EE-HPC testet einen Ansatz zur Verbesserung der Energieeffizienz von HPC-Systemen durch die automatische, jobspezifische Regulierung von Systemparametern und -einstellungen.
InHPC-DE treibt das Bündnis der drei nationalen HPC-Zentren in Deutschland voran, adressiert neue Anforderungen wie Sicherheit und evaluiert das Gaia-X-Ökosystem für Höchstleistungsrechnen.
Forschende in diesem Projekt reduzieren Engpässe bei der Leistungsskalierung auf massiv parallelen HPC-Systemen (High Performance Computing) von CFD- (Computational Fluid Dynamics) Anwendungen.
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 koordiniert und sichert die Verfügbarkeit der HPC-Ressourcen des Gauß-Zentrums für Höchstleistungsrechnen und befasst sich mit Fragen der Finanzierung, des Betriebs, der Ausbildung und des Nutzerservice der nationalen HPC-Infrastruktur in Deutschland.
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.
