HPC and Data Sciences meet Scientific Computing

About the workshop

Data-intensive science requires the integration of two fairly different paradigms: high-performance computing (HPC) and data science. HPC (including large-scale scientific computing) is computer-centric and model-driven; it focuses on high performance of simulation applications, typically using powerful, yet expensive and energy consuming supercomputers, whereas data science (including Machine Learning) is data-centric and data-driven; it focuses on scalability and fault-tolerance of web and cloud applications using cost-effective clusters of commodity hardware. The convergence between HPC and data science or, in its simplest form, big data, has been a recent topic of interest. Such convergence will include for instance, the simulations of physical problems modeled by partial differential equations (PDE) systems that in turn generate a huge amount of data. Another example is combining ML with simulation, which requires a change from typical datasets to scientific datasets, making scientific runtime data analysis necessary for monitoring the ML life cycle. One of the challenges when working with physics-aware ML is to reduce the training cost. This convergence is already at the heart of ongoing research and development activities in the context of joint projects between Brazilian and French researchers. It is also driving the recently launched Center of Excellence in Digital Transformation and Artificial Intelligence of the State of Rio de Janeiro.


  • Scientific Machine Learning (SciML)
  • High performance scientific computing
  • Data Science

Submission Guidelines

Paper submission deadline: July 15th, 2022 August 05th, 2022

Authors should consult Springer's authors' guidelines and use their proceedings templates, either for LaTeX or for MSWord, for the preparation of their articles: template   https://www.springer.com/gp/computer-science/lncs/conference-proceedings-guidelines

  • All submissions must be in English.
  • Full papers should not exceed 12 pages, and extended abstracts should not exceed 2 pages (including references).
  • Only contributions submitted as full papers that are not submitted elsewhere or currently under review will be considered.
  • Only accepted contributions presented at the conference will be included in the workshop proceedings.
  • Article submission is handled electronically with Springer's Online Conference System: OCS. Once in the submission page, select the category "Workshop 7: RISC2 Workshop: HPC and Data Sciences Meet Scientific Computing (HPCDataSciences)"
  • If you are submitting an extended abstract, please include the prefix "EXTENDED ABSTRACT:" when filling the 'Title' field in the submission page.


  • Alvaro L.G.A. Coutinho (COPPE/Federal University of Rio de Janeiro, Brazil)
  • Marta Marta Mattoso (COPPE/Federal University of Rio de Janeiro, Brazil)
  • Antonio Tadeu Azevedo Gomes (Laboratório Nacional de Computação Científica, Brazil)
  • Frédéric Valentin (Laboratório Nacional de Computação Científica, Brazil)
  • Luc Giraud (Inria, France)
  • Stéphane Lanteri (Inria, France)
  • Patrick Valduriez (Inria, France)

RISC2 Project

The European project RISC2 aims to create a network to support the coordination of High-Performance Computing research between Europe and Latin America. Its main goal is exploring the real and potential impact on HPC, namely in coping with the growing environmental and scientific challenges and, therefore, in the economies of Latin America and Europe. Gathering key European HPC actors to encourage stronger cooperation between their research and industrial communities on HPC applications and infrastructure deployment. The results of the RISC2 project will promote the exchange of the best practices in HPC, in Europe and Latin America. The project also focuses on boosting Latin American HPC, promoting the interaction between researchers and policymakers in both regions and strengthening their connection, towards the definition of a coordinated policy and a concrete roadmap for the future.
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