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https://github.com/aknvictor/culingam
CULiNGAM accelerates LiNGAM analysis on GPUs.
https://github.com/aknvictor/culingam
causal-discovery cuda lingam
Last synced: 3 months ago
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CULiNGAM accelerates LiNGAM analysis on GPUs.
- Host: GitHub
- URL: https://github.com/aknvictor/culingam
- Owner: aknvictor
- License: mit
- Created: 2024-02-07T16:42:01.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2024-03-08T16:39:02.000Z (11 months ago)
- Last Synced: 2024-10-29T18:11:10.384Z (3 months ago)
- Topics: causal-discovery, cuda, lingam
- Language: Python
- Homepage:
- Size: 47.9 KB
- Stars: 7
- Watchers: 2
- Forks: 2
- Open Issues: 1
-
Metadata Files:
- Readme: readme.md
- License: LICENSE
Awesome Lists containing this project
README
# CULiNGAM - CUDA Accelerated LiNGAM Analysis
## Introduction
CULiNGAM is a high-performance library that accelerates Linear Non-Gaussian Acyclic Model (LiNGAM) analysis on GPUs. It leverages the computing power of NVIDIA GPUs to provide fast and efficient computations for LiNGAM applications, making it ideal for large-scale data analysis in fields such as bioinformatics, economics, and machine learning. CULiNGAM can be more more than **32x faster than CPU implementations**: https://arxiv.org/abs/2403.03772.
## Installation
To install CULiNGAM, you need a system with NVIDIA CUDA installed and a compatible GPU. This library is tested with CUDA 12.2 and designed for GPUs with the architecture `sm_86`, although it may work with other versions and architectures with appropriate adjustments.
### Prerequisites
- NVIDIA GPU with CUDA Compute Capability 6.0 or higher
- CUDA Toolkit 12.2 or compatible version
- Python 3.6 or newer
- Numpy
- A C++17 compatible compiler
### Installing from PyPI
CULiNGAM is available on PyPI and can be easily installed with pip:
```bash
pip install culingam
```
### Installing CULiNGAM manually
1. Clone the repository to your local machine:
```bash
git clone https://github.com/Viktour19/culingam
```
2. Ensure that CUDA_HOME environment variable is set to your CUDA Toolkit installation path. If not, you can set it as follows (example for default CUDA installation path):
```bash
export CUDA_HOME=/usr/local/cuda-12.2
```
3. Optionally, set the GPU_ARCH environment variable to match your GPU architecture if it differs from the default sm_86:
```bash
export GPU_ARCH=sm_xx # Replace xx with your GPU's compute capability
```
4. Install the library using pip:
```bash
pip install .
```
### Usage
After installation, you can use CULiNGAM in your Python projects to accelerate LiNGAM analysis. Here's a simple example to get started:
https://github.com/Viktour19/culingam/blob/main/examples/basic.py
### Support
For bugs, issues, and feature requests, please submit a report to the repository's issue tracker. Contributions are also welcome.
### Author
Victor Akinwande
### License and Acknowledgment
This project is licensed under the MIT License - see the LICENSE file for details.
A good amount of the code is adapted from sequential implementations of the LiNGAM algorithms present in CULiNGAM: https://github.com/cdt15/lingam.