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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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Lenguaje: | eng |
Publicado: |
2020
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Materias: | |
Acceso en línea: | https://dx.doi.org/10.1088/1748-0221/15/07/C07038 http://cds.cern.ch/record/2749156 |
Sumario: | 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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