{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,11]],"date-time":"2026-01-11T04:03:02Z","timestamp":1768104182790,"version":"3.49.0"},"reference-count":81,"publisher":"Association for Computing Machinery (ACM)","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. ACM Manag. Data"],"published-print":{"date-parts":[[2025,12,4]]},"abstract":"<jats:p>Approximate nearest neighbor search (ANNS) in high-dimensional vector spaces has a wide range of real-world applications. Numerous methods have been proposed to handle ANNS efficiently, while graph-based indexes have gained prominence due to their high accuracy and efficiency. However, the indexing overhead of graph-based indexes remains substantial. With exponential growth in data volume and increasing demands for dynamic index adjustments, this overhead continues to escalate, posing a critical challenge.<\/jats:p>\n                  <jats:p>\n                    In this paper, we introduce Tagore, a fas\n                    <jats:bold>T<\/jats:bold>\n                    library\n                    <jats:bold>a<\/jats:bold>\n                    ccelerated by\n                    <jats:bold>G<\/jats:bold>\n                    PUs f\n                    <jats:bold>or<\/jats:bold>\n                    graph ind\n                    <jats:bold>e<\/jats:bold>\n                    xing, which has powerful capabilities of constructing refinement-based graph indexes such as NSG and Vamana. We first introduce GNN-Descent, a GPU-specific algorithm for efficient k-Nearest Neighbor (k-NN) graph initialization. GNN-Descent speeds up the similarity comparison by a two-phase descent procedure and enables highly parallelized neighbor updates. Next, aiming to support various k-NN graph pruning strategies, we formulate a universal pruning procedure termed CFS and devise two generalized GPU kernels for parallel processing complex dependencies in neighbor relationships. For large-scale datasets exceeding GPU memory capacity, we propose an asynchronous GPU-CPU-disk indexing framework with a cluster-aware caching mechanism to minimize the I\/O pressure on the disk. Extensive experiments on 7 real-world datasets exhibit that Tagore achieves 1.32x to 112.79x speedup while maintaining the index quality.\n                  <\/jats:p>","DOI":"10.1145\/3769825","type":"journal-article","created":{"date-parts":[[2025,12,6]],"date-time":"2025-12-06T04:32:13Z","timestamp":1764995533000},"page":"1-27","source":"Crossref","is-referenced-by-count":0,"title":["Scalable Graph Indexing using GPUs for Approximate Nearest Neighbor Search"],"prefix":"10.1145","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-9693-992X","authenticated-orcid":false,"given":"Zhonggen","family":"Li","sequence":"first","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8082-7398","authenticated-orcid":false,"given":"Xiangyu","family":"Ke","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4555-6232","authenticated-orcid":false,"given":"Yifan","family":"Zhu","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-6027-9633","authenticated-orcid":false,"given":"Bocheng","family":"Yu","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9792-9171","authenticated-orcid":false,"given":"Baihua","family":"Zheng","sequence":"additional","affiliation":[{"name":"Singapore Management University, Singapore, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3816-8450","authenticated-orcid":false,"given":"Yunjun","family":"Gao","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}]}],"member":"320","published-online":{"date-parts":[[2025,12,5]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"COLOR","unstructured":"2009. COLOR. http:\/\/cophir.isti.cnr.it."},{"key":"e_1_2_1_2_1","unstructured":"2010. SIFT and GIST. http:\/\/corpus-texmex.irisa.fr."},{"key":"e_1_2_1_3_1","unstructured":"2024. ANN Benchmark. https:\/\/ann-benchmarks.com\/index.html."},{"key":"e_1_2_1_4_1","first-page":"12","article-title":"Cache locality is not enough: High-performance nearest neighbor search with product quantization fast scan","volume":"9","author":"Andr\u00e9 Fabien","year":"2016","unstructured":"Fabien Andr\u00e9, Anne-Marie Kermarrec, and Nicolas Le Scouarnec. 2016. Cache locality is not enough: High-performance nearest neighbor search with product quantization fast scan. In PVLDB, Vol. 9. 12.","journal-title":"PVLDB"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.14778\/3685800.3685862"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.14778\/3583140.3583166"},{"key":"e_1_2_1_7_1","first-page":"2055","article-title":"Efficient indexing of billion-scale datasets of deep descriptors","author":"Babenko Artem","year":"2016","unstructured":"Artem Babenko and Victor Lempitsky. 2016. Efficient indexing of billion-scale datasets of deep descriptors. In CVPR. 2055-2063.","journal-title":"CVPR."},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.14778\/3632093.3632107"},{"key":"e_1_2_1_9_1","article-title":"Fast Approximate kNN Graph Construction for High Dimensional Data via Recursive Lanczos Bisection","volume":"10","author":"Chen Jie","year":"2009","unstructured":"Jie Chen, Haw-ren Fang, and Yousef Saad. 2009. Fast Approximate kNN Graph Construction for High Dimensional Data via Recursive Lanczos Bisection. Journal of Machine Learning Research 10, 9 (2009).","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_2_1_10_1","first-page":"3225","article-title":"Finger: Fast inference for graph-based approximate nearest neighbor search","author":"Chen Patrick","year":"2023","unstructured":"Patrick Chen, Wei-Cheng Chang, Jyun-Yu Jiang, Hsiang-Fu Yu, Inderjit Dhillon, and Cho-Jui Hsieh. 2023. Finger: Fast inference for graph-based approximate nearest neighbor search. In WWW. 3225-3235.","journal-title":"WWW."},{"key":"e_1_2_1_11_1","volume-title":"SPTAG: A library for fast approximate nearest neighbor search. https:\/\/github.com\/Microsoft\/SPTAG","author":"Chen Qi","year":"2018","unstructured":"Qi Chen, Haidong Wang, Mingqin Li, Gang Ren, Scarlett Li, Jeffery Zhu, Jason Li, Chuanjie Liu, Lintao Zhang, and Jingdong Wang. 2018. SPTAG: A library for fast approximate nearest neighbor search. https:\/\/github.com\/Microsoft\/SPTAG"},{"key":"e_1_2_1_12_1","first-page":"5199","article-title":"Spann: Highly-efficient billion-scale approximate nearest neighborhood search","volume":"34","author":"Chen Qi","year":"2021","unstructured":"Qi Chen, Bing Zhao, Haidong Wang, Mingqin Li, Chuanjie Liu, Zengzhong Li, Mao Yang, and Jingdong Wang. 2021. Spann: Highly-efficient billion-scale approximate nearest neighborhood search. NeurIPS 34 (2021), 5199-5212.","journal-title":"NeurIPS"},{"key":"e_1_2_1_13_1","unstructured":"Wei Dong. 2011. KGraph: A Library for Approximate Nearest Neighbor Search. https:\/\/github.com\/aaalgo\/kgraph."},{"key":"e_1_2_1_14_1","first-page":"577","article-title":"Efficient k-nearest neighbor graph construction for generic similarity measures","author":"Dong Wei","year":"2011","unstructured":"Wei Dong, Charikar Moses, and Kai Li. 2011. Efficient k-nearest neighbor graph construction for generic similarity measures. In WWW. 577-586.","journal-title":"WWW."},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.14778\/3547305.3547308"},{"key":"e_1_2_1_16_1","volume-title":"Efanna: An extremely fast approximate nearest neighbor search algorithm based on knn graph. arXiv","author":"Fu Cong","year":"2016","unstructured":"Cong Fu and Deng Cai. 2016. Efanna: An extremely fast approximate nearest neighbor search algorithm based on knn graph. arXiv (2016)."