CUDA-Vision-Math

🇰🇷 Korean Version (한국어 README)

---

🚀 Overview

CUDA-Vision-Math is a high-performance open-source library developed to accelerate complex computational tasks using NVIDIA CUDA technology.

This repository primarily offers highly optimized, GPU-accelerated algorithms focused on the following areas:

1. Computer Vision (CV) and Image Analysis: Core image processing operations, feature extraction, and manipulation.

2. Numerical Computing and Mathematics: Linear algebra, differential equation solvers, and general mathematical routines.

The goal of this open-source library is to provide developers and researchers with a toolkit that can drastically reduce the execution time of demanding applications by leveraging the parallel processing power of modern GPUs.

---

✨ Features

• CUDA Acceleration: All core routines are implemented using optimized CUDA kernels for maximum parallel performance.

• Bazel Build System: Supports fast, reproducible, and scalable builds using Bazel, perfectly integrating complex CUDA dependencies.

• Broad Coverage: Includes a wide range of algorithms relevant to CV (e.g., filters, transforms) and advanced math (e.g., FFT, specialized matrix operations).

• C++ Interface: Designed with a clean and easy-to-integrate C++ interface.

---

🛠️ Getting Started

Prerequisites

To build and use this library, you will need:

• NVIDIA GPU with CUDA capability (Compute Capability 5.0 or higher recommended).

• CUDA Toolkit (version X.X or later).

• A C++ compiler supporting C++17 or later.

• Bazel (version 5.0 or later recommended).

Installation and Build

This project is built using Bazel. You can build all targets at once with the following commands:

# 1. Clone the repository
git clone https://github.com/YourUsername/cuda-vision-math.git
cd cuda-vision-math

# 2. Build all libraries and executables
bazel build //...

# Optional: Run tests
bazel test //...

---

💡 Usage Example (Placeholder)

Detailed usage guides, including specific Bazel targets for each component (e.g., //vision:convolution_lib), and C++ integration examples will be provided here.

// Example: Accelerated Image Filter (C++ / CUDA)

// #include "cvm/vision/image_filter.h"

// int main() {
//     // Load image data (CPU)
//     // ...

//     // Transfer data to GPU and run CUDA filter
//     // cvm::gpu_image input_gpu = cvm::upload(cpu_data);
//     // cvm::gpu_image output_gpu = cvm::fast_gaussian_blur(input_gpu, 5.0);

//     // Transfer results back to CPU
//     // ...
//     return 0;
// }

---

📜 License

This project is licensed under the Beerware License.

If this library proves valuable for your work or provides a significant performance boost, feel free to buy the author a beer in return! 🍺