[P] TorchPQ: Efficient Nearest Neighbor Search and Clustering on GPUs

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  • TorchPQ

    Approximate nearest neighbor search with product quantization on GPU in pytorch and cuda

    TorchPQ is a python library for approximate nearest neighbor search on GPUs. It has efficient implementations of IVFPQ algorithm as well as some of its variants (e.g IVFPQ+R). The project is written mostly in python using pytorch library, with some custom CUDA kernels to accelerate clustering, searching and indexing.

  • faiss

    A library for efficient similarity search and clustering of dense vectors.

    TorchPQ allows you to search with tens of thousands of queries in millions of vectors within a second. In some settings where high recall rate is prioritized, TorchPQ outperforms the implementation of the same algorithm in faiss. For benchmark results see the bottom part of the README page.

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