},{"key":"e_1_2_1_17_1","first-page":"4139","article-title":"High dimensional similarity search with satellite system graph: Efficiency, scalability, and unindexed query compatibility","volume":"44","author":"Fu Cong","year":"2021","unstructured":"Cong Fu, Changxu Wang, and Deng Cai. 2021. High dimensional similarity search with satellite system graph: Efficiency, scalability, and unindexed query compatibility. TPAMI 44, 8 (2021), 4139-4150.","journal-title":"TPAMI"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.14778\/3303753.3303754"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3589282"},{"key":"e_1_2_1_20_1","first-page":"1","article-title":"RaBitQ: quantizing high-dimensional vectors with a theoretical error bound for approximate nearest neighbor search","volume":"2","author":"Gao Jianyang","year":"2024","unstructured":"Jianyang Gao and Cheng Long. 2024. RaBitQ: quantizing high-dimensional vectors with a theoretical error bound for approximate nearest neighbor search. SIGMOD 2, 3 (2024), 1-27.","journal-title":"SIGMOD"},{"key":"e_1_2_1_21_1","first-page":"183209","article-title":"Semantic similarity-based program retrieval: a multirelational graph perspective","volume":"18","author":"Gou Qianwen","year":"2024","unstructured":"Qianwen Gou, Yunwei Dong, YuJiao Wu, and Qiao Ke. 2024. Semantic similarity-based program retrieval: a multirelational graph perspective. FCS 18, 3 (2024), 183209.","journal-title":"FCS"},{"key":"e_1_2_1_22_1","first-page":"1","article-title":"SymphonyQG: Towards Symphonious Integration of Quantization and Graph for Approximate Nearest Neighbor Search","volume":"3","author":"Gou Yutong","year":"2025","unstructured":"Yutong Gou, Jianyang Gao, Yuexuan Xu, and Cheng Long. 2025. SymphonyQG: Towards Symphonious Integration of Quantization and Graph for Approximate Nearest Neighbor Search. SIGMOD 3, 1 (2025), 1-26.","journal-title":"SIGMOD"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2022.3161156"},{"key":"e_1_2_1_24_1","first-page":"3767","article-title":"Neos: A NVMe-GPUs Direct Vector Service Buffer in User Space","author":"Huang Yuchen","year":"2024","unstructured":"Yuchen Huang, Xiaopeng Fan, Song Yan, and Chuliang Weng. 2024. Neos: A NVMe-GPUs Direct Vector Service Buffer in User Space. In ICDE. 3767-3781.","journal-title":"ICDE."},{"key":"e_1_2_1_25_1","first-page":"585","article-title":"CXLANNS: Software-Hardware collaborative memory disaggregation and computation for Billion-Scale approximate nearest neighbor search","author":"Jang Junhyeok","year":"2023","unstructured":"Junhyeok Jang, Hanjin Choi, Hanyeoreum Bae, Seungjun Lee, Miryeong Kwon, and Myoungsoo Jung. 2023. CXLANNS: Software-Hardware collaborative memory disaggregation and computation for Billion-Scale approximate nearest neighbor search. In ATC. 585-600.","journal-title":"ATC."},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3639471"},{"key":"e_1_2_1_27_1","unstructured":"Yahoo Japan. 2018. Neighborhood Graph and Tree for Indexing High-dimensional Data. https:\/\/github.com\/yahoojapan\/NGT."},{"key":"e_1_2_1_28_1","volume-title":"Ravishankar Krishnawamy, and Rohan Kadekodi.","author":"Subramanya Suhas Jayaram","year":"2019","unstructured":"Suhas Jayaram Subramanya, Fnu Devvrit, Harsha Vardhan Simhadri, Ravishankar Krishnawamy, and Rohan Kadekodi. 2019. Diskann: Fast accurate billion-point nearest neighbor search on a single node. NeurIPS 32 (2019)."},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3581784.3607045"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.14778\/3696435.3696439"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2019.2921572"},{"key":"e_1_2_1_32_1","volume-title":"Jyothi Vedurada, et al.","author":"Khan Saim","year":"2024","unstructured":"Saim Khan, Somesh Singh, Harsha Vardhan Simhadri, Jyothi Vedurada, et al. 2024. BANG: Billion-Scale Approximate Nearest Neighbor Search using a Single GPU. arXiv (2024)."