The science behind the HiDALGO2 innovations
As global environmental, urban, and computing challenges expand, traditional computational frameworks frequently hit structural limits. Simulating entire cityscapes, tracing microscale atmospheric pollution, or predicting the flash progression of a wildfire requires massive processing capacity. However, raw power is no longer enough; next-generation engineering demands scalability, transparency, and energy awareness.
As a premier EuroHPC Joint Undertaking Center of Excellence, HiDALGO2 bridges this gap. By co-designing next-generation numerical methods, workflow automation pipelines, and machine learning architectures, the consortium is unlocking extreme-scale computing for open science.
To help researchers and industry professionals navigate our project milestones, we have consolidated our latest 16 core publications, benchmarks, and open datasets into five interconnected scientific pillars.
Today, we are sharing 16 must-read publications from the HiDALGO2 consortium – including benchmarks and open datasets – offering fresh insights for researchers and industry professionals. Discover the realm of next-generation technologies and innovation, exploring the science behind HPC advancements to address global challenges on air pollution, building energy efficiency, renewable energy resources management, wildfires and water pollution.
Check out the various themes and articles below.
Theme I: Urban Digital Twins & Sustainable Infrastructure
Developing viable mitigation plans for modern smart cities requires modelling physical infrastructure at an individual building resolution. HiDALGO2 accelerates this deployment by introducing automated cloud-native engineering into classic supercomputing environments, eliminating manual deployment friction.
1. Ktirio Urban Building: A Computational Framework for City Energy Simulations Enhanced by CI/CD Innovations on EuroHPC Systems, by Luca Berti, Vincent Chabannes, Gwennolé Chappron, Javier Cladellas, Abdoulaye Diallo, Maryam Maslek Elayam, Philippe Pinçon & Christophe Prud’homme.
https://link.springer.com/chapter/10.1007/978-3-031-85703-4_2
2. Open-source complex-geometry 3D fluid dynamics for applications with unpredictable heterogeneous dynamic high-performance-computing loads, by J. Bakosi, Széchenyi Egyetem, University of Győr, Hungary
https://www.sciencedirect.com/science/article/pii/S0045782523007107
Theme II: Microscale Environmental Modelling & Climate Adaptation
From vehicular emissions to active forest fires, climate adaptation relies heavily on capturing high-resolution, real-time spatial trends. Our research couples microscale atmospheric dispersion tracking with hyper-fast predictive models to support environmental policy and crisis response.
3. Estimating the air quality standard exceedance areas and the spatial representativeness of urban air quality stations applying microscale modelling, by F. Martín, V. Rodrigues, J.L. Santiago, J. Sousa, J. Stocker, S. Janssen, R. Jackson, F. Russo, M.G. Villani, G. Tinarelli, D. Barbero, R. San José, J.L. Pérez-Camanyo, G. Sousa-Santos, L. Tarrason, J. Bartzis, I. Sakellaris, Z. Horváth, L. Környei, X. Jurado, P. Thunis
https://www.sciencedirect.com/science/article/pii/S0048969725014652?via%3Dihub
4. Using dispersion models at microscale to assess long-term air pollution in urban hot spots: A FAIRMODE joint intercomparison exercise for a case study in Antwerp, by F. Martín, S. Janssen, V. Rodrigues, J.Sousa, J.L. Santiago, E.Rivas, J.Stocker, R.Jackson, F.Russo, M.G.Villani, G.Tinarelli, D.Barbero, R.San José, J.L.Pérez-Camanyo, G.Sousa Santos, J.Bartzis, I.Sakellaris, Z.Horváth, L.Környei, B.Liszkai, C.Cuvelier
https://www.sciencedirect.com/science/article/pii/S0048969724019041?via%3Dihub
5. AI for Global Challenges: Case Studies in Urban Solar Exposure & Wildfire Management, by Giorgos Filandrianos, Angeliki Dimitriou, Vasiliki Kostoula, Nikolaos Chalvantzis, David Caballero, Luis Torres, Michal Kulczewski, Javier Cladellas, Zoltán Horváth, Harald Köstler, Konstantinos Nikas, Dimitrios Tsoumakos, Giorgos Stamou.
https://www.thinkmind.org/articles/aisys_2024_1_20_88003.pdf
6. Simulation of Wildfires Using EuroHPC Resources: Challenges and Opportunities, by David Caballero, Leydi Laura Salazar, Ángela Rivera & Luis Torres
https://link.springer.com/chapter/10.1007/978-3-031-85703-4_1
Theme III: Graph Analytics, Network Science & Explainable AI (XAI)
The intersection of HPC and AI demands deep transparency. As deep learning models expand into physical domains, HiDALGO2 is actively building frameworks that replace traditional “black-box” systems with explainable, semantic structural networks.
