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OverviewIn modern cities, tackling complex environmental and energy issues through experiments can be impractical due to cost, risk, or scale. Extreme-scale computing now enables the creation of highly accurate predictive models and allows for the analysis of vast datasets, thanks to advancements in AI. Integrating predictive modelling with machine learning opens new opportunities in science, […]

The building sector is one of the largest consumers of energy and contributors to greenhouse gas emissions in the European Union (EU), representing 35% of energy-related EU emissions in 2021. In response, the EU has set ambitious targets, aiming to become the world’s first climate-neutral continent by 2050.  This objective is at the heart of

In a previous blog, we discussed the performance of different programming models—SYCL, OpenMP, CUDA, and HIP—when applied to Computational Fluid Dynamics (CFD). In this article we will delve deeper into these three languages, comparing their pros and cons, installation requirements, and performance using a simple vector addition example. GPU programming CUDA has been the go-to

Computational Fluid Dynamics (CFD) simulations are critical for understanding and predicting complex fluid dynamics phenomena that are often difficult or impossible to capture by experimental methods alone. An example of the active use of CFD simulation to aid in the understanding of a complex flow scenario is demonstrated in this project, which marks the first

Hardware acceleration, especially through Graphics Processing Units (GPUs), is vital for enhancing computational speed and efficiency. GPUs are designed for parallel processing, enabling them to handle multiple data sets simultaneously. This makes them highly efficient at rendering graphics, image processing, and machine learning tasks. In contrast, CPUs process operations sequentially, making GPUs faster at computations.

What does HiDALGO2 stand for?                                                                                   HiDALGO2 stands for HPC and Big Data Technologies for Global Challenges. This means that the project integrates the technologies of High Performance Computing, High Performance Data Analytics, and Artificial Intelligence to provide solutions and innovations for major environmental issues.  What is the vision of HiDALGO2? The vision of the project

HiDALGO2 brings together teams and institutions across different countries and time zones, all working together to improve the scalability of several applications from the domain of Global Challenges on Europe’s HPC systems. Here, the concept of DevOps provides the cultural push, and the methodology of continuous integration and deployment (CI/CD) provides the unified framework needed

Technologies used in more detail    HiDALGO2 is building, from an AI point of view, on state-of-the-art algorithms and frameworks such as PyTorch, TensorFlow, or Theano to implement the intelligent workflow composition mechanism. From the HPDA perspective, well-adopted tools and their integration ability are being investigated to enable in-situ analysis of produced data. Specifically, the data

Europe experienced recently intense heat waves resulting in record temperatures, as a follow-up of the worrying increase of the average temperature of the planet. Wildfires along the Mediterranean countries have shown extreme behaviour over the past summer weeks, particularly in Greece, Spain, Portugal, Croatia, Italy, Algeria Tunisia, and Turkey. These devastating phenomena resulted in dozens

In the HiDALGO2 project, we need to address the pressing question of how well our models and simulations perform by openly discussing the assumptions, approximations, and limitations that impact the credibility of our simulations. For that purpose, we apply an uncertainty quantification (UQ) framework, specifically focusing on its application to environmental challenges that require computational-fluid

QCG Constantly growing numbers on the Top500 list of the World’s largest supercomputers can look appealing to computational scientists and engineers. Multi Peta- or even Exa-scale resources that are already available for users is something that 15-years ago was only a dream. Thus many application developers and users who were blocked by the computer power,

Cemosis The Centre de Modélisation et de Simulation de Strabourg, Cemosis, is the University of Strasbourg’s mathematics platform for collaborations with companies and research laboratories. It is attached to the IRMA UMR 7501 laboratory, where it has been housed since its creation in 2013. Cemosis is financed by collaborative research contracts and services to industry.

What is the HiDALGO2 project? HiDALGO2 project brings together 10 interdisciplinary partners from 8 countries in order to achieve best results. The scope of the work of HiDALGO2 is to some extent a continuation of the tasks carried out in the HiDALGO project. In the core of the current project are the same people, which