},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.14778\/3603581.3603592"},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3318464.3380600"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3284028.3284030"},{"key":"e_1_2_1_36_1","first-page":"1475","article-title":"Approximate nearest neighbor search on high dimensional data-experiments, analyses, and improvement","volume":"32","author":"Li Wen","year":"2019","unstructured":"Wen Li, Ying Zhang, Yifang Sun, Wei Wang, Mingjie Li, Wenjie Zhang, and Xuemin Lin. 2019. Approximate nearest neighbor search on high dimensional data-experiments, analyses, and improvement. TKDE 32, 8 (2019), 1475-1488.","journal-title":"TKDE"},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3419111.3421281"},{"key":"e_1_2_1_38_1","first-page":"103","article-title":"BGL:GPU-Efficient GNN training by optimizing graph data I\/O and preprocessing","author":"Liu Tianfeng","year":"2023","unstructured":"Tianfeng Liu, Yangrui Chen, Dan Li, Chuan Wu, Yibo Zhu, Jun He, Yanghua Peng, Hongzheng Chen, Hongzhi Chen, and Chuanxiong Guo. 2023. BGL:GPU-Efficient GNN training by optimizing graph data I\/O and preprocessing. In NSDI. 103-118.","journal-title":"NSDI."},{"key":"e_1_2_1_39_1","first-page":"549","article-title":"JUNO: Optimizing high-dimensional approximate nearest neighbour search with sparsity-aware algorithm and ray-tracing core mapping","author":"Liu Zihan","year":"2024","unstructured":"Zihan Liu,Wentao Ni, Jingwen Leng, Yu Feng, Cong Guo, Quan Chen, Chao Li, Minyi Guo, and Yuhao Zhu. 2024. JUNO: Optimizing high-dimensional approximate nearest neighbour search with sparsity-aware algorithm and ray-tracing core mapping. In ASPLOS. 549-565.","journal-title":"ASPLOS."},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.14778\/3489496.3489506"},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.14778\/3397230.3397240"},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.is.2013.10.006"},{"key":"e_1_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2889473"},{"key":"e_1_2_1_44_1","first-page":"470","article-title":"HET-GMP: A graph-based system approach to scaling large embedding model training","author":"Miao Xupeng","year":"2022","unstructured":"Xupeng Miao, Yining Shi, Hailin Zhang, Xin Zhang, Xiaonan Nie, Zhi Yang, and Bin Cui. 2022. HET-GMP: A graph-based system approach to scaling large embedding model training. In SIGMOD. 470-480.","journal-title":"SIGMOD."},{"key":"e_1_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2006.264"},{"key":"e_1_2_1_46_1","unstructured":"Nvidia. 2024. cuVS: Vector Search and Clustering on the GPU. https:\/\/github.com\/rapidsai\/cuvs."},{"key":"e_1_2_1_47_1","first-page":"1659","article-title":"Relative nn-descent: A fast index construction for graph-based approximate nearest neighbor search","author":"Ono Naoki","year":"2023","unstructured":"Naoki Ono and Yusuke Matsui. 2023. Relative nn-descent: A fast index construction for graph-based approximate nearest neighbor search. In MM. 1659-1667.","journal-title":"MM."},{"key":"e_1_2_1_48_1","first-page":"4236","article-title":"Cagra: Highly parallel graph construction and approximate nearest neighbor search for gpus","author":"Ootomo Hiroyuki","year":"2024","unstructured":"Hiroyuki Ootomo, Akira Naruse, Corey Nolet, Ray Wang, Tamas Feher, and Yong Wang. 2024. Cagra: Highly parallel graph construction and approximate nearest neighbor search for gpus. In ICDE. 4236-4247.","journal-title":"ICDE."