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, our global consortium is unlocking extreme-scale computing for open science.
To help researchers, developers, and policymakers navigate our project milestones, we have consolidated our latest 16 core publications, benchmarks, and open datasets into five interconnected scientific pillars.
Theme I: Urban Digital Twins & Sustainable Infrastructure
Developing viable mitigation plans for modern smart cities requires modeling 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.
Thematic Research & Repositories:
1. Automated Building Orchestration: Discover the implementation of continuous integration and continuous deployment (CI/CD) pipelines engineered to push municipal infrastructure simulations directly onto HPC execution nodes.
Read the Chapter 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. Validation Engine Frameworks: Deep dive into the underlying structural configurations and code orchestration layers supporting next-gen urban digital twins.
Read the article “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 Modeling & 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.
Thematic Research & Repositories:
3. Antwerp Field Validation (Part A): Evaluating microscale dispersion analytics and spatial tracking metrics under international FAIRMODE compliance standards to identify nitrogen dioxide (NO_2) levels.
Read the article “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. Exceedance Mapping Profiles (Part B): Utilizing high-resolution computing clusters to isolate neighborhood pollution hot spots and optimise station placement.
Read the article “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. Predicting Shading & Wildfire Behavior (AISYS 2024): Harnessing High-Performance Data Analytics (HPDA) and AI to process massive climate variables, supporting both urban shading predictions and real-time wildfire prediction engines
Integrating Graph Neural Networks and HPDA to map building-to-building solar masks in Strasbourg and deploy real-time shape-matching algorithms for wildfire propagation front containment
Read the conference proceedings “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. Wildfire Simulations on EuroHPC Resources: A comprehensive look at the mathematical, computational, and infrastructure challenges of scaling wildfire and smoke propagation models across Europe’s leading supercomputing sites.
Read the article “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.
Thematic Research & Repositories:
7. Explainable AI (XAI) Architecture: Introducing Semantic Graph Counterfactuals to bypass NP-hard graph comparison limitations, generating clear explanation trees for visual domain neural networks.
Read the proceedings of the 41st International Conference on Machine Learning (ICML), PMLR 235:10897-10926, 2024 “Structure Your Data: Towards Semantic Graph Counterfactuals, ” by Angeliki Dimitriou, Maria Lymperaiou, Georgios Filandrianos, Konstantinos Thomas, Giorgos Stamou
https://proceedings.mlr.press/v235/dimitriou24a.html
8. Affected Buildings Network Topology: Utilizing 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.
Thematic Research & Repositories:
9. The Predictive CVOPTS Framework: Introducing the CVOPTS metric, an analytical model that predicts cluster behavior and enables researchers to select hardware configurations that balance performance with energy costs.
Read the article “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. Energy Profiling for Air Quality Apps: An in-depth scalability and power consumption study detailing exactly how urban air quality codes behave at high core counts across EuroHPC environments.
Read the article “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.
Thematic Research & Repositories:
11. Mathematical Infrastructure Design: Deconstructing advanced numerical methods to ensure seamless parallel execution and multi-scale physical grid flexibility.
Read the article “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. Bypassing LLM Inference Bottlenecks: Investigating thread execution boundaries and matrix calculations (SpMM/SDDMM) on multi-core architectures to accelerate deep learning inference and remove memory stalls.
Read the document “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. The Xyst Fluid Dynamics Code: Showcasing our open-source, task-parallel code optimized for complex-geometry 3D compressible flow simulations utilizing the Charm++ runtime system.
Read the open-access article “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. The mUQSA Uncertainty Toolkit: Reducing the immense computational footprint of heavy Sensitivity Analysis and Uncertainty Quantification (UQ) through an accessible, web-based tool.
Read the chapter “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. Heterogeneous Pipeline Orchestration: Streamlining distributed application pipelines while maintaining rigorous validation metrics across diverse European computing sites.
Read the article “Profiling and Optimisation of Multicard GPU Machine Learning Jobs” by Marcin Lawenda, Kyrylo Khloponin, Krzesimir Samborski, Łukasz Szustak on the Journal Wiley – Concurrency and Computation: Practice and Experience.
https://onlinelibrary.wiley.com/doi/10.1002/cpe.70196
16. Open Science Verification Repositories: Access the raw performance data, benchmarking parameters, and code execution logs supporting our consortium’s scaling validations.
Explore the repositories “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 modeling 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.
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