CERN Accelerating science

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2021-03-02
06:39
Accelerate Scientific Deep Learning Models on Heterogeneous Computing Platform with FPGA / Jiang, Chao (Florida U.) ; Ojika, David (Florida U.) ; Vallecorsa, Sofia (CERN) ; Kurth, Thorsten (NVIDIA, Santa Clara) ; Prabhat ; Patel, Bhavesh ; Lam, Herman
AI and deep learning are experiencing explosive growth in almost every domain involving analysis of big data. Deep learning using Deep Neural Networks (DNNs) has shown great promise for such scientific data analysis applications. [...]
2020 - 10 p. - Published in : EPJ Web Conf. 245 (2020) 09014 Fulltext: PDF;
In : 24th International Conference on Computing in High Energy and Nuclear Physics, Adelaide, Australia, 4 - 8 Nov 2019, pp.09014

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2021-03-02
06:39
CMS strategy for HPC resource exploitation / Yzquierdo, Antonio Pérez-Calero (Madrid, CIEMAT ; PIC, Bellaterra) /CMS Collaboration
High Energy Physics (HEP) experiments will enter a new era with the start of the HL-LHC program, with computing needs surpassing by large factors the current capacities. Anticipating such scenario, funding agencies from participating countries are encouraging the experimental collaborations to consider the rapidly developing High Performance Computing (HPC) international infrastructures to satisfy at least a fraction of the foreseen HEP processing demands. [...]
2020 - 8 p. - Published in : EPJ Web Conf. 245 (2020) 09012
In : 24th International Conference on Computing in High Energy and Nuclear Physics, Adelaide, Australia, 4 - 8 Nov 2019, pp.09012

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2021-03-02
06:39
Large-scale HPC deployment of Scalable CyberInfrastructure for Artificial Intelligence and Likelihood Free Inference (SCAILFIN) / Hildreth, Michael (Notre Dame U.) ; Hurtado Anampa, Kenyi Paolo (Notre Dame U.) ; Kankel, Cody (Notre Dame U.) ; Hampton, Scott (Notre Dame U.) ; Brenner, Paul (Notre Dame U.) ; Johnson, Irena (Notre Dame U.) ; Simko, Tibor (CERN)
The NSF-funded Scalable CyberInfrastructure for Artificial Intelligence and Likelihood Free Inference (SCAILFIN) project aims to develop and deploy artificial intelligence (AI) and likelihood-free inference (LFI) techniques and software using scalable cyberinfrastructure (CI) built on top of existing CI elements. Specifically, the project has extended the CERN-based REANA framework, a cloud-based data analysis platform deployed on top of Kubernetes clusters that was originally designed to enable analysis reusability and reproducibility. [...]
2020 - 6 p. - Published in : EPJ Web Conf. 245 (2020) 09011
In : 24th International Conference on Computing in High Energy and Nuclear Physics, Adelaide, Australia, 4 - 8 Nov 2019, pp.09011

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2021-03-02
06:39
Development of a Versatile, Full-featured Search Functionality for Indico / Constanta, Penelope (Fermilab) ; Rind, Ofer (Brookhaven) ; Caballero Bejar, Jose (Brookhaven) ; Ferreira, Pedro (CERN) ; Mönnich, Adrian (CERN) ; Panero, Pablo (CERN) ; Antunes, Carina Rafaela De Oliveira (CERN) ; Koufakos, Aristofanis Chionis (CERN)
Indico, CERN’s popular open-source tool for event management, is in widespread use among facilities that make up the HEP community. It is extensible through a robust plugin architecture that provides features such as search and video conferencing integration. [...]
FERMILAB-CONF-20-642-CCD.- 2020 - 6 p. - Published in : EPJ Web Conf. 245 (2020) 08028 Fulltext: fermilab-conf-20-642-ccd - PDF; 10.1051_epjconf_202024508028 - PDF; External link: Fermilab Library Server
In : 24th International Conference on Computing in High Energy and Nuclear Physics, Adelaide, Australia, 4 - 8 Nov 2019, pp.08028

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2021-03-02
06:39
A Software Institute for Data-Intensive Sciences, Joining Computer Science Academia and Natural Science Research / Bird, Ian (CERN) ; Campana, Simone (CERN) ; Mato Vila, Pere (CERN) ; Roiser, Stefan (CERN) ; Schulz, Markus (CERN) ; Stewart, Graeme A (CERN) ; Valassi, Andrea (CERN)
With the ever-increasing size of scientific collaborations and complexity of scientific instruments, the software needed to acquire, process and analyze the gathered data is increasing in both complexity and size. Unfortunately the role and career path of scientists and engineers working on software R&D; and developing scientific software are neither clearly established nor defined in many fields of natural science. [...]
2020 - 7 p. - Published in : EPJ Web Conf. 245 (2020) 08020
In : 24th International Conference on Computing in High Energy and Nuclear Physics, Adelaide, Australia, 4 - 8 Nov 2019, pp.08020

