ChEESE: the European Center of Excellence for supercomputing in geosciences

The EuroHPC Center of Excellence for Exascale in Solid Earth (ChEESE CoE, 2018–2026, DOI: 10.3030/101093038) is preparing 11 community-driven, open-source codes to run optimally on large accelerated supercomputing infrastructures (Leonardo, LUMI, MareNostrum-5). The CoE works with flagship codes in different areas of geophysics (earthquakes, tsunamis, volcanoes, magnetohydrodynamics, geodynamics, and glacier modelling), focusing on performance, scalability, CI/CD on EuroHPC systems, and portability across current and emerging hardware architectures. During 2025, the CoE has been awarded more than 1 million node-hours on GPU-accelerated systems such as Leonardo (4 NVIDIA A100 per node), LUMI-G (8 AMD MI250X per node), and MareNostrum-5 (4 NVIDIA H100 per node). The resulting simulations and use cases are being stored in data lakes together with their metadata for use by the scientific community, for example to train AI models or to be accessed through the European Plate Observing System (EPOS). All codes and applications under the ChEESE umbrella are available in open GitLab/GitHub repositories and undergo an SQAaaS process to obtain software quality badges. In addition, the project aims to enable urgent supercomputing services for emergencies during high-impact events (earthquakes, tsunamis, and volcanoes), including the associated technical challenges and recommendations on access policies. This is done in collaboration with end users such as civil protection agencies in various European countries. For example, ChEESE researchers tested an urgent supercomputing service for earthquakes during the September 19th 2025 drill, in collaboration with the Mexican Seismological Service (SSM).Funded by the European Union. This work has received funding from the European High Performance Computing Joint Undertaking (JU) and Spain, Italy, Iceland, Germany, Norway, France, Finland and Croatia under grant agreement No 101093038, ChEESE-2P, project PCI2022-134973-2 funded by MCIN/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR.