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GPU-Native Approximate Nearest Neighbor Search with IVF-RaBitQ: Fast Index Build and Search

Summary: GPU-native IVF-RaBitQ combines IVF clustering with low-bit RaBitQ quantization for fast, compact ANNS without raw-vector reranking. Fused GPU kernels deliver ~3× CAGRA QPS, 14.7× faster builds, and >4.5× IVF-PQ throughput at ~0.95 recall. (summarized by gpt-5.6-luna on Aug 28 2026)

Paper ID
hcf9fca992e45fef6
Venue
VLDB
Year
2026
Pagerank
4.9793485e-05
Overall Rank
10,873 | 26.90%
DOI
10.14778/3836663.3836701

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BibTeX Citation

@article{shi_vldb26,
        title = {{GPU-Native Approximate Nearest Neighbor Search with IVF-RaBitQ: Fast Index Build and Search}},
        author = {Shi, Jifan and Gao, Jianyang and Xia, James and Fehér, Tamás Béla and Long, Cheng},
        journal = {PVLDB},
        series = {{VLDB} '26},
        volume = {19},
        number = {11},
        pages = {3454--3467},
        doi = {10.14778/3836663.3836701},
        url = {https://doi.org/10.14778/3836663.3836701},
        year = {2026}
}

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