SVFusion: A CPU-GPU Co-Processing Architecture for Large-Scale Real-Time Vector Search

Summary: SVFusion co-designs a hierarchical GPU–CPU–disk ANN index for high-throughput search with online updates, using workload-aware GPU caching, CUDA streams, adaptive resource management, and concurrency control. It delivers up to 20.9× higher throughput and 50.7× lower latency while preserving recall. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID: 14448
Venue: VLDB
Year: 2026
Pagerank: 5.093636e-05
Overall Rank: 10,512 | 27.88%
DOI: 10.14778/3796195.3796216

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

1. Yuchen Peng (Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security; Zhejiang University)
2. Dingyu Yang (Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security; Zhejiang University)
3. Zhongle Xie (Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security; Zhejiang University)
4. Ji Sun (Huawei)
5. Lidan Shou (Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security; Zhejiang University)
6. Ke Chen (Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security; Zhejiang University)
7. Gang Chen (Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security; Zhejiang University)

BibTeX Citation

@article{peng_vldb26,
        title = {{SVFusion: A CPU-GPU Co-Processing Architecture for Large-Scale Real-Time Vector Search}},
        author = {Peng, Yuchen and Yang, Dingyu and Xie, Zhongle and Sun, Ji and Shou, Lidan and Chen, Ke and Chen, Gang},
        journal = {PVLDB},
        series = {{VLDB} '26},
        volume = {19},
        number = {5},
        pages = {1074--1087},
        doi = {10.14778/3796195.3796216},
        url = {https://doi.org/10.14778/3796195.3796216},
        year = {2026}
}

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank

Outgoing Citations (Sorted by Pagerank)

Showing 16 of 16 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
93	Fast Approximate Nearest Neighbor Search With The Navigating Spreading-out Graph	2019	VLDB	0.00034701237
332	Query-Aware Locality-Sensitive Hashing for Approximate Nearest Neighbor Search	2016	VLDB	0.00020920444
369	Locality-Sensitive Hashing Scheme Based on Dynamic Collision Counting	2012	SIGMOD	0.00019945234
398	A Comprehensive Survey and Experimental Comparison of Graph-Based Approximate Nearest Neighbor Search	2021	VLDB	0.00019194947
1,357	RaBitQ: Quantizing High-Dimensional Vectors with a Theoretical Error Bound for Approximate Nearest Neighbor Search	2024	SIGMOD	0.00011043994
1,546	PM-LSH: A Fast and Accurate LSH Framework for High-Dimensional Approximate NN Search	2020	VLDB	0.00010407159
1,589	Manu: A Cloud Native Vector Database Management System	2022	VLDB	0.00010264469
1,735	High-Dimensional Approximate Nearest Neighbor Search: with Reliable and Efficient Distance Comparison Operations	2023	SIGMOD	9.9004924e-05
2,303	SeRF: Segment Graph for Range-Filtering Approximate Nearest Neighbor Search	2024	SIGMOD	8.7783079e-05
2,534	ELPIS: Graph-Based Similarity Search for Scalable Data Science	2023	VLDB	8.4561875e-05
3,108	Practical and Asymptotically Optimal Quantization of High-Dimensional Vectors in Euclidean Space for Approximate Nearest Neighbor Search	2025	SIGMOD	7.7499741e-05
4,712	Revisiting the Index Construction of Proximity Graph-Based Approximate Nearest Neighbor Search	2025	VLDB	6.5506649e-05
7,124	GTI: Graph-based Tree Index with Logarithm Updates for Nearest Neighbor Search in High-Dimensional Spaces	2025	VLDB	5.6961208e-05
7,376	LiteHST: A Tree Embedding based Method for Similarity Search	2023	SIGMOD	5.6298131e-05
8,872	Cost-Effective, Low Latency Vector Search with Azure Cosmos DB	2025	VLDB	5.35418e-05
9,918	HAKES: Scalable Vector Database for Embedding Search Service	2025	VLDB	5.1955087e-05

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