},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2008.917757"},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-024-00864-x"},{"key":"e_1_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.14778\/3407790.3407834"},{"key":"e_1_2_1_52_1","first-page":"1","article-title":"Efficient approximate nearest neighbor search in multi-dimensional databases","volume":"1","author":"Peng Yun","year":"2023","unstructured":"Yun Peng, Byron Choi, Tsz Nam Chan, Jianye Yang, and Jianliang Xu. 2023. Efficient approximate nearest neighbor search in multi-dimensional databases. SIGMOD 1, 1 (2023), 1-27.","journal-title":"SIGMOD"},{"key":"e_1_2_1_53_1","first-page":"10672","article-title":"HM-ANN: Efficient billion-point nearest neighbor search on heterogeneous memory","volume":"33","author":"Ren Jie","year":"2020","unstructured":"Jie Ren, Minjia Zhang, and Dong Li. 2020. HM-ANN: Efficient billion-point nearest neighbor search on heterogeneous memory. NeurIPS 33 (2020), 10672-10684.","journal-title":"NeurIPS"},{"key":"e_1_2_1_54_1","first-page":"106","article-title":"Hgraph: a connected-partition approach to proximity graphs for similarity search","author":"Shimomura Larissa Capobianco","year":"2019","unstructured":"Larissa Capobianco Shimomura and Daniel S Kaster. 2019. Hgraph: a connected-partition approach to proximity graphs for similarity search. In DEXA. 106-121.","journal-title":"DEXA."},{"key":"e_1_2_1_55_1","volume-title":"Ravishankar Krishnaswamy, and Harsha Vardhan Simhadri.","author":"Singh Aditi","year":"2021","unstructured":"Aditi Singh, Suhas Jayaram Subramanya, Ravishankar Krishnaswamy, and Harsha Vardhan Simhadri. 2021. Freshdiskann: A fast and accurate graph-based ann index for streaming similarity search. arXiv (2021)."},{"key":"e_1_2_1_56_1","first-page":"1","article-title":"Vexless: a serverless vector data management system using cloud functions","volume":"2","author":"Su Yongye","year":"2024","unstructured":"Yongye Su, Yinqi Sun, Minjia Zhang, and Jianguo Wang. 2024. Vexless: a serverless vector data management system using cloud functions. SIGMOD 2, 3 (2024), 1-26.","journal-title":"SIGMOD"},{"key":"e_1_2_1_57_1","first-page":"165","article-title":"Legion: Automatically pushing the envelope of Multi-GPU system for Billion-Scale GNN training","author":"Sun Jie","year":"2023","unstructured":"Jie Sun, Li Su, Zuocheng Shi, Wenting Shen, Zeke Wang, Lei Wang, Jie Zhang, Yong Li, Wenyuan Yu, Jingren Zhou, et al. 2023. Legion: Automatically pushing the envelope of Multi-GPU system for Billion-Scale GNN training. In ATC. 165-179.","journal-title":"ATC."},{"key":"e_1_2_1_58_1","first-page":"321","article-title":"Hyperion","author":"Sun Jie","year":"2025","unstructured":"Jie Sun, Mo Sun, Zheng Zhang, Zuocheng Shi, Jun Xie, Zihan Yang, Jie Zhang, ZekeWang, and FeiWu. 2025. Hyperion: Co-Optimizing SSD Access and GPU Computation for Cost-Efficient GNN Training. In ICDE. 321-335.","journal-title":"In ICDE."},{"key":"e_1_2_1_59_1","first-page":"1135","article-title":"Scalable Billion-point Approximate Nearest Neighbor Search Using SmartSSDs","author":"Tian Bing","year":"2024","unstructured":"Bing Tian, Haikun Liu, Zhuohui Duan, Xiaofei Liao, Hai Jin, and Yu Zhang. 2024. Scalable Billion-point Approximate Nearest Neighbor Search Using SmartSSDs. In ATC. 1135-1150.","journal-title":"ATC."},{"key":"e_1_2_1_60_1","first-page":"171","article-title":"Towards High-throughput and Low-latency Billion-scale Vector Search via CPU\/GPU Collaborative Filtering and Re-ranking","author":"Tian Bing","year":"2025","unstructured":"Bing Tian, Haikun Liu, Yuhang Tang, Shihai Xiao, Zhuohui Duan, Xiaofei Liao, Hai Jin, Xuecang Zhang, Junhua Zhu, and Yu Zhang. 2025. Towards High-throughput and Low-latency Billion-scale Vector Search via CPU\/GPU Collaborative Filtering and Re-ranking. In FAST. 171-185.","journal-title":"FAST."},{"key":"e_1_2_1_61_1","first-page":"1929","article-title":"Fast k-nn graph construction by gpu based nn-descent","author":"Wang Hui","year":"2021","unstructured":"Hui Wang, Wan-Lei Zhao, Xiangxiang Zeng, and Jianye Yang. 2021. Fast k-nn graph construction by gpu based nn-descent. In CIKM. 1929-1938.","journal-title":"CIKM."