Page 41 - 《软件学报》2026年第3期

1004 软件学报 2026 年第 37 卷第 3 期

[129] Karthik V, Khan S, Singh S, Simhadri HV, Vedurada J. BANG: Billion-scale approximate nearest neighbor search using a single GPU.
arXiv:2401.11324, 2024.
[130] Ootomo H, Naruse A, Nolet C, Wang R, Feher T, Wang Y. CAGRA: Highly parallel graph construction and approximate nearest
neighbor search for GPUs. In: Proc. of the 40th IEEE Int’l Conf. on Data Engineering (ICDE). Utrecht: IEEE, 2024. 4236–4247.
[doi: 10.1109/ICDE60146.2024.00323]
[131] Li WC, Feng C, Lian DF, Xie YX, Liu HF, Ge Y, Chen EH. Learning balanced tree indexes for large-scale vector retrieval. In: Proc. of
the 29th ACM SIGKDD Conf. on Knowledge Discovery and Data Mining. Long Beach: ACM, 2023. 1353–1362. [doi: 10.1145/
3580305.3599406]
[132] Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I. Attention is all you need. In: Proc. of the
31st Int’l Conf. on Neural Information Processing Systems. Long Beach: Curran Associates Inc., 2017. 6000–6010.
[133] Wang YF, Ma HD, Wang DZ. LIDER: An efficient high-dimensional learned index for large-scale dense passage retrieval. Proc. of the
VLDB Endowment, 2022, 16(2): 154–166. [doi: 10.14778/3565816.3565819]
[134] Li CL, Zhang MJ, Andersen DG, He YX. Improving approximate nearest neighbor search through learned adaptive early termination.
In: Proc. of the 2020 ACM SIGMOD Int’l Conf. on Management of Data. Portland: ACM, 2020. 2539–2554. [doi: 10.1145/3318464.
3380600]
[135] Zheng BL, Yue ZY, Hu Q, Yi XM, Luan XF, Xie C, Zhou XF, Jensen CS. Learned probing cardinality estimation for high-dimensional
approximate NN search. In: Proc. of the 39th IEEE Int’l Conf. on Data Engineering (ICDE). Anaheim: IEEE, 2023. 3209–3221. [doi: 10.
1109/ICDE55515.2023.00246]
[136] Yue Q, Xu XL, Wang YX, Tao YK, Luo XLY. Routing-guided learned product quantization for graph-based approximate nearest
neighbor search. In: Proc. of the 40th IEEE Int’l Conf. on Data Engineering (ICDE). Utrecht: IEEE, 2024. 4870–4883. [doi: 10.1109/
ICDE60146.2024.00370]
[137] Li MJ, Zhang Y, Sun YF, Wang W, Tsang IW, Lin XM. I/O efficient approximate nearest neighbour search based on learned functions.
In: Proc. of the 36th IEEE Int’l Conf. on Data Engineering (ICDE). Dallas: IEEE, 2020. 289–300. [doi: 10.1109/ICDE48307.2020.
00032]
[138] Tian B, Liu HK, Duan ZH, Liao XF, Jin H, Zhang Y. Scalable billion-point approximate nearest neighbor search using SmartSSDs. In:
Proc. of the 2024 USENIX Annual Technical Conf. Santa Clara: USENIX Association, 2024. 69.
[139] Yang MY, Li WT, Jin JB, Zhong XY, Wang XY, Shen ZT, Jia W, Wang W. Effective and general distance computation for
approximate nearest neighbor search. In: Proc. of the 41st IEEE Int’l Conf. on Data Engineering (ICDE). Hong Kong: IEEE, 2025.
1098–1110. [doi: 10.1109/ICDE65448.2025.00087]
[140] Deng LW, Chen PH, Zeng XM, Wang TF, Zhao Y, Zheng K. Efficient data-aware distance comparison operations for high-dimensional
approximate nearest neighbor search. Proc. of the VLDB Endowment, 2024, 18(3): 812–821. [doi: 10.14778/3712221.3712244]
[141] Chen P, Chang WC, Jiang JY, Yu HF, Dhillon I, Hsieh CJ. FINGER: Fast inference for graph-based approximate nearest neighbor
search. In: Proc. of the 2023 ACM Web Conf. Austin: ACM, 2023. 3225–3235. [doi: 10.1145/3543507.3583318]
[142] Wang MZ, Xu WZ, Yi XM, Wu SL, Peng ZY, Ke XY, Gao YJ, Xu XL, Guo RT, Xie C. Starling: An I/O-efficient disk-resident graph
index framework for high-dimensional vector similarity search on data segment. Proc. of the ACM on Management of Data, 2024, 2(1):
14. [doi: 10.1145/3639269]
[143] Coleman B, Segarra S, Smola A, Shrivastava A. Graph reordering for cache-efficient near neighbor search. In: Proc. of the 36th Int’l
Conf. on Neural Information Processing Systems. New Orleans: Curran Associates Inc., 2022. 2789.
[144] Wang MZ, Wu HT, Ke XY, Gao YJ, Zhu YF, Zhou WC. Accelerating graph indexing for ANNS on modern CPUs. Proc. of the ACM
on Management of Data, 2025, 3(3): 123. [doi: 10.1145/3725260]
[145] Kuffo L, Krippner E, Boncz P. PDX: A data layout for vector similarity search. Proc. of the ACM on Management of Data, 2025, 3(3):
196. [doi: 10.1145/3725333]
[146] Zhong XY, Li HT, Jin JB, Yang MY, Chu DM, Wang XY, Shen ZT, Jia W, Gu G, Xie Y, Lin XM, Shen HT, Song JK, Cheng P.
VSAG: An optimized search framework for graph-based approximate nearest neighbor search. arXiv:2503.17911, 2025.
[147] Yahoojapan/NGT. 2025. https://github.com/yahoojapan/NGT
[148] Compute Express Link. 2025. https://en.wikipedia.org/wiki/Compute_Express_Link
[149] Jang J, Choi H, Bae H, Lee S, Kwon M, Jung M. CXL-ANNS: Software-hardware collaborative memory disaggregation and
computation for billion-scale approximate nearest neighbor search. In: Proc. of the 2023 USENIX Annual Technical Conf. Boston:
USENIX Association, 2023. 585–600.
[150] Jang J, Choi H, Bae H, Lee S, Kwon M, Jung M. Bridging software-hardware for CXL memory disaggregation in billion-scale nearest