Many users of HPC systems are also Python programmers. Python is a great programming language for prototyping data analyses and simulations, but things become more challenging when trying to leverage cross-node and within-node parallelism. In this tutorial, we present the general-purpose Chapel programming language for productive, parallel programming. Participants can experiment with Chapel code examples from applications such as k-mer counting, solving a diffusion PDE, sorting, and image processing. For hands-on activities, we provide a container for quick setup and instructions on how to use Chapel on the UArizona HPC systems. Active learning exercises such as online multiple choice about converting common Python patterns into Chapel code enable participants to check what they have learned. Throughout the tutorial, existing large applications written in Chapel are highlighted with quotes from their developers and example code snippets showing Chapel usage in production. We also give a brief introduction to Chapel’s newfound support for GPU programming. Come join us for a fun couple of hours exploring how to write parallel programs in a productive and performant way!
Prerequisites: Please install podman (https://podman.io/) on your laptop beforehand or bring along a friend who has it installed on their laptop and is willing to share. Here is how you could install and start it on a mac:
brew install podman // ignore the llvm15 dep error
podman machine init
podman machine start
podman machine stop // what you can use to stop it
Here are the commands you can use to do an initial test of chapel ahead of time if you would like:
podman pull docker.io/chapel/chapel // takes about 3 minutes
echo ‘writeln(“Hello, world!”);’ > hello.chpl
podman run –rm -v “$PWD”:/myapp -w /myapp chapel/chapel chpl -o hello hello.chpl
podman run –rm -v “$PWD”:/myapp -w /myapp chapel/chapel ./hello
If you have any questions, contact us on Gitter (https://gitter.im/chapel-lang/chapel) or Discourse (https://chapel.discourse.group/).