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Unfortunately, recent filtered-ANNS solutions are primarily designed for CPUs, with few exploration and limited performance of filtered-ANNS that take advantage of the massive parallelism offered by GPUs. In this paper, we present VecFlow, a novel high-performance vector filtered search system that achieves unprecedented high throughput and recall while obtaining low latency for filtered-ANNS on GPUs. We propose a novel label-centric indexing and search algorithm that significantly improves the selectivity of ANNS with filters. In addition to algorithmic level optimization, we provide architecture-aware optimizations for VecFlow's functional modules, effectively supporting both small batch and large batch queries, and single-label and multi-label query processing. Experimental results on NVIDIA A100 GPU over several public available datasets validate that VecFlow achieves 5 million QPS for recall 90%, outperforming state-of-the-art CPU-based solutions such as Filtered-DiskANN by up to 135 times. Alternatively, VecFlow can easily extend its support to high recall 99% regime, whereas strong GPU-based baselines plateau at around 80% recall.<\/jats:p>","DOI":"10.1145\/3749189","type":"journal-article","created":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T17:17:03Z","timestamp":1758647823000},"page":"1-27","source":"Crossref","is-referenced-by-count":1,"title":["VecFlow: A High-Performance Vector Data Management System for Filtered-Search on GPUs"],"prefix":"10.1145","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-9425-0655","authenticated-orcid":false,"given":"Jingyi","family":"Xi","sequence":"first","affiliation":[{"name":"SSAIL Lab, UIUC, Urbana, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9860-6325","authenticated-orcid":false,"given":"Chenghao","family":"Mo","sequence":"additional","affiliation":[{"name":"SSAIL Lab, UIUC, Urbana, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4011-8903","authenticated-orcid":false,"given":"Ben","family":"Karsin","sequence":"additional","affiliation":[{"name":"Nvidia, Honolulu, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6936-2040","authenticated-orcid":false,"given":"Artem","family":"Chirkin","sequence":"additional","affiliation":[{"name":"Nvidia, Zurich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-0270-9489","authenticated-orcid":false,"given":"Mingqin","family":"Li","sequence":"additional","affiliation":[{"name":"Microsoft, Redmond, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8165-166X","authenticated-orcid":false,"given":"Minjia","family":"Zhang","sequence":"additional","affiliation":[{"name":"SSAIL Lab, UIUC, Urbana, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,9,23]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Accessed: 04-13-2025. pgvector: Open-source vector similarity search for Postgres. https:\/\/github.com\/pgvector\/pgvector."},{"key":"e_1_2_1_2_1","unstructured":"RAPIDS AI. 2025. cuVS. https:\/\/github.com\/rapidsai\/cuvs. Accessed: 2025-01-18."},{"key":"e_1_2_1_3_1","first-page":"215","volume-title":"SIGIR","author":"Aliannejadi Mohammad","year":"2018","unstructured":"Mohammad Aliannejadi, Hamed Zamani, Fabio Crestani, and W. 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