EE-HPC is testing an approach for improving energy efficiency in HPC systems by automatically regulating system parameters and settings based on current job requirements.
InHPC-DE furthers the federation of the three national HPC centres in Germany, addresses new requirements such as security, and evaluates the Gaia-X ecosystem in the context of high-performance computing.
exaFOAM is working to reduce bottlenecks in performance scaling for computational fluid dynamics (CFD) applications on massively parallel high-performance computing (HPC) systems.
This consortium of academic institutes, HPC centers, and industrial partners in Europe and Brazil is developing novel algorithms and state-of-the-art codes to support the development of more efficient technologies for wind power.
This project coordinates strategic collaboration and outreach among EU-funded Centres of Excellence to more efficiently exploit the benefits of extreme scale applications for addressing scientific, industrial, or societal challenges.
ChEESE developed European flagship codes for upcoming pre-exascale and exascale supercomputing systems, focusing on Earth science fields such as computational seismology, magnetohydrodynamics, physical volcanology, tsunamis, and earthquake monitoring.
Eurolab4HPC2 worked to promote the consolidation of European research excellence in exascale HPC systems.
SiVeGCS coordinates and ensures the availability of HPC resources of the Gauss Centre for Supercomputing, addressing issues related to funding, operation, training, and user support across Germany's national HPC infrastructure.
The main goal of ExaFLOW is to address key algorithmic challenges in CFD (Computational Fluid Dynamics) to enable simulation at exascale, guided by a number of use cases of industrial relevance, and to provide open-source pilot implementations.
The Mont-Blanc project aims to design a new type of computer architecture capable of setting future HPC standards, built from energy-efficient solutions used in embedded and mobile devices.
DASH aims to ease the efficient programming of future supercomputing systems for data-intensive applications. These systems will be characterized by their extreme scale and a multi-level hierarchical organization.
Today, exascale computers are characterized by billion-way parallelism. Computing on such extreme scale requires methods that scale perfectly and have optimal complexity. This project brings together several crucial aspects of extreme scale solving.
