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CERN Document Server 4 записей найдено  Поиск длился 1.31 секунд. 
1.
Leveraging State-of-the-Art Engines for Large-Scale Data Analysis in High Energy Physics / Padulano, Vincenzo Eduardo (CERN ; Valencia, Polytechnic U.) ; Kabadzhov, Ivan Donchev (CERN ; Freiburg U., Inst. Phys. Chem.) ; Tejedor Saavedra, Enric (CERN) ; Guiraud, Enrico (CERN) ; Alonso-Jordá, Pedro (Valencia, Polytechnic U.)
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. [...]
2023 - 21 p. - Published in : J. Grid Comput. 21 (2023) 9 Fulltext: PDF;
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2.
A caching mechanism to exploit object store speed in High Energy Physics analysis / Padulano, Vincenzo Eduardo (CERN ; Valencia, Polytechnic U.) ; Saavedra, Enric Tejedor (CERN) ; Alonso-Jorda, Pedro (Valencia, Polytechnic U.) ; Gomez, Javier Lopez (CERN) ; Blomer, Jakob (CERN)
AbstractData analysis workflows in High Energy Physics (HEP) read data written in the ROOT columnar format. Such data has traditionally been stored in files that are often read via the network from remote storage facilities, which represents a performance penalty especially for data processing workflows that are I/O bound. [...]
2023 - 16 p. - Published in : Cluster Comput. 26 (2023) 2757-2772 Fulltext: PDF;
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3.
A Serverless Engine for High Energy Physics Distributed Analysis / Kuśnierz, Jacek (AGH-UST, Cracow ; Munich, Tech. U.) ; Padulano, Vincenzo Eduardo (CERN ; Valencia, Polytechnic U.) ; Malawski, Maciej (AGH-UST, Cracow) ; Burkiewicz, Kamil (AGH-UST, Cracow) ; Saavedra, Enric Tejedor (CERN) ; Alonso-Jordá, Pedro (Valencia, Polytechnic U.) ; Pitt, Michael (CERN) ; Avati, Valentina (CERN)
The Large Hadron Collider (LHC) at CERN has generated in the last decade an unprecedented volume of data for the High-Energy Physics (HEP) field. Scientific collaborations interested in analysing such data very often require computing power beyond a single machine. [...]
arXiv:2206.00942.- 2022-05 - 10 p. - Published in : 10.1109/CCGrid54584.2022.00067 Fulltext: PDF;
In : 22nd IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, Taormina, Italy, 16 - 19 May 2022, pp.575-584
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4.
Fine-grained data caching approaches to speedup a distributed RDataFrame analysis / Padulano, Vincenzo Eduardo (Valencia, Polytechnic U. ; CERN) ; Tejedor Saavedra, Enric (CERN) ; Alonso-Jordá, Pedro (Valencia, Polytechnic U.)
Thanks to its RDataFrame interface, ROOT now supports the execution of the same physics analysis code both on a single machine and on a cluster of distributed resources. In the latter scenario, it is common to read the input ROOT datasets over the network from remote storage systems, which often increases the time it takes for physicists to obtain their results. [...]
2021 - 11 p. - Published in : EPJ Web Conf. 251 (2021) 02027 Fulltext: PDF;
In : 25th International Conference on Computing in High-Energy and Nuclear Physics (CHEP), Online, Online, 17 - 21 May 2021, pp.02027
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