Mauro Werder

In the Fall Semester 2021 at ETH Zurich, we designed and taught a new course: **Solving PDEs in parallel on GPUs with Julia**. We present technical and teaching experiences we gained: we look at our tech-stack `CUDA.jl`, `ParallelStencils.jl` and `ImplictGlobalGrid.jl` for GPU-computing; and `Franklin.jl`, `Literate.jl`, `IJulia.jl`/Jupyter for web, slides, and exercises. We look into the crash-course in Julia, teaching software-engineering (git, CI) and project-based student evaluations.

In the Fall Semester 2021 at ETH Zurich, we designed and taught a new Master-level course: [**Solving PDEs in parallel on GPUs with Julia**](https://eth-vaw-glaciology.github.io/course-101-0250-00/).

We had prior experience in teaching workshops and individual lectures based on Julia, this was our first end-to-end Julia-based lecture course. It filled a niche at ETH Zurich, Switzerland: namely numerical GPU computing for the domain scientists.

Whilst we had great prior experience with the GPU tech-stack used (we're developing part of it), we had to learn much on the presentational tech-stack to create a website, slides and assignments. The presentation will focus on both the GPU-stack (`CUDA.jl`, `ParallelStencils.jl` and `ImplictGlobalGrid.jl`) and the presentation-stack (`Literate.jl`, `Franklin.jl`, `IJulia.jl`/Jupyter).

Co-authors: Mauro Werder¹ ² , Samuel Omlin³

¹ Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) | ² ETH Zurich | ³ Swiss National Supercomputing Centre (CSCS)

Mauro Werder

Talks:

Teaching GPU computing, experiences from our Master-level course

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