Block-based Approximate Nearest Neighbor (BBAnn)

BBAnn is an algorithm optimized for SSD storage. It organizes data so that they are aligned with SSD block size.

The source code is mainly located in include and src folders. By running scripts under python directory, it will create docker image, install python library bound with pybind11 and then run the framework.

Prerequisites

• CMake >= 3.10
• gcc >= 6.1
• AIO
• Docker

Get Started

git clone --recurse-submodules https://github.com/zilliztech/BBAnn.git
cd BBAnn/python

# Run knn search
sudo ./run_framework.sh

# Run range search
sudo ./run_range_search.sh

To run a dataset other than random-xs and random-range-xs, you first need to prepare the dataset

cd BBAnn/benchmark
sudo python3 create_dataset.py --dataset [dataset_name]
cd ../python

# Change dataset name
vi run_framework.sh 

sudo ./run_framework.sh

The parameters for datasets are located in python/bbann-algo.yaml.