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The theory of divide-and-conquer parallelization has been well-studied in thepast, providing a solid basis upon which to explore different approaches to theparallelization of merge sort in Python. Python's simplicity and extensiveselection of libraries make it the most popular scientific programminglanguage, so it is a fitting language in which to implement and analyze thesealgorithms.  In this paper, we use Python packages multiprocessing and mpi4py to implementseveral different parallel merge sort algorithms. Experiments are conducted onan academic supercomputer, upon which benchmarks are performed using Cloudmesh.We find that hybrid multiprocessing merge sort outperforms several otheralgorithms, achieving a 1.5x speedup compared to the built-in Python sorted()and a 34x speedup compared to sequential merge sort. Our results provideinsight into different approaches to implementing parallel merge sort in Pythonand contribute to the understanding of general divide-and-conquerparallelization in Python on both shared and distributed memory systems.

Approaches to the Parallelization of Merge Sort in Python