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Evolution of the energy efficiency of LHCb’s real-time processing
The upgraded LHCb detector, due to start datataking in 2022, will have to process an average data rate of 4 TB/s in real time. Because LHCb’s physics objectives require that the full detector information for every LHC bunch crossing is read out and made available for real-time processing, this bandw...
Autores principales: | , , , , , , , , , |
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Lenguaje: | eng |
Publicado: |
2021
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Materias: | |
Acceso en línea: | https://dx.doi.org/10.1051/epjconf/202125104009 http://cds.cern.ch/record/2773126 |
_version_ | 1780971488110706688 |
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author | Aaij, Roel Cámpora Pérez, Daniel Hugo Colombo, Tommaso Fitzpatrick, Conor Gligorov, Vladimir Vava Hennequin, Arthur Neufeld, Niko Nolte, Niklas Schwemmer, Rainer Vom Bruch, Dorothea |
author_facet | Aaij, Roel Cámpora Pérez, Daniel Hugo Colombo, Tommaso Fitzpatrick, Conor Gligorov, Vladimir Vava Hennequin, Arthur Neufeld, Niko Nolte, Niklas Schwemmer, Rainer Vom Bruch, Dorothea |
author_sort | Aaij, Roel |
collection | CERN |
description | The upgraded LHCb detector, due to start datataking in 2022, will have to process an average data rate of 4 TB/s in real time. Because LHCb’s physics objectives require that the full detector information for every LHC bunch crossing is read out and made available for real-time processing, this bandwidth challenge is equivalent to that of the ATLAS and CMS HL-LHC software read-out, but deliverable five years earlier. Over the past six years, the LHCb collaboration has undertaken a bottom-up rewrite of its software infrastructure, pattern recognition, and selection algorithms to make them better able to efficiently exploit modern highly parallel computing architectures. We review the impact of this reoptimization on the energy efficiency of the realtime processing software and hardware which will be used for the upgrade of the LHCb detector. We also review the impact of the decision to adopt a hybrid computing architecture consisting of GPUs and CPUs for the real-time part of LHCb’s future data processing. We discuss the implications of these results on how LHCb’s real-time power requirements may evolve in the future, particularly in the context of a planned second upgrade of the detector. |
id | cern-2773126 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2021 |
record_format | invenio |
spelling | cern-27731262023-10-12T05:28:42Zdoi:10.1051/epjconf/202125104009http://cds.cern.ch/record/2773126engAaij, RoelCámpora Pérez, Daniel HugoColombo, TommasoFitzpatrick, ConorGligorov, Vladimir VavaHennequin, ArthurNeufeld, NikoNolte, NiklasSchwemmer, RainerVom Bruch, DorotheaEvolution of the energy efficiency of LHCb’s real-time processingParticle Physics - ExperimentDetectors and Experimental TechniquesThe upgraded LHCb detector, due to start datataking in 2022, will have to process an average data rate of 4 TB/s in real time. Because LHCb’s physics objectives require that the full detector information for every LHC bunch crossing is read out and made available for real-time processing, this bandwidth challenge is equivalent to that of the ATLAS and CMS HL-LHC software read-out, but deliverable five years earlier. Over the past six years, the LHCb collaboration has undertaken a bottom-up rewrite of its software infrastructure, pattern recognition, and selection algorithms to make them better able to efficiently exploit modern highly parallel computing architectures. We review the impact of this reoptimization on the energy efficiency of the realtime processing software and hardware which will be used for the upgrade of the LHCb detector. We also review the impact of the decision to adopt a hybrid computing architecture consisting of GPUs and CPUs for the real-time part of LHCb’s future data processing. We discuss the implications of these results on how LHCb’s real-time power requirements may evolve in the future, particularly in the context of a planned second upgrade of the detector.The upgraded LHCb detector, due to start datataking in 2022, will have to process an average data rate of 4~TB/s in real time. Because LHCb's physics objectives require that the full detector information for every LHC bunch crossing is read out and made available for real-time processing, this bandwidth challenge is equivalent to that of the ATLAS and CMS HL-LHC software read-out, but deliverable five years earlier. Over the past six years, the LHCb collaboration has undertaken a bottom-up rewrite of its software infrastructure, pattern recognition, and selection algorithms to make them better able to efficiently exploit modern highly parallel computing architectures. We review the impact of this reoptimization on the energy efficiency of the real-time processing software and hardware which will be used for the upgrade of the LHCb detector. We also review the impact of the decision to adopt a hybrid computing architecture consisting of GPUs and CPUs for the real-time part of LHCb's future data processing. We discuss the implications of these results on how LHCb's real-time power requirements may evolve in the future, particularly in the context of a planned second upgrade of the detector.arXiv:2106.07701oai:cds.cern.ch:27731262021 |
spellingShingle | Particle Physics - Experiment Detectors and Experimental Techniques Aaij, Roel Cámpora Pérez, Daniel Hugo Colombo, Tommaso Fitzpatrick, Conor Gligorov, Vladimir Vava Hennequin, Arthur Neufeld, Niko Nolte, Niklas Schwemmer, Rainer Vom Bruch, Dorothea Evolution of the energy efficiency of LHCb’s real-time processing |
title | Evolution of the energy efficiency of LHCb’s real-time processing |
title_full | Evolution of the energy efficiency of LHCb’s real-time processing |
title_fullStr | Evolution of the energy efficiency of LHCb’s real-time processing |
title_full_unstemmed | Evolution of the energy efficiency of LHCb’s real-time processing |
title_short | Evolution of the energy efficiency of LHCb’s real-time processing |
title_sort | evolution of the energy efficiency of lhcb’s real-time processing |
topic | Particle Physics - Experiment Detectors and Experimental Techniques |
url | https://dx.doi.org/10.1051/epjconf/202125104009 http://cds.cern.ch/record/2773126 |
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