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Accelerating High-Dimensional ANN Search via Skipping Redundant Distance Computations
Summary: Shows estimation-and-testing DCOs become costly under SIMD and best-first search forces many redundant DCOs. SkipComputing uses subspace-based candidate filtering, distance-decomposition lower bounds, and layout tweaks to skip redundant SIMD DCOs, achieving up to 6× vs HNSW and 2.7× vs SOTA.
(summarized by gpt-5-mini on Feb 11 2026)
Paper ID
hcba1211e9aa94b13
Venue
SIGMOD
Year
2026
Pagerank
4.9793485e-05
Overall Rank
10,540 | 29.14%
DOI
10.1145/3769753
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BibTeX Citation
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@inproceedings{song_sigmod26,
title = {{Accelerating High-Dimensional ANN Search via Skipping Redundant Distance Computations}},
author = {Song, Ziwen and Wang, Bin and Yang, Xiaochun},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3769753},
url = {https://dl.acm.org/doi/10.1145/3769753},
year = {2026}
}
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Outgoing Citations (Sorted by Pagerank)
Showing 29 of 29 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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Pagerank
45
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1998
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A Comprehensive Survey and Experimental Comparison of Graph-Based Approximate Nearest Neighbor Search
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576
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804
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839
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2,871
Graph-Based Vector Search: An Experimental Evaluation of the State-of-the-Art
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3,230
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3,290
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3,325
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