We compare two packages for performing many-task computing (MTC) in Python: IPython Parallel and Celery. We describe these packages in detail and compare their features as applied to many-task computing on a cluster, including a scaling study using over 12,000 cores and several thousand tasks. We use mpi4py as a baseline for our comparisons. Our results suggest that Python is an excellent way to manage many-task computing and that no single technique is the obvious choice in every situation.
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