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Billion-Scale Similarity Search with GPUs
Open accessAbstract
Similarity search finds application in database systems handling complex data such as images or videos, which are typically represented by high-dimensional features and require specific indexing structures. This paper tackles the problem of better utilizing GPUs for this task. While GPUs excel at data parallel tasks such as distance computation, prior approaches in this domain are bottlenecked by algorithms that expose less parallelism, such ask-min selection, or make poor use of the memory hierarchy. We propose a novel design fork-selection. We apply it in different similarity search scenarios, by optimizing brute-force, approximate and compressed-domain search based on product quantization. In all these setups, we outperform the state of the art by large margins. Our implementation operates at up to 55 percent of theoretical peak performance, enabling a nearest neighbor implementation that is 8.5 × faster than prior GPU state of the art. It enables the construction of a high accuracyk-NN graph on 95 million images from the Yfcc100M dataset in 35 minutes, and of a graph connecting 1 billion vectors in less than 12 hours on 4 Maxwell Titan X GPUs. We have open-sourced our approach for the sake of comparison and reproducibility.
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“They scale gracefully to millions of records but lose structured relationships: you can ask 'what's most similar?' but not 'what caused what?'”
From Memory for Autonomous LLM Agents:Mechanisms, Evaluation, and Emerging Frontiers (2026) · cites this paper as Johnson et al., 2021
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“Dense passage retrieval [Karpukhin et al., 2020] via learned encoders, typically backed by FAISS-style approximate nearest-neighbor search [Johnson et al., 2021], remains the default implementation, often augmented with sparse BM25 and metadata filters (timestamps, tool types, task tags).”
From Memory for Autonomous LLM Agents:Mechanisms, Evaluation, and Emerging Frontiers (2026) · cites this paper as Johnson et al., 2021
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Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Distributed Representations of Words and Phrases and their Compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533
Han, S., Mao, H., & Dally, W. J. (2015) · arXiv · doi:10.48550/arxiv.1510.00149 · Cited by 4,662
Bag of Tricks for Efficient Text Classification
Joulin, A., Grave, E., Bojanowski, P., & Mikolov, T. (2017) · Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers · doi:10.18653/v1/e17-2068 · Cited by 4,545
CNN Features Off-the-Shelf: An Astounding Baseline for Recognition
Razavian, A. S., Azizpour, H., Sullivan, J., & Carlsson, S. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2014.131 · Cited by 4,451
Product Quantization for Nearest Neighbor Search
Jégou, H., Douze, M., & Schmid, C. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.57 · Cited by 2,956 · cited 5×
“ing a minimal accuracy loss results in orders of magnitude of compression [21]. The most popular vector compression methods can be classied into either binary codes [18, 22], or quantization methods [25, 37]. Both have the desirable property that searching neighbors does not require reconstructing the vectors. Our pap…”
BackgroundMethodInfluentialSelf-citationWilliams, S., Waterman, A., & Patterson, D. A. (2009) · Communications of the ACM · doi:10.1145/1498765.1498785 · Cited by 2,798
Deep Clustering for Unsupervised Learning of Visual Features
Caron, M., Bojanowski, P., Joulin, A., & Douze, M. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01264-9_9 · Cited by 2,674
Self-citationSorting networks and their applications
Batcher, K. E. (1968) · doi:10.1145/1468075.1468121 · Cited by 2,442
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Research works printed but not linked: 3
- leighton, 1992, Introduction to Parallel Algorithms and Architectures Array Trees Hypercubes
- andoni, 2006, Near-optimal hashing algorithms for near neighbor problem in high dimensions, Proc Symp Foundations Comput Sci, 459
- weber, 1998, A quantitative analysis and performance study for similarity-search methods in high-dimensional spaces, Proc Int Conf Very Large Databases, 194
Cited by
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Rives, A., Meier, J., Sercu, T., Goyal, S., Lin, Z., Liu, J., Guo, D., Ott, M., Zitnick, C. L., Ma, J., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2016239118 · Cited by 3,286
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Karpukhin, V., Oguz, B., Min, S., Lewis, P., Wu, L., Edunov, S., Chen, D., & Yih, W.-T. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.550 · Cited by 3,575
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Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al. (2021) · arXiv · doi:10.48550/arxiv.2108.07258 · Cited by 2,676
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Raschka, S., Patterson, J. M., & Nolet, C. (2020) · Information · doi:10.3390/info11040193 · Cited by 928
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Douze, M., Guzhva, A., Deng, C., Johnson, J., Szilvasy, G., Mazaré, P.-E., Lomeli, M., Hosseini, L., & Jégou, H. (2026) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2025.3618474 · Cited by 478
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(2021) · doi:10.1145/3448016.3457550 · Cited by 416
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Sachdev, S., Li, H., Luan, S., Kim, S., Sen, K., & Chandra, S. (2018) · ACM SIGPLAN International Workshop on Machine Learning and Programming Languages (MAPL) · doi:10.1145/3211346.3211353 · Cited by 174
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Romanov, R. A., Tretiakov, E. O., Kastriti, M. E., Zupančič, M., Häring, M., Korchynska, S., Popadin, K. Y., Benevento, M., Rebernik, P., Lallemend, F., et al. (2020) · Nature · doi:10.1038/s41586-020-2266-0 · Cited by 178
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Yue, X., Zheng, Z., Zhang, S., Gao, Y., Darrell, T., Keutzer, K., & Sangiovanni-Vincentelli, A. L. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.01362 · Cited by 189
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Humeau, S., Shuster, K., Lachaux, M.-A., & Weston, J. (2020) · International Conference on Learning Representations · Cited by 177
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“We implement our models based on Faiss ANNS library [21] and RecBole [67, 69].”
MethodSparse-Interest Network for Sequential Recommendation
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Xu, H., Zhang, Y., Zhou, B., Wang, L., Yao, X., Meng, G., & Shen, S. (2022) · IEEE Transactions on Robotics · doi:10.1109/tro.2022.3182503 · Cited by 176
Humeau, S., Shuster, K., Lachaux, M.-A., & Weston, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.01969 · Cited by 291
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Li, Z., Wang, Z., Wang, W., Hung, K., Xie, H., & Wang, F. L. (2025) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2025.100417 · Cited by 151
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Metadata
- Authors
- Johnson, J., Douze, M., & Jegou, H.
- Affiliations
- Meta (United States) (US)
- Venue
- IEEE Transactions on Big Data
- Published
- 2021-07-01
- Volume
- 7
- Issue
- 3
- Pages
- 535-547
- ISSN
- 2332-7790 (ISSN-L)
- 2372-2096
- OpenAlex
- W2593864460
- Cited by
- 4,228
- References
- 65
- Citations by year
- 2026: 973 · 2025: 691 · 2024: 565 · 2023: 677 · 2022: 446 · 2021: 432
- FWCI
- 136.66
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Big Data (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Hardware and Architecture › Parallel Computing and Optimization Techniques
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Graph Theory and Algorithms
- Keywords
- similarity search, nearest neighbor search, GPU acceleration, product quantization, approximate nearest neighbor search, high-dimensional indexing, image retrieval, memory hierarchy, parallel algorithms
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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