https://github.com/bjornmelin/cuda-core-projects
🎯 Essential CUDA programming patterns and optimizations. Showcasing parallel computing expertise through matrix operations, memory management, and advanced kernel implementations. 💻
https://github.com/bjornmelin/cuda-core-projects
cpp cuda cuda-kernels gpu-computing high-performance-computing nvidia optimization parallel-computing
Last synced: 3 months ago
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🎯 Essential CUDA programming patterns and optimizations. Showcasing parallel computing expertise through matrix operations, memory management, and advanced kernel implementations. 💻
- Host: GitHub
- URL: https://github.com/bjornmelin/cuda-core-projects
- Owner: BjornMelin
- License: mit
- Created: 2025-01-24T02:29:03.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-01-24T03:13:14.000Z (9 months ago)
- Last Synced: 2025-03-18T08:49:47.729Z (7 months ago)
- Topics: cpp, cuda, cuda-kernels, gpu-computing, high-performance-computing, nvidia, optimization, parallel-computing
- Homepage:
- Size: 5.86 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# CUDA Core Projects 🎯
[](https://isocpp.org/)
[](https://developer.nvidia.com/cuda-toolkit)
[](https://www.python.org/downloads/)
[](LICENSE)
[](CONTRIBUTING.md)
> Essential CUDA programming patterns and optimizations. Showcasing parallel computing expertise through matrix operations, memory management, and advanced kernel implementations.
[Features](#features) • [Installation](#installation) • [Quick Start](#quick-start) • [Documentation](#documentation) • [Contributing](#contributing)
## 📑 Table of Contents
- [Features](#features)
- [Project Structure](#project-structure)
- [Prerequisites](#prerequisites)
- [Installation](#installation)
- [Quick Start](#quick-start)
- [Documentation](#documentation)
- [Kernels](#kernels)
- [Memory Optimization](#memory-optimization)
- [Benchmarks](#benchmarks)
- [Contributing](#contributing)
- [Versioning](#versioning)
- [Authors](#authors)
- [Citation](#citation)
- [License](#license)
- [Acknowledgments](#acknowledgments)
## ✨ Features
- Advanced CUDA kernel implementations
- Memory optimization techniques
- Thread synchronization patterns
- Warp-level primitives
- Performance profiling tools
## 📁 Project Structure
```mermaid
graph TD
A[cuda-core-projects] --> B[kernels]
A --> C[include]
A --> D[examples]
A --> E[benchmarks]
B --> F[basic]
B --> G[advanced]
B --> H[warp-level]
C --> I[utils]
C --> J[memory]
E --> K[profiling]
E --> L[comparison]
```
Click to expand full directory structure
```plaintext
cuda-core-projects/
├── kernels/ # CUDA kernel implementations
│ ├── basic/ # Fundamental operations
│ ├── advanced/ # Complex algorithms
│ └── warp-level/ # Warp primitives
├── include/ # Header files
│ ├── utils/ # Utility functions
│ └── memory/ # Memory management
├── examples/ # Example applications
├── benchmarks/ # Performance tests
├── python/ # Python bindings
├── tests/ # Unit tests
└── README.md # Documentation
```
## 🔧 Prerequisites
- CUDA Toolkit 11.8+
- NVIDIA GPU (Compute Capability 6.0+)
- C++17 compatible compiler
- CMake 3.15+
- Python 3.8+ (for Python bindings)
## 📦 Installation
```bash
# Clone repository
git clone https://github.com/BjornMelin/cuda-core-projects.git
cd cuda-core-projects
# Create build directory
mkdir build && cd build
# Configure and build
cmake ..
make -j8
# Install Python bindings (optional)
cd ../python
pip install -e .
```
## 🚀 Quick Start
### C++ Usage
```cpp
#include
// Initialize matrices
float* A_d = allocateDeviceMemory(size);
float* B_d = allocateDeviceMemory(size);
// Launch optimized matrix multiplication
matrixMultiply<<>>(A_d, B_d, C_d, M, N, K);
```
### Python Usage
```python
from cuda_core import matrix_ops
# Use CUDA-accelerated operations
result = matrix_ops.multiply(A, B)
```
## 📚 Documentation
### Kernels
| Kernel | Description | Performance | Optimization Level |
|--------|-------------|-------------|-------------------|
| MatMul | Matrix multiplication | 95% peak FLOPS | Advanced |
| Reduction | Parallel reduction | 90% bandwidth | Optimized |
| Convolution | 2D convolution | 85% efficiency | Intermediate |
### Memory Optimization
- Coalesced memory access
- Shared memory utilization
- Bank conflict avoidance
- Memory transfer optimization
### Benchmarks
Performance across different operations:
| Operation | Size | GPU | Time (ms) | Bandwidth (GB/s) |
|-----------|------|-----|-----------|------------------|
| Matrix Mult | 4096x4096 | A100 | 1.2 | 1250 |
| Reduction | 1M elements | V100 | 0.3 | 850 |
| Convolution | 1024x1024 | 3090 | 0.8 | 760 |
## 🤝 Contributing
- [Contributing Guidelines](CONTRIBUTING.md)
- [Code of Conduct](CODE_OF_CONDUCT.md)
- [Development Guide](DEVELOPMENT.md)
## 📌 Versioning
We use [SemVer](http://semver.org/) for versioning. For available versions, see the [tags on this repository](https://github.com/BjornMelin/cuda-core-projects/tags).
## ✍️ Authors
**Bjorn Melin**
- GitHub: [@BjornMelin](https://github.com/BjornMelin)
- LinkedIn: [Bjorn Melin](https://linkedin.com/in/bjorn-melin)
## 📝 Citation
```bibtex
@misc{melin2024cudacoreprojects,
author = {Melin, Bjorn},
title = {CUDA Core Projects: Advanced GPU Computing Implementations},
year = {2024},
publisher = {GitHub},
url = {https://github.com/BjornMelin/cuda-core-projects}
}
```
## 📄 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## 🙏 Acknowledgments
- NVIDIA for CUDA toolkit and documentation
- GPU Computing community for optimization insights
- Open source contributors for testing and feedback
---
Made with 🎯 and ❤️ by Bjorn Melin