Efficient exact K-nearest neighbor graph construction for billion-scale datasets using GPUs with tensor cores | Proceedings of the 36th ACM International Conference on Supercomputing (2024)

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Efficient exact K-nearest neighbor graph construction for billion-scale datasets using GPUs with tensor cores | Proceedings of the 36th ACM International Conference on Supercomputing (2)

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  • Zhuoran Ji The University of Hong Kong, Hong Kong, China

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  • Cho-Li Wang The University of Hong Kong, Hong Kong, China

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ICS '22: Proceedings of the 36th ACM International Conference on SupercomputingJune 2022Article No.: 10Pages 1–12https://doi.org/10.1145/3524059.3532368

Published:28 June 2022Publication HistoryEfficient exact K-nearest neighbor graph construction for billion-scale datasets using GPUs with tensor cores | Proceedings of the 36th ACM International Conference on Supercomputing (3)

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ICS '22: Proceedings of the 36th ACM International Conference on Supercomputing

Efficient exact K-nearest neighbor graph construction for billion-scale datasets using GPUs with tensor cores

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Efficient exact K-nearest neighbor graph construction for billion-scale datasets using GPUs with tensor cores | Proceedings of the 36th ACM International Conference on Supercomputing (4)

ABSTRACT

Approximate nearest neighbor search plays a fundamental role in many areas, and the k-nearest neighbor graph (KNNG) becomes a promising solution, especially in high-dimensional space. The advantages of KNNG come at the expense of high construction time, which is in quadratic time complexity in the number of points. Many GPUs have adopted specialized hardware units for matrix multiplication, providing an even higher arithmetic throughput. This paper presents flyKNNG, a GPU KNNG construction algorithm for billion-scale datasets. It deploys the distance matrix calculation to matrix multiplication units and adopts on-the-fly top-k selection to avoid transferring the exa-scale distance matrix to/from device memory. flyKNNG co-designs the two key algorithms to optimize the overall performance: the distance matrix calculation algorithm considers the data communication costs and pruning strategy of top-k selection; the top-k selection algorithm is also specially designed for on-the-fly usage, which impacts the data reuse and instruction-level parallelism of the distance matrix calculation as little as possible. Moreover, our top-k selection algorithm is optimized for the special data layout adopted by most matrix multiplication units. Experiments show that flyKNNG achieves 4.67X speedup compared with CUML/FAISS, one of the state-of-the-art approaches.

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      1. Computing methodologies

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          Efficient exact K-nearest neighbor graph construction for billion-scale datasets using GPUs with tensor cores | Proceedings of the 36th ACM International Conference on Supercomputing (67)

          ICS '22: Proceedings of the 36th ACM International Conference on Supercomputing

          June 2022

          514 pages

          ISBN:9781450392815

          DOI:10.1145/3524059

          • General Chairs:
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            University of Illinois at Urbana-Champaign

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            Virginia Tech

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            National Technical University of Athens

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