Vector Search on CPUs, GPUs, and On-Chip Accelerators

We study the performance of vector search algorithms such as IVF, HNSW, and Flat on different hardware architectures such as Sapphire Rapids CPU, A100 GPUs, and Intel AMX accelerators.

Cloning the Repository

git clone --recursive https://github.com/JayjeetAtGithub/vector-search-gpu

Downloading Dataset

We use the Yandex Text-to-Image dataset from here. It has float32 elements in vectors of 200 dimensions and built using IP as the distance metric.

mkdir -p /workspace/dataset/t2i
cd /workspace/dataset/t2i
axel -o dataset.bin https://storage.yandexcloud.net/yandex-research/ann-datasets/T2I/query.learn.50M.fbin
axel -o query.bin https://storage.yandexcloud.net/yandex-research/ann-datasets/T2I/query.public.100K.fbin

Downloading Indexes

cd src/
aws s3 cp --recursive s3://cpu-faiss-indexes .

Installing FAISS on CPU & GPU

# for Sapphire Rapids CPU
./install_faiss_cpu_spr.sh

# for other CPU
./install_faiss_cpu.sh

# for NVIDIA GPU
./install_faiss_gpu.sh

Machine Setup

Updating cmake

wget https://github.com/Kitware/CMake/releases/download/v3.30.5/cmake-3.30.5-linux-x86_64.sh
chmod +x cmake-3.30.5-linux-x86_64.sh
./cmake-3.30.5-linux-x86_64.sh
cd cmake-3.30.5-linux-x86_64
sudo cp -r bin/* /usr/local/bin/
sudo cp -r doc/* /usr/local/doc/
sudo cp -r man/* /usr/local/man/
sudo cp -r share/* /usr/local/share/
cmake --version