},{"key":"e_1_2_1_62_1","first-page":"2614","article-title":"Milvus: A purpose-built vector data management system","author":"Yi Xiaomeng","year":"2021","unstructured":"JianguoWang, Xiaomeng Yi, Rentong Guo, Hai Jin, Peng Xu, Shengjun Li, XiangyuWang, Xiangzhou Guo, Chengming Li, Xiaohai Xu, et al. 2021. Milvus: A purpose-built vector data management system. In SIGMOD. 2614-2627.","journal-title":"SIGMOD."},{"key":"e_1_2_1_63_1","volume-title":"Accelerating Graph Indexing for ANNS on Modern CPUs. arXiv","author":"Wang Mengzhao","year":"2025","unstructured":"Mengzhao Wang, Haotian Wu, Xiangyu Ke, Yunjun Gao, Yifan Zhu, and Wenchao Zhou. 2025. Accelerating Graph Indexing for ANNS on Modern CPUs. arXiv (2025)."},{"key":"e_1_2_1_64_1","first-page":"1","article-title":"Starling: An i\/o-efficient disk-resident graph index framework for high-dimensional vector similarity search on data segment","volume":"2","author":"Wang Mengzhao","year":"2024","unstructured":"Mengzhao Wang, Weizhi Xu, Xiaomeng Yi, Songlin Wu, Zhangyang Peng, Xiangyu Ke, Yunjun Gao, Xiaoliang Xu, Rentong Guo, and Charles Xie. 2024. Starling: An i\/o-efficient disk-resident graph index framework for high-dimensional vector similarity search on data segment. SIGMOD 2, 1 (2024), 1-27.","journal-title":"SIGMOD"},{"key":"e_1_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.14778\/3476249.3476255"},{"key":"e_1_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.14778\/3415478.3415541"},{"key":"e_1_2_1_67_1","first-page":"1813","article-title":"Speedup graph processing by graph ordering","author":"Wei Hao","year":"2016","unstructured":"Hao Wei, Jeffrey Xu Yu, Can Lu, and Xuemin Lin. 2016. Speedup graph processing by graph ordering. In SIGMOD. 1813-1828.","journal-title":"SIGMOD."},{"key":"e_1_2_1_68_1","first-page":"545","article-title":"Spfresh: Incremental in-place update for billion-scale vector search","author":"Xu Yuming","year":"2023","unstructured":"Yuming Xu, Hengyu Liang, Jin Li, Shuotao Xu, Qi Chen, Qianxi Zhang, Cheng Li, Ziyue Yang, Fan Yang, Yuqing Yang, et al. 2023. Spfresh: Incremental in-place update for billion-scale vector search. In SOSP. 545-561.","journal-title":"SOSP."},{"key":"e_1_2_1_69_1","volume-title":"Effective and General Distance Computation for Approximate Nearest Neighbor Search. arXiv","author":"Yang Mingyu","year":"2024","unstructured":"Mingyu Yang, Wentao Li, Jiabao Jin, Xiaoyao Zhong, Xiangyu Wang, Zhitao Shen, Wei Jia, and Wei Wang. 2024. Effective and General Distance Computation for Approximate Nearest Neighbor Search. arXiv (2024)."},{"key":"e_1_2_1_70_1","volume-title":"Xiyue Gao, Qianru Wang, Yanguo Peng, and Jiangtao Cui.","author":"Yang Shuo","year":"2024","unstructured":"Shuo Yang, Jiadong Xie, Yingfan Liu, Jeffrey Xu Yu, Xiyue Gao, Qianru Wang, Yanguo Peng, and Jiangtao Cui. 2024. Revisiting the index construction of proximity graph-based approximate nearest neighbor search. arXiv preprint arXiv:2410.01231 (2024)."},{"key":"e_1_2_1_71_1","first-page":"1","article-title":"AquaPipe: A Quality-Aware Pipeline for Knowledge Retrieval and Large Language Models","volume":"3","author":"Yu Runjie","year":"2025","unstructured":"Runjie Yu, Weizhou Huang, Shuhan Bai, Jian Zhou, and Fei Wu. 2025. AquaPipe: A Quality-Aware Pipeline for Knowledge Retrieval and Large Language Models. SIGMOD 3, 1 (2025), 1-26.","journal-title":"SIGMOD"},{"key":"e_1_2_1_72_1","first-page":"552","article-title":"GPU-accelerated proximity graph approximate nearest Neighbor search and construction","author":"Yu Yuanhang","year":"2022","unstructured":"Yuanhang Yu, Dong Wen, Ying Zhang, Lu Qin, Wenjie Zhang, and Xuemin Lin. 2022. GPU-accelerated proximity graph approximate nearest Neighbor search and construction. In ICDE. 552-564.","journal-title":"ICDE."