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Leveraging state-of-the-art engines for large-scale data analysis in High Energy Physics

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Leveraging state-of-the-art engines for large-scale data analysis in High Energy Physics

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dc.contributor.author Padulano, Vincenzo Eduardo es_ES
dc.contributor.author Kabadzhov, Ivan Donchev es_ES
dc.contributor.author Tejedor Saavedra, Enric es_ES
dc.contributor.author Guiraud, Enrico es_ES
dc.contributor.author Alonso-Jordá, Pedro es_ES
dc.date.accessioned 2023-03-01T19:02:10Z
dc.date.available 2023-03-01T19:02:10Z
dc.date.issued 2023-02-10 es_ES
dc.identifier.issn 1570-7873 es_ES
dc.identifier.uri http://hdl.handle.net/10251/192206
dc.description.abstract [EN] The Large Hadron Collider (LHC) at CERN has generated a vast amount of information from physics events, reaching peaks of TB of data per day which are then sent to large storage facilities. Traditionally, data processing workflows in the High Energy Physics (HEP) field have leveraged grid computing resources. In this context, users have been responsible for manually parallelising the analysis, sending tasks to computing nodes and aggregating the partial results. Analysis environments in this field have had a common building block in the ROOT software framework. This is the de facto standard tool for storing, processing and visualising HEP data. ROOT offers a modern analysis tool called RDataFrame, which can parallelise computations from a single machine to a distributed cluster while hiding most of the scheduling and result aggregation complexity from users. This is currently done by leveraging Apache Spark as the distributed execution engine, but other alternatives are being explored by HEP research groups. Notably, Dask has rapidly gained popularity thanks to its ability to interface with batch queuing systems, widespread in HEP grid computing facilities. Furthermore, future upgrades of the LHC are expected to bring a dramatic increase in data volumes. This paper presents a novel implementation of the Dask backend for the distributed RDataFrame tool in order to address the aforementioned future trends. The scalability of the tool with both the new backend and the already available Spark backend is demonstrated for the first time on more than two thousand cores, testing a real HEP analysis. es_ES
dc.description.sponsorship Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work benefited from the support of grant PID2020-113656RBC22 funded by Ministerio de Ciencia e Innovacion (Spain) MCIN/AEI/10.13039/501100011033. es_ES
dc.language Inglés es_ES
dc.publisher Springer-Verlag es_ES
dc.relation.ispartof Journal of Grid Computing es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Root es_ES
dc.subject High energy physics es_ES
dc.subject Distributed computing es_ES
dc.subject Dask es_ES
dc.subject Spark es_ES
dc.subject.classification CIENCIAS DE LA COMPUTACION E INTELIGENCIA ARTIFICIAL es_ES
dc.title Leveraging state-of-the-art engines for large-scale data analysis in High Energy Physics es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s10723-023-09645-2 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-113656RB-C22/ES/COMPUTACION Y COMUNICACIONES DE ALTAS PRESTACIONES CONSCIENTES DEL CONSUMO ENERGETICO. APLICACIONES AL APRENDIZAJE PROFUNDO COMPUTACIONAL - UPV/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica es_ES
dc.description.bibliographicCitation Padulano, VE.; Kabadzhov, ID.; Tejedor Saavedra, E.; Guiraud, E.; Alonso-Jordá, P. (2023). Leveraging state-of-the-art engines for large-scale data analysis in High Energy Physics. Journal of Grid Computing. 21:1-21. https://doi.org/10.1007/s10723-023-09645-2 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1007/s10723-023-09645-2 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 21 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 21 es_ES
dc.relation.pasarela S\482341 es_ES
dc.contributor.funder Agencia Estatal de Investigación es_ES
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