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ggnn

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GGNN: State of the Art Graph-based GPU Nearest Neighbor Search

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- - build-python-wheels.yaml
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# Installation Guide

1. Get the code

git clone https://github.com/cgtuebingen/ggnn

Downloads the entire project code from GitHub to your computer.

cd ggnn

Moves into the project folder you just downloaded.

2. CMake

Medium Recommended

Prerequisites

Git Needed to download the project code from GitHub.
CMake The tool used to generate build configuration.
C/C++ 컴파일러 Windows needs Visual Studio (Community edition, free), macOS needs Xcode Command Line Tools, Linux needs the gcc/g++ package.

cmake ..

Analyzes the source code and generates build configuration files (must be run inside the build folder).

✅ Check that an executable was created inside the build folder, then run it directly (e.g. ./build/app_name).

Pulled directly from this repo's README.

3. Python

Easy

Prerequisites

Git Needed to download the project code from GitHub.
Python 3 On Windows, be sure to check 'Add Python to PATH' during installation.

python -m pip install .

Installs the Python libraries listed in requirements.txt (or similar).

✅ If it runs without errors and prints output in the terminal, it worked.

Pulled directly from this repo's README.

// repository documentation

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# GGNN: Graph-based GPU Nearest Neighbor Search

GGNN performs nearest-neighbor computations on CUDA-capable GPUs.
It supports billion-scale, high-dimensional datasets
and can execute on multiple GPUs through sharding.
When using just a single GPU, data can be exchanged directly with other code (e.g., torch tensors)
without copying through CPU memory.
GGNN is implemented using C++ and CUDA.
It can also be used from Python (>=3.8) via its [nanobind](https://github.com/wjakob/nanobind) bindings.

GGNN is based on the method proposed in the paper [GGNN: Graph-based GPU Nearest Neighbor Search](#citing-this-project)
by Fabian Groh, Lukas Ruppert, Patrick Wieschollek, and Hendrik P.A. Lensch.
The original/official code corresponding to the published paper can be found in the [release_0.5](https://github.com/cgtuebingen/ggnn/tree/release_0.5) branch.

For more detailed information see our [documentation](https://ggnn.readthedocs.io/en/latest/).

## Installing the Python Module

Simply clone or download the repository and use `pip` to install the GGNN python module:

```bash
git clone https://github.com/cgtuebingen/ggnn.git
cd ggnn
python -m pip install .
```

### Prerequisites

GGNN is implemented in C++20/CUDA.
To compile and install the GGNN library, you need the [CUDA Toolkit](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/) in version 12 or newer
and GCC/Clang C++ compilers version 10 or newer.

To run GGNN, a CUDA-capable GPU is required.

## Compiling the C++/CUDA Code

To build the example programs for running benchmarks,
or to use GGNN with your own C++ or CUDA code, compile it using CMake:

```bash
git clone https://github.com/cgtuebingen/ggnn.git
cd ggnn
mkdir build
cd build
cmake ..
make -j4
```

### Prerequisites

- NVCC 12 or newer (CUDA Toolkit 12 or newer)
- either GCC (>=10) or Clang (>=10)
  (e.g., `g++-10` `libstdc++-10-dev` or `clang-10` `libc++-10-dev` `libc++abi-10-dev` on Ubuntu)
- `cmake` (>= 3.25.2)
- [glog](https://github.com/google/glog)
  (`libgoogle-glog-dev` on Ubuntu)
- [gflags](https://github.com/gflags/gflags)
  (`libgflags-dev` on Ubuntu)

The glog and gflags development libraries will be automatically fetched by CMake, if not installed.

If you want to compile the Python module via CMake, you also have to install

- [nanobind](https://github.com/wjakob/nanobind)
  (`python -m pip install nanobind`)

For more details on installation and troubleshooting advice,
please see the [documentation](https://ggnn.readthedocs.io/en/latest/install.html).

## Example Usage

The GGNN Python module can be used to perform GPU-accelerated approximate nearest-neighbor (ANN) queries using search graph or brute-force queries for determining the ground truth results.

* First, you need to setup a GGNN instance.
* Then, set the base dataset.
  * Datasets can be given as CPU/CUDA torch tensors or as numpy arrays.
* Given the base, you can build a search graph.
* Using the search graph, you can run queries.
* You can also run brute-force queries (no search graph required).
* The brute-force results can be used to evaluate the accuracy of the ANN queries.

```python
#! /usr/bin/python3

import ggnn
import torch

# get detailed logs
ggnn.set_log_level(4)

# create data
base = torch.rand((10_000, 128), dtype=torch.float32, device='cpu')
query = torch.rand((10_000, 128), dtype=torch.float32, device='cpu')

# initialize ggnn
my_ggnn = ggnn.GGNN()
my_ggnn.set_base(base)

# choose a distance measure
measure=ggnn.DistanceMeasure.Euclidean

# build the graph
my_ggnn.build(k_build=24, tau_build=0.5, refinement_iterations=2, measure=measure)

# run query
k_query: int = 10
tau_query: float = 0.64
max_iterations: int = 400

indices, dists = my_ggnn.query(query, k_query, tau_query, max_iterations, measure)

# run brute-force query to get a ground truth and evaluate the results of the query
gt_indices, gt_dists = my_ggnn.bf_query(query, k_gt=k_query, measure=measure)
evaluator = ggnn.Evaluator(base, query, gt_indices, k_query=k_query)
print(evaluator.evaluate_results(indices))

# print the indices of the 10 NN of the first five queries and their squared euclidean distances
print('indices:', indices[:5], '\n squared dists:',  dists[:5], '\n')

```

For more examples in Python and in C++ see the [examples](https://github.com/cgtuebingen/ggnn/tree/release_0.9/examples) folder.
For more information about the parameters, on how to deal with data that is already on a GPU and on how to utilize multiple GPUs,
check out the [documentation](https://ggnn.readthedocs.io/en/latest/usage_python.html).
We also provide scripts that load and process typical [benchmark datasets](https://ggnn.readthedocs.io/en/latest/benchmarking.html).

## Capabilities and Limitations

The GGNN library supports...

- Billion-scale datasets with up to 2^31-1 vectors.
- Data with up to 4096 dimensions.
- Building search graphs with up to 512 edges per node.
- Searching for up to 6000 nearest neighbors.
- Two distance measures: cosine and euclidean (L2) distance.

## Citing this Project

You can use the following BibTeX entry to cite GGNN:

```bibtex
@ARTICLE{groh2022ggnn,
  author={Groh, Fabian and Ruppert, Lukas and Wieschollek, Patrick and Lensch, Hendrik P. A.},
  journal={IEEE Transactions on Big Data},
  title={GGNN: Graph-Based GPU Nearest Neighbor Search},
  year={2023},
  volume={9},
  number={1},
  pages={267-279},
  doi={10.1109/TBDATA.2022.3161156}
}
```

The official article can be found on IEEE, following this DOI: [10.1109/TBDATA.2022.3161156](https://doi.org/10.1109/TBDATA.2022.3161156).
Alternatively, see the [ArXiV preprint](https://arxiv.org/abs/1912.01059).

---
We hope this library makes your life easier and helps you to solve your problem!

Happy programming,
[Lukas Ruppert](https://github.com/LukasRuppert) and [Deborah Kornwolf](https://github.com/XDeboratti)

PS: If you have a question or a problem that occurs, please feel free to open an issue, we will be happy to help you.