},{"key":"e_1_2_1_73_1","first-page":"283","article-title":"DF-GAS: a distributed FPGA-as-a-service architecture towards billion-scale graph-based approximate nearest neighbor search","author":"Zeng Shulin","year":"2023","unstructured":"Shulin Zeng, Zhenhua Zhu, Jun Liu, Haoyu Zhang, Guohao Dai, Zixuan Zhou, Shuangchen Li, Xuefei Ning, Yuan Xie, Huazhong Yang, et al. 2023. DF-GAS: a distributed FPGA-as-a-service architecture towards billion-scale graph-based approximate nearest neighbor search. In MICRO. 283-296.","journal-title":"MICRO."},{"key":"e_1_2_1_74_1","first-page":"1","article-title":"Litehst: A tree embedding based method for similarity search","volume":"1","author":"Zeng Yuxiang","year":"2023","unstructured":"Yuxiang Zeng, Yongxin Tong, and Lei Chen. 2023. Litehst: A tree embedding based method for similarity search. SIGMOD 1, 1 (2023), 1-26.","journal-title":"SIGMOD"},{"key":"e_1_2_1_75_1","first-page":"2601","article-title":"Continuously adaptive similarity search","author":"Zhang Huayi","year":"2020","unstructured":"Huayi Zhang, Lei Cao, Yizhou Yan, Samuel Madden, and Elke A Rundensteiner. 2020. Continuously adaptive similarity search. In SIGMOD. 2601-2616.","journal-title":"SIGMOD."},{"key":"e_1_2_1_76_1","first-page":"23","article-title":"Fast Vector Query Processing for Large Datasets Beyond GPU Memory with Reordered Pipelining","author":"Zhang Zili","year":"2024","unstructured":"Zili Zhang, Fangyue Liu, Gang Huang, Xuanzhe Liu, and Xin Jin. 2024. Fast Vector Query Processing for Large Datasets Beyond GPU Memory with Reordered Pipelining. In NSDI. 23-40.","journal-title":"NSDI."},{"key":"e_1_2_1_77_1","first-page":"1033","article-title":"Song: Approximate nearest neighbor search on gpu","author":"Zhao Weijie","year":"2020","unstructured":"Weijie Zhao, Shulong Tan, and Ping Li. 2020. Song: Approximate nearest neighbor search on gpu. In ICDE. 1033-1044.","journal-title":"ICDE."},{"key":"e_1_2_1_78_1","doi-asserted-by":"publisher","DOI":"10.14778\/3594512.3594527"},{"key":"e_1_2_1_79_1","first-page":"4582","article-title":"Distributed hybrid cpu and gpu training for graph neural networks on billion-scale heterogeneous graphs","author":"Zheng Da","year":"2022","unstructured":"Da Zheng, Xiang Song, Chengru Yang, Dominique LaSalle, and George Karypis. 2022. Distributed hybrid cpu and gpu training for graph neural networks on billion-scale heterogeneous graphs. In SIGKDD. 4582-4591.","journal-title":"SIGKDD."},{"key":"e_1_2_1_80_1","first-page":"359","article-title":"Sparse tensor core: Algorithm and hardware co-design for vector-wise sparse neural networks on modern gpus","author":"Zhu Maohua","year":"2019","unstructured":"Maohua Zhu, Tao Zhang, Zhenyu Gu, and Yuan Xie. 2019. Sparse tensor core: Algorithm and hardware co-design for vector-wise sparse neural networks on modern gpus. In MICRO. 359-371.","journal-title":"MICRO."},{"key":"e_1_2_1_81_1","first-page":"1","article-title":"GTS: GPU-based tree index for fast similarity search","volume":"2","author":"Zhu Yifan","year":"2024","unstructured":"Yifan Zhu, Ruiyao Ma, Baihua Zheng, Xiangyu Ke, Lu Chen, and Yunjun Gao. 2024. GTS: GPU-based tree index for fast similarity search. SIGMOD 2, 3 (2024), 1-27.","journal-title":"SIGMOD"}],"container-title":["Proceedings of the ACM on Management of Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3769825","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T23:29:12Z","timestamp":1768087752000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3769825"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,4]]},"references-count":81,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,12,4]]}},"alternative-id":["10.1145\/3769825"],"URL":"https:\/\/doi.org\/10.1145\/3769825","relation":{},"ISSN":["2836-6573"],"issn-type":[{"value":"2836-6573","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,4]]}}}