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CERN Document Server 27 notices trouvées  1 - 10suivantfin  aller vers la notice: La recherche a duré 0.57 secondes. 
1.
Boosting RDataFrame performance with transparent bulk event processing / Guiraud, Enrico (CERN) ; Blomer, Jakob (CERN) ; Canal, Philippe (Fermilab) ; Naumann, Axel (CERN)
RDataFrame is ROOT’s high-level interface for Python and C++ data analysis. Since it first became available, RDataFrame adoption has grown steadily and it is now poised to be a major component of analysis software pipelines for LHC Run 3 and beyond. [...]
FERMILAB-CONF-24-0684-CSAID.- 2024 - 8 p. - Published in : EPJ Web Conf. 295 (2024) 06006 Fulltext: 3afc9b57f03c0d303680c14cbbcb8c23 - 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.06006
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2.
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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3.
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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4.
Direct Evolutionary Optimization of Variational Autoencoders with Binary Latents / Drefs, Jakob (Oldenburg U.) ; Guiraud, Enrico (CERN) ; Panagiotou, Filippos (Oldenburg U.) ; Lücke, Jörg (Oldenburg U.)
Many types of data are generated at least partly by discrete causes. Deep generative models such as variational autoencoders (VAEs) with binary latents consequently became of interest. [...]
2023
In : European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2022), Grenoble, France, 19 - 23 Sep 2022, pp.357–372
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5.
Evolutionary expectation maximization / Guiraud, Enrico (Oldenburg U. ; CERN) ; Drefs, Jakob (Oldenburg U.) ; Lücke, Jörg (Oldenburg U.)
We establish a link between evolutionary algorithms (EAs) and learning of probabilistic generative models with binary hidden variables. Learning is formulated as approximate maximum likelihood optimization using variational expectation maximization. [...]
2018 - 8 p. - Published in : 10.1145/3205455.3205588
In : Genetic and Evolutionary Computation Conference (GECCO '18), Kyoto, Japan, 15 - 19 Jul 2018, pp.442-449
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6.
RDataFrame enhancements for HEP analyses / Guiraud, E (CERN) ; Blomer, J (CERN) ; Hageboeck, S (CERN) ; Naumann, A (CERN) ; Padulano, V E (CERN) ; Tejedor, E (CERN) ; Wunsch, S (CERN)
In recent years, RDataFrame, ROOT’s high-level interface for data analysis and processing, has seen widespread adoption on the part of HEP physicists. Much of this success is due to RDataFrame’s ergonomic programming model that enables the implementation of common analysis tasks more easily than previous APIs, without compromising on application performance. [...]
2023 - 6 p. - Published in : J. Phys. : Conf. Ser. 2438 (2023) 012116 Fulltext: PDF;
In : 20th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2021), Daejeon, Korea, 29 Nov - 3 Dec 2021, pp.012116
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7.
Leveraging HPC resources with distributed RDataFrame / Padulano, V E (CERN ; Valencia, Polytechnic U.) ; Kabadzhov, I D (CERN ; Freiburg U., Inst. Phys. Chem.) ; Saavedra, E T (CERN) ; Guiraud, E (CERN)
The declarative approach to data analysis provides high-level abstractions for users to operate on their datasets in a much more ergonomic fashion compared to imperative interfaces. ROOT offers such a tool with RDataFrame, which has been tested in production environments and used in real-world analyses with optimal results. [...]
2023 - 5 p. - Published in : J. Phys. : Conf. Ser. 2438 (2023) 012097 Fulltext: PDF;
In : 20th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2021), Daejeon, Korea, 29 Nov - 3 Dec 2021, pp.012097
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8.
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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9.
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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10.
Evolutionary Variational Optimization of Generative Models / Drefs, Jakob (Oldenburg U.) ; Guiraud, Enrico (CERN) ; Lücke, Jörg (Oldenburg U.) ; Wood, Frank (ed.)
We combine two popular optimization approaches to derive learning algorithms for generative models: variational optimization and evolutionary algorithms. The combination is realized for generative models with discrete latents by using truncated posteriors as the family of variational distributions. [...]
2022 - 51 p. - Published in : J. Mach. Learn. Res.: 23 (2022) , pp. 1-51 Fulltext: PDF; External link: Fulltext
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20 Guiraud, Enrico
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