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This work analyzes and parallelizes LearnedSort, the novel algorithm thatsorts using machine learning models based on the cumulative distributionfunction. LearnedSort is analyzed under the lens of algorithms withpredictions, and it is argued that LearnedSort is a learning-augmentedSampleSort. A parallel LearnedSort algorithm is developed combining LearnedSortwith the state-of-the-art SampleSort implementation, IPS4o. Benchmarks onsynthetic and real-world datasets demonstrate improved parallel performance forparallel LearnedSort compared to IPS4o and other sorting algorithms.

LearnedSort as a learning-augmented SampleSort: Analysis and Parallelization