Dask on HPC

Recently I saw that Dask, a distributed Python library, created some really handy wrappers for running Dask projects on a High-Performance Computing Cluster, HPC.

Most people who use HPC are pretty well versed in technologies like MPI, and just generally abusing multiple compute nodes all at once, but I think technologies like Dask are really going to be game-changers in the way we all work. Because really, who wants to write MPI code or vectorize?

If you've never heard of Dask and it's awesomeness before, I think the easiest way to get started is to look at their Embarrassingly Parallel Example, and don't listen to the haters who think speeding up for loops is lame. It's a superpower!

Onward with examples!

Client and Scheduler

Firstly, these are all pretty much borrowed from the Dask Job Queues page. Pretty much, what you do, is you write your Python code as usual. Then, when you need to scale across nodes you leverage your HPC scheduler to get you some...

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