Metadata-Version: 2.1
Name: daisy-exact-search
Version: 1.0.1
Summary: DaiSy: A Library for Scalable Data Series Similarity Search
Home-page: https://github.com/MChatzakis/daisy
Author: Franscesca Del Gaudio, Manos Chatzakis, Gayathiri Ravendirane, Themis Palpanas
Author-email: 
Project-URL: Homepage, https://github.com/MChatzakis/DaiSy
Project-URL: Repository, https://github.com/MChatzakis/DaiSy
Project-URL: Issues, https://github.com/MChatzakis/DaiSy/issues
Keywords: similarity-search,data-series
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Classifier: Programming Language :: C++
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=2.2.6
Requires-Dist: pybind11>=3.0.0
Provides-Extra: dev
Requires-Dist: build>=1.0.0; extra == "dev"
Requires-Dist: cmake>=3.15; extra == "dev"
Requires-Dist: pip>=24.0.0; extra == "dev"
Requires-Dist: pytest>=7.0.0; extra == "dev"
Requires-Dist: setuptools>=80.9.0; extra == "dev"
Requires-Dist: twine>=4.0.0; extra == "dev"
Requires-Dist: wheel>=0.45.1; extra == "dev"
Provides-Extra: mpi
Requires-Dist: mpi4py>=4.0.3; extra == "mpi"

# DaiSy: A Library for Scalable Data Series Similarity Search
DaiSy (DAta series sImilarity sSearch librarY) is a unified library for exact data series similarity search that integrates multiple state-of-the-art algorithms within a single, coherent framework, developed at LIPADE, Université Paris Cité.
It supports a wide range approaches tailored for different execution environments, including disk-based, in-memory, GPU-accelerated, and distributed scalable similarity search. 
DaiSy is implemented in C++, while it also offers a convenient Python interface for ease of use and integration with data science workflows.

**Important Note**: The current version of DaiSy is experimental. The library is still under active development, with special focus on improving and resolving issues related to installation and building.
We welcome early suggestions and recommendations.


When using DaiSy, please consider citing the following paper:

```latex
Coming Soon!
```



## Supported State-of-the-Art algorithms
We currently support several algorithms for exact similarity search, each optimized for specific use cases and environments. 
The following table summarizes the key features of each algorithm:

| Algorithm | Description |
|-----------|-------------|
| **Bruteforce** | Naive parallel similarity search implementation |
| **Lower Bound Bruteforce** | Optimized bruteforce with lower bounding for the distance calculations |
| **[MESSI](https://helios2.mi.parisdescartes.fr/~themisp/messi/)** | In-memory parallel similarity search |
| **[PARIS](https://helios2.mi.parisdescartes.fr/~themisp/paris/)** | Disk-based parallel similarity search |
| **[SING](https://helios2.mi.parisdescartes.fr/~themisp/sing/)** | GPU-accelerated in-memory parallel similarity search |
| **[Odyssey](https://helios2.mi.parisdescartes.fr/~themisp/odyssey/)** | Distributed and parallel in-memory similarity search |


## Quickstart

### Dependencies
- **Operating System**: Linux, macOS, or Windows
- **C++ Compiler**: C++14 or higher (GCC 6+, Clang 3.4+, MSVC 2015+)
- **CMake**: Version 3.15 or higher

Optionally,

- **Python**: 3.10-3.12
- **MPI**: Required for Odyssey distributed computing algorithm
- **CUDA**: Required for SING GPU acceleration algorithm


### Installation
To download DaiSy, use:
```bash
git clone https://github.com/MChatzakis/daisy.git

cd daisy
git submodule update --init --recursive
```

Based on the available hardware, you can specify the below arguments to enable/disable features.
| Flag | Description | Default | Dependencies |
|------|-------------|---------|--------------|
| `BUILD_PYTHON` | Enable Python bindings | `ON` | Python 3.10+ |
| `BUILD_BENCHMARK` | Build benchmarking tools | `ON` | GoogleBenchmark |
| `BUILD_TESTS` | Build test suite | `ON` | GoogleTest |
| `BUILD_DEMO` | Build demonstration applications | `ON` | Core library |
| `ODYSSEY_MPI` | Enable MPI for distributed computing | `ON` | OpenMPI/MPICH |
| `SING_CUDA` | Enable CUDA for GPU acceleration | `ON` | CUDA Toolkit |
| `DEBUG_MSG` | Enable debug output | `OFF` | None |

To compile:
```bash
mkdir build && cd build

cmake ..
make
```

### DaiSy with Python
```bash
pip install daisy-exact-search
```

<!--
```bash
python3.12 -m venv DaiSy_env

source DaiSy_env/bin/activate 
pip install -r requirements_DaiSy.txt
```
-->


### Example Usage
We provide several usage examples in both C++ and Python under [`demos/`](demos/), demonstrating how to utilize the library for various similarity search tasks.

<!--

#### Build con CUDA (per testare la demo Sing)
Richiede **CUDA Toolkit** installato (`nvcc --version` e `nvidia-smi` funzionanti). La demo `demo_Sing_L2Square` viene compilata solo se CUDA è disponibile.

```bash
mkdir -p build && cd build
# Se non usi MPI (consigliato per test locali):
cmake .. -DODYSSEY_MPI=OFF -DSING_CUDA=ON -DBUILD_DEMO=ON
# Oppure con MPI:
# cmake .. -DSING_CUDA=ON -DBUILD_DEMO=ON

cmake --build . -j
./demos/demo_Sing_L2Square
```

- **Architettura GPU**: di default è `75` (Turing). Se hai un’GPU diversa imposta ad es. `-DCMAKE_CUDA_ARCHITECTURES=86` (Ampere) o `89` (Ada). Controlla [CUDA arch list](https://docs.nvidia.com/cuda/cuda-compiler-driver-nvcc/index.html#gpu-feature-list).
- Se la configurazione segnala "CUDA toolkit not found", verifica `PATH` e `LD_LIBRARY_PATH` (vedi `docs/cuda-installation.md`).
`



## Running the test suite
```bash
cd build

ctest --output-on-failure --verbose
```

## To run a performance analysis
Execute performance benchmarks to compare algorithm performance:

```bash
cd build

# Core algorithms
./benchmark/bm_bruteforce_L2Square
./benchmark/bm_LbBruteforce_L2Square
./benchmark/bm_Messi_L2Square

# Advanced algorithms (if available)
./benchmark/bm_Odyssey_L2Square    # MPI required
./benchmark/bm_Sing_L2Square       # CUDA required
```
-->

## About
DaiSy is developed by the [diNo research group at LIPADE, Université Paris Cité](https://dino.mi.parisdescartes.fr/). 
It is provided with no warranty, and we encourage contributions from the community to enhance its capabilities and performance. For questions, issues, or contributions, please open an issue or submit a pull request on GitHub.
DaiSy licensed under the [MIT License](LICENSE).














