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While GPUs excel at\ndata-parallel tasks, prior approaches are bottlenecked by algorithms that\nexpose less parallelism, such as k-min selection, or make poor use of the\nmemory hierarchy.\n  We propose a design for k-selection that operates at up to 55% of theoretical\npeak performance, enabling a nearest neighbor implementation that is 8.5x\nfaster than prior GPU state of the art. We apply it in different similarity\nsearch scenarios, by proposing optimized design for brute-force, approximate\nand compressed-domain search based on product quantization. In all these\nsetups, we outperform the state of the art by large margins. Our implementation\nenables the construction of a high accuracy k-NN graph on 95 million images\nfrom the Yfcc100M dataset in 35 minutes, and of a graph connecting 1 billion\nvectors in less than 12 hours on 4 Maxwell Titan X GPUs. 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