Reflections From Previous Training Events

Contents of this documents and quicklinks：

1. Julia

  • deeper HPC related issues with Julia
  • developing programs locally that targets HPC i.e. use of Docker and/or AppTainer
  • Writing Julia for Python programmers
  • ML using Julia (SciML)
    • how about split ML part in Julia workshops into a separate ML-using-Julia lesson
    • Francesco?
  • Julia for data scientist and review of some packages for CAE and other demanding projects (ray tracing, CFD machine learning …)

2. Python

Expansion of High Performance Data Analytics in Python into to three workshop: https://hackmd.io/@yonglei/python-workshops

    1. Python HPDA
    • If we split them, we could also cover more things about big data storage and retrieval (S3, databases, tips on parallel file systems…) (Francesco)
    • data management
    • data storage
    • database
    1. Python HPC
    • it would be beneficial to have comparison of all parallelization methods and best use cases as a summary
    • like an overview of common data formats
    • Someone in the feedback mentioned having a KB of tips and tricks, sounds interesting! (Francesco)
    • ashwin!
    1. Python ML/DL
    • Practical machine learning
    • Practical deep learning
    1. a blogpost about data format/management in all eurohpc systems

3. AI/ML/DL

lesson material practical deep learning from csc

  • theory on ML and DL at a deeper level
  • https://github.com/csc-training/intro-to-dl
  • more ML algorithms/applications
    • workshops at intermediate level
  • model optimization on AMD GPUs
  • how to make use of GPU resources
  • measure the performance of network
    • performance issues
  • it will be interesting to have a workshop to learn to use alpha fold
  • advance DL on specific models and application on prace machines for instance
    • rotations about CASTIEL2 course
  • wrap models in web server (Flask, FastAPI, etc) ONNX Runtime Sagemaker/Google Cloud ML/Azure ML
  • explainability on different models, further hands-on with new model architectures, best practices about project structuring
  • ML/AI (tensorflow/PyTorch)
  • DL transformers
  • AI for chemistry
    • maybe we can arrange more events from AI Factories perspective like AI4Science
  • Comparing different Deep-Learning techniques on the same dataset, training in parrallel and on a single CPU
    • start from webinars focusing on regression problems, classification problems, etc.

4. Best practice HPC training

  • more on pedagogy, exercise design, backwards lesson design, hybrid training
  • how to tackle very technical teaching that might be boring for some students
  • pedagogy tips and lesson development sessions
  • training ecosystems
  • part of evita

5. CPU/GPU programming

  • OpenACC-CUDA
    • arrange one workshop at H2
  • MPI
    • piggy-back with our collaborators
  • OpenMP offloading
  • OpenMP with HIP
  • Hybrid programming
  • CUDA-Aware MPI
  • Modern C++ for scientific computing
  • Profiling and code optimization
  • Solving specific physical problems
    • we may consider to write blogposts to solve specific problems using varied programming models
    • like DFT, MD, CFD computations
  • Profiling for bottlenecks
  • Programming FPGA
  • Programming using other accelerators
  • SYCL concurrent programming all system resources (CPU+GPUs from multiple vendors+accelerators…
  • RUST programming
  • GPU toolchains and libraries

6. Quantum computing

  • QC testing
  • quantum machine learning
  • quantum annealing, QC/HPC integration
  • QC vs. other reversible computing paradigms, reversibility in HPC
  • applied quantum programming for climate and machine learning
  • integration of QC in HPC
  • Quantum Error Correction
  • Quantum-safe encryption

7. HPC applications

  • quantum chemistry
    • vasp
    • emto
    • quantum espresso
    • sista
    • yambo
    • bigDFT
  • HPC optimization for molecular dynamics
  • QM/MM/MD methods
  • more examples for CFD calculations
    • openFOAM
    • …

8. Programming tools

  • a workshop on containers like docker and singularity
  • using dockers for training
    • can we have webinars for this topic?
  • software control using Git
Select a repo