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RDataFrame: Easy parallel ROOT analysis at 100 threads

The Physics programmes of LHC Run III and HL-LHC challenge the HEP community. The volume of data to be handled is unprecedented at every step of the data processing chain: analysis is no exception. Physicists must be provided with first-class analysis tools which are easy to use, exploit bleeding ed...

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Autores principales: Piparo, Danilo, Canal, Philippe, Guiraud, Enrico, Valls Pla, Xavier, Ganis, Gerardo, Amadio, Guilherme, Naumann, Axel, Tejedor, Enric
Lenguaje:eng
Publicado: 2019
Materias:
Acceso en línea:https://dx.doi.org/10.1051/epjconf/201921406029
http://cds.cern.ch/record/2699587
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author Piparo, Danilo
Canal, Philippe
Guiraud, Enrico
Valls Pla, Xavier
Ganis, Gerardo
Amadio, Guilherme
Naumann, Axel
Tejedor, Enric
author_facet Piparo, Danilo
Canal, Philippe
Guiraud, Enrico
Valls Pla, Xavier
Ganis, Gerardo
Amadio, Guilherme
Naumann, Axel
Tejedor, Enric
author_sort Piparo, Danilo
collection CERN
description The Physics programmes of LHC Run III and HL-LHC challenge the HEP community. The volume of data to be handled is unprecedented at every step of the data processing chain: analysis is no exception. Physicists must be provided with first-class analysis tools which are easy to use, exploit bleeding edge hardware technologies and allow to seamlessly express parallelism. This document discusses the declarative analysis engine of ROOT, RDataFrame, and gives details about how it allows to profitably exploit commodity hardware as well as high-end servers and manycore accelerators thanks to the synergy with the existing parallelised ROOT components. Real-life analyses of LHC experiments’ data expressed in terms of RDataFrame are presented, highlighting the programming model provided to express them in a concise and powerful way. The recent developments which make RDataFrame a lightweight data processing framework are described, such as callbacks and I/O capabilities. Finally, the flexibility of RDataFrame and its ability to read data formats other than ROOT’s are characterised, as an example it is discussed how RDataFrame can directly read and analyse LHCb’s raw data format MDF.
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institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2019
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spelling oai-inspirehep.net-17612912022-08-10T12:27:05Zdoi:10.1051/epjconf/201921406029http://cds.cern.ch/record/2699587engPiparo, DaniloCanal, PhilippeGuiraud, EnricoValls Pla, XavierGanis, GerardoAmadio, GuilhermeNaumann, AxelTejedor, EnricRDataFrame: Easy parallel ROOT analysis at 100 threadsComputing and ComputersParticle Physics - ExperimentThe Physics programmes of LHC Run III and HL-LHC challenge the HEP community. The volume of data to be handled is unprecedented at every step of the data processing chain: analysis is no exception. Physicists must be provided with first-class analysis tools which are easy to use, exploit bleeding edge hardware technologies and allow to seamlessly express parallelism. This document discusses the declarative analysis engine of ROOT, RDataFrame, and gives details about how it allows to profitably exploit commodity hardware as well as high-end servers and manycore accelerators thanks to the synergy with the existing parallelised ROOT components. Real-life analyses of LHC experiments’ data expressed in terms of RDataFrame are presented, highlighting the programming model provided to express them in a concise and powerful way. The recent developments which make RDataFrame a lightweight data processing framework are described, such as callbacks and I/O capabilities. Finally, the flexibility of RDataFrame and its ability to read data formats other than ROOT’s are characterised, as an example it is discussed how RDataFrame can directly read and analyse LHCb’s raw data format MDF.FERMILAB-CONF-19-550-SCDoai:inspirehep.net:17612912019
spellingShingle Computing and Computers
Particle Physics - Experiment
Piparo, Danilo
Canal, Philippe
Guiraud, Enrico
Valls Pla, Xavier
Ganis, Gerardo
Amadio, Guilherme
Naumann, Axel
Tejedor, Enric
RDataFrame: Easy parallel ROOT analysis at 100 threads
title RDataFrame: Easy parallel ROOT analysis at 100 threads
title_full RDataFrame: Easy parallel ROOT analysis at 100 threads
title_fullStr RDataFrame: Easy parallel ROOT analysis at 100 threads
title_full_unstemmed RDataFrame: Easy parallel ROOT analysis at 100 threads
title_short RDataFrame: Easy parallel ROOT analysis at 100 threads
title_sort rdataframe: easy parallel root analysis at 100 threads
topic Computing and Computers
Particle Physics - Experiment
url https://dx.doi.org/10.1051/epjconf/201921406029
http://cds.cern.ch/record/2699587
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AT canalphilippe rdataframeeasyparallelrootanalysisat100threads
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AT ganisgerardo rdataframeeasyparallelrootanalysisat100threads
AT amadioguilherme rdataframeeasyparallelrootanalysisat100threads
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