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- W4299585995 abstract "Similarity search finds application in specialized 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, prior approaches are bottlenecked by algorithms that expose less parallelism, such as k-min selection, or make poor use of the memory hierarchy. We propose a design for k-selection that operates at up to 55% of theoretical peak performance, enabling a nearest neighbor implementation that is 8.5x faster than prior GPU state of the art. We apply it in different similarity search scenarios, by proposing optimized design for 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 enables the construction of a high accuracy k-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." @default.
- W4299585995 created "2022-10-02" @default.
- W4299585995 creator A5033867133 @default.
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- W4299585995 date "2017-02-28" @default.
- W4299585995 modified "2023-10-01" @default.
- W4299585995 title "Billion-scale similarity search with GPUs" @default.
- W4299585995 doi "https://doi.org/10.48550/arxiv.1702.08734" @default.
- W4299585995 hasPublicationYear "2017" @default.
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