GCS LARGE-SCALE PROJECTS

Large-scale projects and highly scalable parallel applications are characterised by large computing time requirements, not only for short time frames but often for longer time periods. Projects are currently classified as "large-scale", if they require at least 100 Mcore-h on Hawk, or 45,000 EFLOP on JUWELS, or 45 Mcore-h on SuperMUC-NG. These values correspond to 2% of the systems’ annual production in terms of estimated availability. The call for GCS Large-Scale Projects is issued twice a year and approved projects start on 1 May and 1 November, respectively.

For an overview of approved GCS Large-Scale Projects, please chose from the list below.

GCS Large-Scale Projects, Call 31, 2024/1

Computing time period for all projects of this call: May 1, 2024 - April 30, 2025

At HLRS:

  • “QCD thermodynamics with minimally doubled quarks“
    Prof. Szabolcs Borsányi, Bergische Universität Wuppertal
    HPC platform: Hawk and JUWELS Booster, JUWELS GPU

  • “Standard model's predictions on near-critical behaviour to be seen in collider experiments”
    Prof. Zoltán Fodor, Bergische Universität Wuppertal
    HPC platform: Hawk

  • “Ab initio simulation of QCD at physical quark masses and small lattice spacing”
    Prof. Harvey Meyer, Universität Mainz
    HPC platform: Hawk

  • “Analysing and modeling thermoacoustics of hydrogen-air flames and flow physics in particle images”
    Dr. Matthias Meinke, RWTH Aachen
    HPC platform: Hawk and JUWELS Booster

  • “Prediction and Modeling of Particle-Laden and Gas-Liquid Multiphase Flows”
    Dr. Matthias Meinke, RWTH Aachen
    HPC platform: Hawk

At JSC:

  • “QCD thermodynamics with minimally doubled quarks“ Prof. Szabolcs Borsányi, Bergische Universität Wuppertal HPC platform: JUWELS Booster, JUWELS GPU and Hawk

  • “Nuclear Lattice Simulations “
    Prof. Dr. Ulf-G. Meißner, Forschungszentrum Jülich GmbH
    HPC platform: JUWELS Booster and JUWELS CPU

  • “Hadronic light-by-light scattering from lattice QCD”
    Prof. Kálmán Szabó, Forschungszentrum Jülich GmbH
    HPC platform: JUWELS Booster and SuperMUC-NG

  • “Large-scale Ammonia-Hydrogen Combustion“
    Prof. Christian Hasse, Technische Universität Darmstadt
    HPC platform: JUWELS Booster and JUWELS CPU

  • “Analysing and modeling thermoacoustics of hydrogen-air flames and flow physics in particle images”
    Dr. Matthias Meinke, RWTH Aachen
    HPC platform: JUWELS Booster and Hawk

  • “DNS-Driven Development of Predictive LES Models for CO Emissions in Gas Turbines”
    Prof. Heinz Pitsch, RWTH Aachen
    HPC platform: JUWELS Booster, JUWELS CPU and SuperMUC-NG

  • “TACO-VLM: Tackling challenges in large-scale multimodal learning”
    Prof. Zeynep Akata, Helmholtz Zentrum München Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH)
    HPC platform: JUWELS Booster

  • “Fostering Advancements in AI Foundation Models and Embeddings via Unsupervised and Self-
    Supervised Learning for Downstream Tasks in Earth Observation”
    Prof. Dr. Gabriele Cavallaro, Forschungszentrum Jülich GmbH
    HPC platform: JUWELS Booster

  • “Building and maintaining open multi-modal foundation models for strongly transferable learning”
    Dr. Jenia Jitsev, Forschungszentrum Jülich GmbH
    HPC platform: JUWELS Booster and JUWELS CPU

  • „OpenGPT-X: Large-Scale European Language Models“
    Prof. Georg Rehm, Deutsches Forschungszentrum für Künstliche Intelligenz
    HPC platform: JUWELS Booster and JUWELS CPU

At LRZ:

  • „Theoretical Condensed Matter Physics“
    Prof. Fakher Assaad, Universität Würzburg
    HPC platform: SuperMUC-NG

  • “Optics, Quantum Optics, Atoms, Molecules, Plasmas”
    Jun.-Prof. Maria Elena Innocenti, Ruhr-Universität Bochum
    HPC platform: SuperMUC-NG

  • „Charmonium and Confinement from Lattice QCD”
    Prof. Dr. Francesco Knechtli, Bergische Universität Wuppertal
    HPC platform: SuperMUC-NG

  • “Hadronic light-by-light scattering from lattice QCD”
    Prof. Kálmán Szabó, Forschungszentrum Jülich GmbH
    HPC platform: SuperMUC-NG and JUWELS Booster

  • “DNS-Driven Development of Predictive LES Models for CO Emissions in Gas Turbines”
    Prof. Heinz Pitsch, RWTH Aachen
    HPC platform: SuperMUC-NG and JUWELS Booster, JUWELS CPU

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