Scientific Data Analysis Using Data-Intensive Scalable Computing: the SciDISC Project

Patrick Valduriez 1, 2 Marta Mattoso 3 Reza Akbarinia 1 Heraldo Borges 4 José Camata 3 Alvaro Coutinho 3 Daniel Gaspar 5 Noel Lemus 5 Ji Liu 1, 2 Hermano Lustosa 5 Florent Masseglia 1 Fabricio Nogueira da Silva 6 Vitor Silva 3 Renan Souza 3 Kary Ocaña 5 Eduardo Ogasawara 4 Daniel Oliveira 6 Esther Pacitti 1 Fábio Porto 5 Dennis Shasha 7
1 ZENITH - Scientific Data Management
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
2 IBC - Institut de Biologie Computationnelle
3 COPPE-UFRJ - COPPE - Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa de Engenharia
4 CEFET/RJ - Centro Federal de Educação Tecnológica Celso Suckow da Fonseca (Rio de Janeiro)
5 LNCC / MCT - Laboratorio Nacional de Computação Cientifica [Rio de Janeiro]
6 UFF - Universidade Federal Fluminense [Rio de Janeiro]
7 CIMS - Courant Institute of Mathematical Sciences [New York]
Abstract : Data-intensive science requires the integration of two fairly different paradigms: high-performance computing (HPC) and data-intensive scalable computing (DISC), as exemplified by frameworks such as Hadoop and Spark. In this context, the SciDISC project addresses the grand challenge of scientific data analysis using DISC, by developing architectures and methods to combine simulation and data analysis. SciDISC is an ongoing project between Inria, several research institutions in Rio de Janeiro and NYU. This paper introduces the motivations and objectives of the project, and reports on the first results achieved so far.
Keywords : HPC Scalable Data-Intensive Computing Big data Scientific data
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Patrick Valduriez, Marta Mattoso, Reza Akbarinia, Heraldo Borges, José Camata, et al.. Scientific Data Analysis Using Data-Intensive Scalable Computing: the SciDISC Project. LADaS: Latin America Data Science Workshop, Aug 2018, Rio de Janeiro, Brazil. ⟨lirmm-01867804⟩

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