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First implementation and results of the Analysis Grand Challenge with a fully Pythonic RDataFrame
/ Padulano, Vincenzo Eduardo (CERN) ; Guiraud, Enrico (CERN ; Princeton U.) ; Falko, Andrii (Taras Shevchenko U.) ; Gazzarrini, Elena (CERN) ; Garcia Garcia, Enrique (CERN) ; Gosein, Domenic (CERN ; Mannheim U.)
The growing amount of data generated by the LHC requires a shift in how HEP analysis tasks are approached. Efforts to address this computational challenge have led to the rise of a middle-man software layer, a mixture of simple, effective APIs and fast execution engines underneath. [...]
2024 - 8 p.
- Published in : EPJ Web Conf. 295 (2024) 06011
Fulltext: PDF;
In : 26th International Conference on Computing in High Energy & Nuclear Physics, Norfolk, Virginia, Us, 8 - 12 May 2023, pp.06011
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I/O performance studies of analysis workloads on production and dedicated resources at CERN
/ Sciabà, Andrea (CERN) ; Blomer, Jakob (CERN) ; Canal, Philippe (Fermilab) ; Duellmann, Dirk (CERN) ; Guiraud, Enrico (CERN) ; Naumann, Axel (CERN) ; Padulano, Vincenzo Eduardo (CERN) ; Panzer-Steindel, Bernd (CERN) ; Peters, Andreas (CERN) ; Schulz, Markus (CERN) et al.
The recent evolutions of the analysis frameworks and physics data formats of the LHC experiments provide the opportunity of using central analysis facilities with a strong focus on interactivity and short turnaround times, to complement the more common distributed analysis on the Grid. In order to plan for such facilities, it is essential to know in detail the performance of the combination of a given analysis framework, of a specific analysis and of the installed computing and storage resources. [...]
FERMILAB-CONF-24-0689-CSAID.-
2024 - 9 p.
- Published in : EPJ Web Conf. 295 (2024) 07025
Fulltext: 9b80f56360993741399aef5f272f8af7 - PDF; document - PDF; External link: Fermilab Library Server
In : 26th International Conference on Computing in High Energy & Nuclear Physics, Norfolk, Virginia, Us, 8 - 12 May 2023, pp.07025
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ROOT’s RNTuple I/O Subsystem: The Path to Production
/ Blomer, Jakob (CERN) ; Canal, Philippe (Fermilab) ; de Geus, Florine (CERN) ; Hahnfeld, Jonas (CERN) ; Naumann, Axel (CERN) ; Lopez-Gomez, Javier (CERN) ; Miotto, Giovanna Lazzari (CERN) ; Padulano, Vincenzo Eduardo (CERN)
The RNTuple I/O subsystem is ROOT’s future event data file format and access API. It is driven by the expected data volume increase at upcoming HEP experiments, e.g. [...]
FERMILAB-CONF-24-0690-CSAID.-
2024 - 7 p.
- Published in : EPJ Web Conf. 295 (2024) 06020
Fulltext: document - PDF; 5fef0b895aa1fd0f7fb49d3c87a67721 - PDF; External link: Fermilab Library Server
In : 26th International Conference on Computing in High Energy & Nuclear Physics, Norfolk, Virginia, Us, 8 - 12 May 2023, pp.06020
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PyHEP.dev 2024 Workshop Summary Report, August 26-30 2024, Aachen, Germany
/ Alshehri, Azzah (Ain Shames U.) ; Bürger, Jan (KISTI, Daejeon) ; Chopra, Saransh (University Coll. London) ; Eich, Niclas (RWTH Aachen U.) ; Eppelt, Jonas (KIT, Karlsruhe, TTP) ; Erdmann, Martin (RWTH Aachen U.) ; Eschle, Jonas (Syracuse U.) ; Fackeldey, Peter (KISTI, Daejeon ; RWTH Aachen U.) ; Farkas, Maté (RWTH Aachen U.) ; Feickert, Matthew (Wisconsin U., Madison) et al.
The second PyHEP.dev workshop, part of the "Python in HEP Developers" series organized by the HEP Software Foundation (HSF), took place in Aachen, Germany, from August 26 to 30, 2024. [...]
arXiv:2410.02112.
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16.
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Leveraging an open source serverless framework for high energy physics computing
/ Padulano, Vincenzo Eduardo (Valencia, Polytechnic U. ; CERN) ; Cortés, Pablo Oliver (Valencia, Polytechnic U.) ; Alonso‑Jordá, Pedro (Valencia, Polytechnic U.) ; Saavedra, Enric Tejedor (CERN) ; Risco, Sebastián (Valencia, Polytechnic U.) ; Moltó, Germán (Valencia, Polytechnic U.)
CERN (Centre Europeen pour la Recherce Nucleaire) is the largest research centre for high energy physics (HEP). It offers unique computational challenges as a result of the large amount of data generated by the large hadron collider. [...]
2023 - 26 p.
- Published in : J. Supercomput. 79 (2023) 8940-8965
Fulltext: PDF;
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Prototyping a ROOT-based distributed analysis workflow for HL-LHC: the CMS use case
/ Tedeschi, Tommaso (INFN, Perugia ; Perugia U.) ; Padulano, Vincenzo Eduardo (CERN) ; Spiga, Daniele (INFN, Perugia) ; Ciangottini, Diego (INFN, Perugia) ; Tracolli, Mirco (INFN, Perugia) ; Tejedor Saavedra, Enric (CERN) ; Guiraud, Enrico (CERN ; Princeton U.) ; Biasotto, Massimo (INFN, Legnaro ; Padua U.)
The challenges expected for the next era of the Large Hadron Collider (LHC), both in terms of storage and computing resources, provide LHC experiments with a strong motivation for evaluating ways of rethinking their computing models at many levels. Great efforts have been put into optimizing the computing resource utilization for the data analysis, which leads both to lower hardware requirements and faster turnaround for physics analyses. [...]
arXiv:2307.12579.-
2023-10-16 - 26 p.
- Published in : Comput. Phys. Commun. 295 (2024) 108965
Fulltext: PDF;
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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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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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