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LICENSE (1070, 2022-12-10)
Project.toml (358, 2022-12-10)
deploy_notebooks.jl (232, 2022-12-10)
figures (0, 2022-12-10)
figures\Stokes2D_vep.gif (583410, 2022-12-10)
figures\cpu_gpu_evo.png (102490, 2022-12-10)
figures\gpu.png (118222, 2022-12-10)
figures\ice2.png (239454, 2022-12-10)
figures\logo2.png (4351, 2022-12-10)
figures\mem_wall.png (164397, 2022-12-10)
figures\parallelstencil.png (24113, 2022-12-10)
figures\ps_igg_perf.png (73055, 2022-12-10)
figures\two_lang.png (136696, 2022-12-10)
julia-workshop-agu22.ipynb (28928, 2022-12-10)
julia-workshop-agu22.jl (21694, 2022-12-10)

# Julia AGU 2022 workshop Using Julia and GPU programming for numerical modelling “ **SCIWS26: Porting Machine Learning and Modeling from a Laptop to a Supercomputer** ## Automatic notebook generation The presentation slides and the demo notebook are self-contained in a Jupyter notebook [julia-workshop-agu22.ipynb](https://github.com/luraess/julia-agu-2022/blob/master/julia-workshop-agu22.ipynb) that can be auto-generated using literate programming by deploying the [julia-workshop-agu22.jl](https://github.com/luraess/julia-agu-2022/blob/master/julia-workshop-agu22.jl) script. To reproduce: 1. Clone this git repo 2. Open Julia and resolve/instantiate the project ```julia-repl using Pkg Pkg.activate(@__DIR__) Pkg.resolve() Pkg.instantiate() ``` 3. Run the deploy script ```julia-repl julia> using Literate julia> include("deploy_notebooks.jl") ``` 4. Then using IJulia, you can launch the notebook and get it displayed in your web browser: ```julia-repl julia> using IJulia julia> notebook(dir=pwd()) ``` _To view the notebook as slide, you need to install the [RISE](https://github.com/luraess/julia-agu-2022/blob/master/https://rise.readthedocs.io/en/stable/installation.html) plugin_ ## Resources #### Packages - [ParallelStencil.jl](https://github.com/luraess/julia-agu-2022/blob/master/https://github.com/omlins/ParallelStencil.jl) - [ImplicitGlobalGrid.jl](https://github.com/luraess/julia-agu-2022/blob/master/https://github.com/eth-cscs/ImplicitGlobalGrid.jl) #### Papers - Bridging HPC Communities through the Julia Programming Language: https://doi.org/10.48550/ARXIV.2211.02740 - High-performance xPU Stencil Computations in Julia: https://doi.org/10.48550/arXiv.2211.15634 - Distributed Parallelization of xPU Stencil Computations in Julia: https://doi.org/10.48550/arXiv.2211.15716 #### Courses and resources - ETHZ course on solving PDEs with GPUs: https://pde-on-gpu.vaw.ethz.ch - Advanced GPU HPC optimisation course at CSCS: https://github.com/omlins/julia-gpu-course and https://github.com/maleadt/cscs_gpu_course/ - More [here](https://github.com/luraess/julia-agu-2022/blob/master/https://pde-on-gpu.vaw.ethz.ch/extras/#extra_material) #### Misc - Frontier GPU multi-physics solvers: https://ptsolvers.github.io/GPU4GEO/

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