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2021-03-02
06:39
eXtreme monitoring: CERN video conference system and audio-visual IoT device infrastructure / Aparicio, Ruben Gaspar (CERN) ; Soulie, Theo (CERN)
Two different use cases for monitoring are analysed in this paper: the CERN video conference system – a complex ecosystem, which is being used by most HEP institutes, together with Swiss Universities through SWITCH; and the CERN Audio-Visual and Conferencing (AVC) environment – a vast Internet of Things (IoT), which includes a great variety of devices accessible via IP. Despite the differences between both use cases, a common set of techniques underpinned by IT services is discussed in order to tackle each situation..
2020 - 8 p. - Published in : EPJ Web Conf. 245 (2020) 08019
In : 24th International Conference on Computing in High Energy and Nuclear Physics, Adelaide, Australia, 4 - 8 Nov 2019, pp.08019

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2021-03-02
06:39
Experience Finding MS Project Alternatives at CERN / Alandes Pradillo, Maria (CERN) ; Jones, Pete (CERN) ; Seweryn, Piotr Jan (CERN)
As of March 2019, CERN is no longer eligible for academic licences of Microsoft products. For this reason, CERN IT started a series of task forces to respond to the evolving requirements of the user community with the goal of reducing as much as possible the need for Microsoft licensed software. [...]
2020 - 9 p. - Published in : EPJ Web Conf. 245 (2020) 08017
In : 24th International Conference on Computing in High Energy and Nuclear Physics, Adelaide, Australia, 4 - 8 Nov 2019, pp.08017

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2021-03-02
06:39
Evolution of the CERNBox platform to support the Malt project / Labrador, Hugo González (CERN) ; Bippus, Vincent Nicolas (CERN) ; Bukowiec, Sebastian (CERN) ; Castro, Diogo (CERN) ; Dellabella, Sebastien (CERN) ; Kwiatek, Michal (CERN) ; Lo Presti, Giuseppe (CERN) ; Mascetti, Luca (CERN) ; Mościcki, Jakub T (CERN) ; Puentes, Esteban (CERN) et al.
CERNBox is the CERN cloud storage hub for more than 25,000 users at CERN. It allows synchronising and sharing files on all major desktop and mobile platforms (Linux, Windows, MacOSX, Android, iOS) providing universal, ubiquitous, online- and offline access to any data stored in the CERN EOS infrastructure. [...]
2020 - 7 p. - Published in : EPJ Web Conf. 245 (2020) 08015
In : 24th International Conference on Computing in High Energy and Nuclear Physics, Adelaide, Australia, 4 - 8 Nov 2019, pp.08015

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2021-03-02
06:39
Open data provenance and reproducibility: a case study from publishing CMS open data / Šimko, Tibor (CERN) ; de Bittencourt, Heitor Pascoal (Helsinki Inst. of Phys.) ; Carrera, Edgar (San Francisco de Quito U.) ; Lopez, Diyaselis Delgado (Unlisted, PR) ; Lange, Clemens (CERN) ; Lassila-Perini, Kati (Helsinki Inst. of Phys.) ; Lintuluoto, Adelina (CERN ; Helsinki Inst. of Phys.) ; Iglesias, Lara Lloret (Cantabria Inst. of Phys.) ; McCauley, Thomas (Notre Dame U.) ; Okraska, Jan (CERN) et al.
In this paper we present the latest CMS open data release published on the CERN Oopen Data portal. Samples of collision and simulated datasets were released together with detailed information about the data provenance. [...]
2020 - 8 p. - Published in : EPJ Web Conf. 245 (2020) 08014
In : 24th International Conference on Computing in High Energy and Nuclear Physics, Adelaide, Australia, 4 - 8 Nov 2019, pp.08014

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2021-03-02
06:39
Dataset of tau neutrino interactions recorded by the OPERA experiment / De Lellis, Giovanni (Naples U. ; INFN, Naples ; CERN) ; Dmitrievsky, Sergey (Dubna, JINR) ; Galati, Giuliana (INFN, Naples) ; Lavasa, Artemis (CERN) ; Šimko, Tibor (CERN) ; Tsanaktsidis, Ioannis (CERN) ; Ustyuzhanin, Andrey (Higher Sch. of Economics, Moscow ; Natl. U. Sci. Tech., Moscow ; INFN, Naples)
We describe the dataset of very rare events recorded by the OPERA experiment. The events represent tracks of particles associated with tau neutrino interactions coming from the transformation of muon neutrinos due to a process known as neutrino oscillations. [...]
2020 - 7 p. - Published in : EPJ Web Conf. 245 (2020) 08013
In : 24th International Conference on Computing in High Energy and Nuclear Physics, Adelaide, Australia, 4 - 8 Nov 2019, pp.08013

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