Summary

This course connects several approaches to parallel computing in Python into one decision framework. Threads, processes, MPI, Snakemake, ipyparallel, GPUs, and Dask each solve different performance problems, and each comes with overheads and correctness concerns.

Effective parallel programming starts by understanding the workload: whether it is I/O-bound or CPU-bound, whether tasks are independent, whether data must be shared or communicated, whether the computation can exploit GPU parallelism, and whether the dataset should be processed lazily in chunks. The course emphasizes common pitfalls such as GIL limitations, race conditions, excessive process communication, host-device transfer overhead, and poor Dask chunking.

After completing the material, learners should be able to choose an appropriate parallel strategy, test it against a serial baseline, interpret speedups and slowdowns, and apply MPI, Snakemake, ipyparallel, GPU programming, and Dask to realistic scientific and engineering workloads.