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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Vector-indexed stores scale to millions of records but lose structured relationships; they support similarity queries but not causal queries.
“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
The Introduction (page unavailable) describes similarity search on billion-scale vector databases and says that, for the applications discussed, numerical similarity is more suitable than searching via structured relations. It does not say that vector indexes lose structured relationships or establish that they cannot answer causal queries. (LLM, conf=0.86)
Experimental — accuracy not yet measuredDense passage retrieval via learned encoders, typically backed by FAISS-style approximate nearest-neighbor search, remains the default implementation, often augmented with sparse BM25 and metadata filters.
“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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Mapping the space of chemical reactions using attention-based neural networks
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Retrieval-Augmented Generation for AI-Generated Content: A Survey
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Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval
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Self-Supervised MultiModal Versatile NetworksPreprint
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Autoregressive Entity RetrievalPreprint
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Dense Text Retrieval Based on Pretrained Language Models: A Survey
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Solving the spike sorting problem with KilosortPreprint
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Learning Joint Multilingual Sentence Representations with Neural Machine Translation
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Self-citationUnsupervised Corpus Aware Language Model Pre-training for Dense Passage Retrieval
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A Survey on Deep Visual Place Recognition
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PubMedCLIP: How Much Does CLIP Benefit Visual Question Answering in the Medical Domain?
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Internet-Augmented Dialogue Generation
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COIL: Revisit Exact Lexical Match in Information Retrieval with Contextualized Inverted List
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Beyond Goldfish Memory: Long-Term Open-Domain Conversation
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Survey of vector database management systems
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Unsupervised Speech RecognitionPreprint
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Efficient Neighbourhood Consensus Networks via Submanifold Sparse Convolutions
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Learning Vector-Quantized Item Representation for Transferable Sequential Recommenders
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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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- 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 · 2020: 231 · 2019: 125 · 2018: 71 · 2017: 17
- 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 › Computer Vision and Pattern Recognition › 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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