Coupling machine learning with HPC simulation

Presenter: Harvey Richardson(HPE)
Co-author: Alessandro Rigazzi (HPE)

Extra materials

• Presentation slides

• Recent LUMI User Coffee Break seminar on SmartSim

• References from the slides:

  • Language Interoperability

    • f2py and fmodpy intro

    • forpy

    • pybind11

    • Talk: "Reducing the overhead of coupling machine learning models between Python and Fortran" YouTube video and slides

  • Interoperability at framework level:

    • FTorch: Torch inference library for Fortran on GitHub

    • Fortran Keras Bridge, tensorflow but not very active on GitHub and paper on arXiv

  • CrayLabs SmartSim:

    • CrayLabs SmartSim on GitHub

    • SmartSim Example Zoo on GitHub

    • Archer2 webinar "Exploring new computations frontiers with SmartSim"

  • Examples

    • AI and Weather Forcasting at ECMWF

    • Co-development of a SmartSim module for OpenFOAM

    • Examples from Density Functional Theory

      • Pure non-local machine-learned density functional theory for electron correlation

      • Large-Scale Materials Modeling at Quantum Accuracy

    • DeepDriveMD example of selecting "promising" protein folding solutions from an ensemble

      • Paper DeepDriveMD: Deep-Learning Driven Adaptive Molecular Simulations for Protein Folding

    • Nobel Prize in Chemistry 2024 for protein folding using Alphafold and other tools

      • Nobel press release and additional technical document 1 and additional technical document 2

Q&A

/