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Accelerating Product Quantization Query Execution Runtime

Summary: Reviews two SIMD-accelerated Product Quantization techniques for ANN search; proposes a hardware-agnostic algorithm. Competitive runtime with SIMD yet hardware-agnostic, while mitigating accuracy loss and encoding-bit budget constraints. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6074
Venue
SIGMOD
Year
2021
Pagerank
5.3985954e-05
Overall Rank
8,622 | 40.85%
DOI
10.1145/3448016.3450574

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{edian_sigmod21,
        title = {{Accelerating Product Quantization Query Execution Runtime}},
        author = {Edian, Ikraduya},
        series = {{SIGMOD} '21},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3448016.3450574},
        url = {https://dl.acm.org/doi/10.1145/3448016.3450574},
        year = {2021}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
4,430 PQCache: Product Quantization-based KVCache for Long Context LLM Inference 2025 SIGMOD 6.7091071e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 6 of 6 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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