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New challenges for distributed computing at the CMS experiment

The Large Hadron Collider (LHC) experiments soon step into the next period of run-3 data taking with an increased data rate and high pileup requiring an excellent working computing infrastructure. In the future High-Luminosity LHC (HL-LHC) data-taking period, the compute, storage and network facilit...

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Autor principal: Krammer, N
Lenguaje:eng
Publicado: 2020
Materias:
Acceso en línea:https://dx.doi.org/10.1088/1748-0221/15/07/C07038
http://cds.cern.ch/record/2749156
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author Krammer, N
author_facet Krammer, N
author_sort Krammer, N
collection CERN
description The Large Hadron Collider (LHC) experiments soon step into the next period of run-3 data taking with an increased data rate and high pileup requiring an excellent working computing infrastructure. In the future High-Luminosity LHC (HL-LHC) data-taking period, the compute, storage and network facilities have to be further extended by large factors and flexible and sophisticated computing models are essential. New techniques of modern state-of-the-art methods in physics analysis and data science, Deep Learning and Big Data tools, are crucial to handle high-dimensional and more complex problems. Beside flexible cloud computing technologies the usage of High Performance Computing (HPC) at the LHC experiments are explored. In this presentation, I will discuss the LHC run-3 and future HL-LHC runs computing technologies and the utilization of modern physics analysis and data science methods for the increasing and complex demands of large-scale scientific computing.
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institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2020
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spelling oai-inspirehep.net-18107932022-11-17T14:32:50Zdoi:10.1088/1748-0221/15/07/C07038http://cds.cern.ch/record/2749156engKrammer, NNew challenges for distributed computing at the CMS experimentDetectors and Experimental TechniquesThe Large Hadron Collider (LHC) experiments soon step into the next period of run-3 data taking with an increased data rate and high pileup requiring an excellent working computing infrastructure. In the future High-Luminosity LHC (HL-LHC) data-taking period, the compute, storage and network facilities have to be further extended by large factors and flexible and sophisticated computing models are essential. New techniques of modern state-of-the-art methods in physics analysis and data science, Deep Learning and Big Data tools, are crucial to handle high-dimensional and more complex problems. Beside flexible cloud computing technologies the usage of High Performance Computing (HPC) at the LHC experiments are explored. In this presentation, I will discuss the LHC run-3 and future HL-LHC runs computing technologies and the utilization of modern physics analysis and data science methods for the increasing and complex demands of large-scale scientific computing.oai:inspirehep.net:18107932020
spellingShingle Detectors and Experimental Techniques
Krammer, N
New challenges for distributed computing at the CMS experiment
title New challenges for distributed computing at the CMS experiment
title_full New challenges for distributed computing at the CMS experiment
title_fullStr New challenges for distributed computing at the CMS experiment
title_full_unstemmed New challenges for distributed computing at the CMS experiment
title_short New challenges for distributed computing at the CMS experiment
title_sort new challenges for distributed computing at the cms experiment
topic Detectors and Experimental Techniques
url https://dx.doi.org/10.1088/1748-0221/15/07/C07038
http://cds.cern.ch/record/2749156
work_keys_str_mv AT krammern newchallengesfordistributedcomputingatthecmsexperiment