Parallel Python: multithreading, multiprocessing and parallel python programming for HPC: Glossary

Key Points

1. Accelerating Python	SIH is availble to researchers to help them research! Many ways to make Python go faster.
2. Simple methods	Understand there are different ways to accelerate The best method depends on your algorithms, code and data
3. Connecting to Artemis HPC	Several methods and tools to connect to a remote machine Get access to more resources than your local computer
4. Traditional python approaches to multi cpu and nodes	load multiprocessing library to execute a function in a parallel manner
5. Intro to Dask and Dask Dataframes	Dask builds on numpy and pandas APIs but operates in a parallel manner Computations are by default lazy and must be triggered - this reduces unneccessary computation time
6. Introducing dask Bag and more dask examples	Dask Bag uses map filter and group by operations on python objects or semi/unstrucutred data dask.multiprocessing is under the hood Xarray for holding scientific data

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