The Case for a Learned Sorting Algorithm
Summary: A learned distribution sort maps keys to positions via a CDF model, then finishes with a near-sorted pass. Up to 1B doubles, normally distributed, it yields 3.38x STL, 1.49x Radix, 5.54x TimSort, showing learned sorting as a practical primitive. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Ani Kristo (Brown University)
- 2. Kapil Vaidya (Massachusetts Institute of Technology)
- 3. Uğur Çetintemel (Brown University)
- 4. Sanchit Misra (Intel)
- 5. Tim Kraska (Massachusetts Institute of Technology)
BibTeX Citation
@inproceedings{kristo_sigmod20,
title = {{The Case for a Learned Sorting Algorithm}},
author = {Kristo, Ani and Vaidya, Kapil and Çetintemel, Uğur and Misra, Sanchit and Kraska, Tim},
series = {{SIGMOD} '20},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3318464.3389752},
url = {https://dl.acm.org/doi/10.1145/3318464.3389752},
year = {2020}
}
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