7. Structure Your Data: Towards Semantic Graph Counterfactuals (proceedings of the 41st International Conference on Machine Learning (ICML), PMLR 235:10897-10926, 2024) by Angeliki Dimitriou, Maria Lymperaiou, Georgios Filandrianos, Konstantinos Thomas, Giorgos Stamou
https://proceedings.mlr.press/v235/dimitriou24a.html
8. Affected Buildings Network Topology: Utilising Graph Convolutional Networks (GCNs) and transductive link prediction to map and discover hidden topological shading dependencies across dense cityscape networks.
Theme IV: Energy-Aware Supercomputing & Scalability Profiling
True supercomputing innovation is a balancing act. Pushing simulation meshes across multi-thousand-core setups dramatically drives up energy footprints, meaning codes must become energy-aware to remain operationally sustainable.
9. Prediction model of performance–energy trade-off for CFD codes on AMD-based cluster, by Marcin Lawenda, Łukasz Szustak, László Környei
https://www.sciencedirect.com/science/article/abs/pii/S0167739X25001050
10. Evaluating AMD EPYC CPU architectures on CFD applications, by Marcin Lawenda, Łukasz Szustak, László Környei, Flavio Cesar Cunha Galeazzo, Paweł Bratek
https://www.sciencedirect.com/science/article/abs/pii/S0167739X2500531X
Theme V: Advanced Parallel Engineering & Numerical Portability
At the very foundation of the HiDALGO2 project lies core parallel software engineering. By tackling cross-platform portability, memory latency bottlenecks, and load imbalances, we ensure our workflows run fluidly across heterogeneous clusters.
11. Partition deactivation with load balancing for parallel flow simulations, by J. Bakosi, Széchenyi Egyetem, University of Győr, Hungary
https://www.sciencedirect.com/science/article/pii/S0021999124006351?via%3Dihub
12. Breaking Down LLM Inference: A preliminary performance analysis of sparsified transformers, by
Ioanna Tasou; Petros Anastasiadis; Panagiotis Mpakos; Dimitrios Galanopoulos; Nectarios Koziris; Georgios Goumas
https://ieeexplore.ieee.org/document/11105852
13. Complex-Geometry 3D Computational Fluid Dynamics with Automatic Load Balancing, by József Bakosi, Mátyás Constans, Zoltán Horváth, Ákos Kovács, László Környei, Marc Charest, Aditya Pandare, Paula Rutherford and Jacob Waltz
https://www.mdpi.com/2311-5521/8/5/147
14. Fostering Uncertainty Quantification in Global Challenges with mUQSA Toolkit, by Michał Kulczewski, Bartosz Bosak, Piotr Kopta, Wojciech Szeliga & Tomasz Piontek
https://link.springer.com/chapter/10.1007/978-3-031-85703-4_3
15. Profiling and Optimisation of Multicard GPU Machine Learning Jobs (Journal Wiley – Concurrency and Computation: Practice and Experience), by Marcin Lawenda, Kyrylo Khloponin, Krzesimir Samborski, Łukasz Szustak https://onlinelibrary.wiley.com/doi/10.1002/cpe.70196
16. Efficient allocation of image recognition and LLM tasks on multi-GPU system, by Marcin Lawenda, Krzesimir Samborski, Kyrylo Khloponin, Łukasz Szusta
https://zenodo.org/records/15055009
17. Uncut-GEMMs: Communication-aware matrix multiplication on multi-GPU nodes, by Petros Anastasiadis, Nikela Papadopoulou, Nectarios Koziris and Georgios Goumas.
https://zenodo.org/records/14536176
Bringing Open Science into the Future
Every script, dataset, and manuscript produced under the HiDALGO2 umbrella is designed to be fully reproducible, transparent, and aligned with European Open Science standards. We invite the broader HPC, data science, and environmental modelling communities to deploy our open-source tools, review our energy benchmarks, and collaborate with us as we continue to co-design the future of high-performance simulations.
Stay tuned for more scientific publications of the HiDALGO2 Centre of Excellence coming out soon.
Learn about the papers presented at the PPAM Conference
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