  This work analyzes and parallelizes LearnedSort, the novel algorithm that
sorts using machine learning models based on the cumulative distribution
function. LearnedSort is analyzed under the lens of algorithms with
predictions, and it is argued that LearnedSort is a learning-augmented
SampleSort. A parallel LearnedSort algorithm is developed combining LearnedSort
with the state-of-the-art SampleSort implementation, IPS4o. Benchmarks on
synthetic and real-world datasets demonstrate improved parallel performance for
parallel LearnedSort compared to IPS4o and other sorting algorithms.
