CARLA 2022-Calls for papers Workshop HPC and Data Sciences meet Scientific Computing (before July 15th)

CARLA is an international conference that provides a forum to foster the growth and strength of the High Performance Computing (HPC) community in Latin America through the exchange and dissemination of new ideas, techniques, and research in HPC and its applications areas. It started in 2014. CARLA has become the flagship conference for HPC in the region. We invite the international community to share its advances on both HPC
and HPC&AI (convergence between HPC and Artificial Intelligence) as those two key areas are becoming the predominant engine for innovation and development.
In 2022, the Latin America High Performance Computing Conference (CARLA 2022) will be in Porto Alegre, Brazil, from September 26-30, 2022. We expect contributions from faculty members, researchers, specialists, and
graduate students around the world.

Conference dates: September 26-30, 2022

CARLA 2022 will feature three exciting tracks: HPC (High Performance Computing), HPC&AI and HPC Applications. The latter is a new addition to the regular CARLA program and focuses on the synergies between Applications and HPC. The conference program includes keynote and invited talks from the academy and industry, full-article and poster sessions (presenting both mature work and new ideas in research), workshops, tutorials, discussion spaces, and more.

Workshop HPC and Data Sciences meet Scientific Computing

The CfP for our workshop (2nd RISC2 event) within CARLA22 is now online:
Paper submission deadline: July 15th, 2022

The RISC2 project has launched a working group for a convergence between High Performance Computing (HPC), Data Science (including Machine Learning and Deep Learning) and large-scale Scientific Computing. This working group was proposed by Inria, LNCC, UFRJ/COPPE, partners of the RISC2 project. Among its actions, the working group aims to organize joint workshops between RISC2 partners.

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


  • 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)



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