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Effective and General Distance Computation for Approximate Nearest Neighbor Search (ICDE 2025)

Prerequisites

C++ require:

  • Eigen
  • Boost
  • OpenMP

python environment:

  • numpy
  • faiss
  • numpy
  • scikit-learn
  • matplotlib
  • scipy
  • tqdm

Data set

Reproduction

  1. use ./data/compute_gt.py compute the learning query groundtruth
  2. set the store_path and dataset in set.sh
  3. run bash run.sh

Hardware Notice

  • We have implemented an experimental environment under different hardware acceleration.
  • Specifically, if you use SIMD-AVX, please set the definition of Cmakelist to
-std=c++17 -Ofast -march=core-avx2 -mavx512f -fpic -fopenmp -ftree-vectorize -fexceptions
  • To disable SIMD, we add an executable target with different settings, please set the definition of Cmakelist to
-std=c++17 -O3

and comment out the corresponding executable target such as "search_ivf_512" or "search_hnsw_512".

Baseline Notice

Notice

  1. The code is forked from https://github.com/gaoj0017/ADSampling we add multiprocess for fast index

Reference

Reference to cite when you use this paper or code in a research paper:

@article{Yang2024EffectiveAG,
  title={Effective and General Distance Computation for Approximate Nearest Neighbor Search},
  author={Mingyu Yang and Wentao Li and Jiabao Jin and Xiaoyao Zhong and Xiangyu Wang and Zhitao Shen and Wei Jia and Wei Wang},
  year={2024},
  url={https://arxiv.org/abs/2404.16322}
}

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