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Using TAGs to Speed up the ATLAS Analysis Process

In the ATLAS experiment, Tag Data, or short TAG, are event-level metadata -- thumbnail information about events to support efficient identification and selection of events of interest to a given analysis. TAG quantities range from detector status and trigger information to basic physics quantities,...

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Autores principales: Ehrenfeld, W, Buckingham, R, Cranshaw, J, Cuhadar Donszelmann, T, Doherty, T, Gallas, E, Hrivnac, J, Malon, D, Nowak, M, Slater, M, Viegas, F, Vinek, E, Zhang, Q
Lenguaje:eng
Publicado: 2011
Materias:
Acceso en línea:http://cds.cern.ch/record/1321577
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author Ehrenfeld, W
Buckingham, R
Cranshaw, J
Cuhadar Donszelmann, T
Doherty, T
Gallas, E
Hrivnac, J
Malon, D
Nowak, M
Slater, M
Viegas, F
Vinek, E
Zhang, Q
author_facet Ehrenfeld, W
Buckingham, R
Cranshaw, J
Cuhadar Donszelmann, T
Doherty, T
Gallas, E
Hrivnac, J
Malon, D
Nowak, M
Slater, M
Viegas, F
Vinek, E
Zhang, Q
author_sort Ehrenfeld, W
collection CERN
description In the ATLAS experiment, Tag Data, or short TAG, are event-level metadata -- thumbnail information about events to support efficient identification and selection of events of interest to a given analysis. TAG quantities range from detector status and trigger information to basic physics quantities, e. g. the number of loose electrons candidates and kinematic information for a limited number of these candidates sorted by their transverse momentum. The average TAG size per event is around 1kB, which is a factor 100 smaller than the Analysis Object Data (AOD) used for physics analysis. TAGs are primarily produced from AODs and stored in ROOT files. For easier access and usability TAGs are also stored in a database. Queries to the database can produce again TAG files. In a standard ATLAS analysis job, TAGs can be used to preselect events based on the TAG quantities before accessing the full AOD content. This allows for a significant speed up of the processing time. This paper will discuss the different analysis work flows using TAGs and compare them with other analysis work flows within ATLAS. Further, the performance for preselecting events using either directly AODs or TAG files is measured and compared. Peak performance is estimated on a single machine with local disk access, while more realistic performance is estimated using Grid like data access.
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institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2011
record_format invenio
spelling cern-13215772019-09-30T06:29:59Zhttp://cds.cern.ch/record/1321577engEhrenfeld, WBuckingham, RCranshaw, JCuhadar Donszelmann, TDoherty, TGallas, EHrivnac, JMalon, DNowak, MSlater, MViegas, FVinek, EZhang, QUsing TAGs to Speed up the ATLAS Analysis ProcessDetectors and Experimental TechniquesIn the ATLAS experiment, Tag Data, or short TAG, are event-level metadata -- thumbnail information about events to support efficient identification and selection of events of interest to a given analysis. TAG quantities range from detector status and trigger information to basic physics quantities, e. g. the number of loose electrons candidates and kinematic information for a limited number of these candidates sorted by their transverse momentum. The average TAG size per event is around 1kB, which is a factor 100 smaller than the Analysis Object Data (AOD) used for physics analysis. TAGs are primarily produced from AODs and stored in ROOT files. For easier access and usability TAGs are also stored in a database. Queries to the database can produce again TAG files. In a standard ATLAS analysis job, TAGs can be used to preselect events based on the TAG quantities before accessing the full AOD content. This allows for a significant speed up of the processing time. This paper will discuss the different analysis work flows using TAGs and compare them with other analysis work flows within ATLAS. Further, the performance for preselecting events using either directly AODs or TAG files is measured and compared. Peak performance is estimated on a single machine with local disk access, while more realistic performance is estimated using Grid like data access.ATL-SOFT-PROC-2011-003oai:cds.cern.ch:13215772011-01-12
spellingShingle Detectors and Experimental Techniques
Ehrenfeld, W
Buckingham, R
Cranshaw, J
Cuhadar Donszelmann, T
Doherty, T
Gallas, E
Hrivnac, J
Malon, D
Nowak, M
Slater, M
Viegas, F
Vinek, E
Zhang, Q
Using TAGs to Speed up the ATLAS Analysis Process
title Using TAGs to Speed up the ATLAS Analysis Process
title_full Using TAGs to Speed up the ATLAS Analysis Process
title_fullStr Using TAGs to Speed up the ATLAS Analysis Process
title_full_unstemmed Using TAGs to Speed up the ATLAS Analysis Process
title_short Using TAGs to Speed up the ATLAS Analysis Process
title_sort using tags to speed up the atlas analysis process
topic Detectors and Experimental Techniques
url http://cds.cern.ch/record/1321577
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