HPC and Big Data Technologies for Global Challenges
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The HiDALGO2 project is addressing challenges caused by climate change, focusing on technical issues related to scalability on HPC and AI infrastructures, the use of computational fluid dynamics methods, and uncertainty analysis.

HiDALGO2 will deliver highly scalable solutions for improving air quality, energy efficiency, and renewable energy technologies; for predicting the spread of wildfires; and for meteo-hydrological forecasting. It will also address skills gaps and improve knowledge sharing within user communities.

Climate change has long been an undeniable phenomenon and is now visible at close range in many places worldwide. It influences our daily lives and increasingly affects our quality of life. One of the main reasons for climate change is the dynamic development of societies, which over decades has had a significant impact on the natural environment. The effects of climate change can be dramatically seen today through severe weather phenomena (e.g., dangerous storms and heavy rainfall). In this way, climate change has a direct influence on our daily lives and increasingly affects our quality of life.

Because of their inherent complexity, global challenges such as climate change require interdisciplinary expertise to address them. HiDALGO2, therefore, will explore synergies involving modelling, data acquisition, simulation, data analysis, and visualization. It will also improve the scalability of codes on current and future HPC and AI infrastructures, delivering solutions that can effectively utilize pre-exascale systems.

HiDALGO2 is focusing on five environmental use cases:

  • air quality in urban conurbations
  • energy efficiency of buildings
  • renewable energy sources
  • spread of forest fires
  • meteorological-hydrological forecasting.

A common feature of simulations in these areas is the numerical analysis of flows using computational fluid dynamics (CFD), a computationally intensive approach. HiDALGO2 attaches great importance to questions of the scalability of its solutions. This includes optimizing software for the infrastructure on which it will run (co-design). This is being achieved using appropriate benchmarking methods and algorithmic optimization procedures. In this way, HPC systems can simulate complex structures with an accuracy that cannot be achieved using cloud solutions. We also evaluate the quality of our solutions by conducting uncertainty analyses performed through ensemble runs. In addition, HiDALGO2 is actively contributing to the development of user communities in the EU, closing the skills gap and sharing knowledge through organized, specialized workflows and training activities.

In its first project phase, HiDALGO’s goal was to address global challenges using the latest technologies in high-performance computing and artificial intelligence. It addressed global challenges in the areas of forced migration, social network analysis, urban air pollution, and the spread of pandemics such as COVID-19. The project enhanced research in these areas by developing simulation tools for HPC, including highly scalable agent-based simulations. In addition, HiDALGO applied a concept called coupling to implement hybrid workflows that combine HPC and AI technologies, as well as multiple datasets; for example, to improve the accuracy of weather forecasts. A major challenge in the first phase of HiDALGO was to involve and bring together experts from various research fields.

In the second phase of HiDALGO, our interest in addressing global challenges is now focused on environmental challenges, particularly for looming problems such as the energy crisis in Europe. To this end, HiDALGO2 will build on the results achieved in the first phase and continue to be the focal point for decision-makers, technical experts, and relevant stakeholders in the global challenges ecosystem in Europe.

Project partners

  • PSNC, Poznan Supercomputing Centre, PL
  • USTUTT, University of Stuttgart, DE
  • ATOS, Atos Spain, ES
  • SZE, Széchenyi István University, HU
  • MTG, MeteoGrid, ES
  • UNISTRA, University of Strasbourg, FR
  • ICCS, National Technical University of Athens, EL
  • Future Needs, CY

Predecessor project HiDALGO

If you would like to learn more about the predecessor project HiDALGO, which
was funded by the EU under the Horizon 2020 programme from 2018 to 2022, we
refer you to the project page: hidalgo-project.eu.


Dennis Hoppe

Head, Service Management and Business Processes

+49 711 685-60300 dennis.hoppe(at)hlrs.de