gaoj0017 / ADSampling

[SIGMOD 2023] High-Dimensional Approximate Nearest Neighbor Search: with Reliable and Efficient Distance Comparison Operations

[SIGMOD 2023] High-Dimensional Approximate Nearest Neighbor Search: with Reliable and Efficient Distance Comparison Operations

We note that we have included detailed comments of our core algorithms in

• ./src/adsampling.h
• ./src/hnswlib/hnswalg.h
• ./src/ivf/ivf.h

Prerequisites

• Eigen == 3.4.0
  1. Download the Eigen library from https://gitlab.com/libeigen/eigen/-/archive/3.4.0/eigen-3.4.0.tar.gz.
  2. Unzip it and move the Eigen folder to ./src/.

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GIST Reproduction

The tested datasets are available at https://www.cse.cuhk.edu.hk/systems/hash/gqr/datasets.html.

1. Download and preprocess the datasets. Detailed instructions can be found in ./data/README.md.

2. Index the datasets. It could take several hours.

   # Index IVF/IVF+/IVF++
   ./script/index_ivf.sh
   
   # Index HNSW/HNSW+/HNSW++
   ./script/index_hnsw.sh

3. Test the queries of the datasets. The results are generated in ./results/. Detailed configurations can be found in ./script/README.md.

   # Index IVF/IVF+/IVF++
   ./script/search_ivf.sh
   
   # Index HNSW/HNSW+/HNSW++
   ./script/search_hnsw.sh