Efficient String Sort with Multi-Character Encoding and Adaptive Sampling
Summary: Proposes a multi-character encoding to shrink radix in string sorting, leveraging sparse alphabet use and distinguishing-prefix sparsity. Paired with adaptive sampling, it blends radix sort with sample sort, delivering ~4.85x STL, ~1.5x state-of-the-art radix, and competitive parallel performance (pS5). (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Wen Jin (Independent)
- 2. Weining Qian (East China Normal University)
- 3. Aoying Zhou (East China Normal University)
BibTeX Citation
@inproceedings{jin_sigmod21,
title = {{Efficient String Sort with Multi-Character Encoding and Adaptive Sampling}},
author = {Jin, Wen and Qian, Weining and Zhou, Aoying},
series = {{SIGMOD} '21},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3448016.3457319},
url = {https://dl.acm.org/doi/10.1145/3448016.3457319},
year = {2021}
}
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,865 | The Case for a Learned Sorting Algorithm | 2020 | SIGMOD | 7.0621718e-05 |
| 4,021 | PARADIS: An Efficient Parallel Algorithm for In-place Radix Sort | 2015 | VLDB | 6.9501893e-05 |
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