usage in the ATLAS High Level Trigger
title Frameworks to monitor and predict rates and resource usage in the ATLAS High Level Trigger
title_full Frameworks to monitor and predict rates and resource usage in the ATLAS High Level Trigger
title_fullStr Frameworks to monitor and predict rates and resource usage in the ATLAS High Level Trigger
title_full_unstemmed Frameworks to monitor and predict rates and resource usage in the ATLAS High Level Trigger
title_short Frameworks to monitor and predict rates and resource usage in the ATLAS High Level Trigger
title_sort frameworks to monitor and predict rates and resource usage in the atlas high level trigger
topic Particle Physics - Experiment
url https://dx.doi.org/10.1088/1742-6596/898/3/032007
http://cds.cern.ch/record/2239816
work_keys_str_mv AT martintim frameworkstomonitorandpredictratesandresourceusageintheatlashighleveltrigger