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Showcasing parallel computing expertise through matrix operations, memory management, and advanced kernel implementations.\n\n[Features](#features) • [Installation](#installation) • [Quick Start](#quick-start) • [Documentation](#documentation) • [Contributing](#contributing)\n\n## 📑 Table of Contents\n- [Features](#features)\n- [Project Structure](#project-structure)\n- [Prerequisites](#prerequisites)\n- [Installation](#installation)\n- [Quick Start](#quick-start)\n- [Documentation](#documentation)\n  - [Kernels](#kernels)\n  - [Memory Optimization](#memory-optimization)\n  - [Benchmarks](#benchmarks)\n- [Contributing](#contributing)\n- [Versioning](#versioning)\n- [Authors](#authors)\n- [Citation](#citation)\n- [License](#license)\n- [Acknowledgments](#acknowledgments)\n\n## ✨ Features\n- Advanced CUDA kernel implementations\n- Memory optimization techniques\n- Thread synchronization patterns\n- Warp-level primitives\n- Performance profiling tools\n\n## 📁 Project Structure\n\n```mermaid\ngraph TD\n    A[cuda-core-projects] --\u003e B[kernels]\n    A --\u003e C[include]\n    A --\u003e D[examples]\n    A --\u003e E[benchmarks]\n    B --\u003e F[basic]\n    B --\u003e G[advanced]\n    B --\u003e H[warp-level]\n    C --\u003e I[utils]\n    C --\u003e J[memory]\n    E --\u003e K[profiling]\n    E --\u003e L[comparison]\n```\n\n\u003cdetails\u003e\n\u003csummary\u003eClick to expand full directory structure\u003c/summary\u003e\n\n```plaintext\ncuda-core-projects/\n├── kernels/            # CUDA kernel implementations\n│   ├── basic/         # Fundamental operations\n│   ├── advanced/      # Complex algorithms\n│   └── warp-level/    # Warp primitives\n├── include/           # Header files\n│   ├── utils/         # Utility functions\n│   └── memory/        # Memory management\n├── examples/          # Example applications\n├── benchmarks/        # Performance tests\n├── python/           # Python bindings\n├── tests/            # Unit tests\n└── README.md         # Documentation\n```\n\u003c/details\u003e\n\n## 🔧 Prerequisites\n- CUDA Toolkit 11.8+\n- NVIDIA GPU (Compute Capability 6.0+)\n- C++17 compatible compiler\n- CMake 3.15+\n- Python 3.8+ (for Python bindings)\n\n## 📦 Installation\n\n```bash\n# Clone repository\ngit clone https://github.com/BjornMelin/cuda-core-projects.git\ncd cuda-core-projects\n\n# Create build directory\nmkdir build \u0026\u0026 cd build\n\n# Configure and build\ncmake ..\nmake -j8\n\n# Install Python bindings (optional)\ncd ../python\npip install -e .\n```\n\n## 🚀 Quick Start\n\n### C++ Usage\n```cpp\n#include \u003ccuda_core/matrix.cuh\u003e\n\n// Initialize matrices\nfloat* A_d = allocateDeviceMemory\u003cfloat\u003e(size);\nfloat* B_d = allocateDeviceMemory\u003cfloat\u003e(size);\n\n// Launch optimized matrix multiplication\nmatrixMultiply\u003c\u003c\u003cgrid, block\u003e\u003e\u003e(A_d, B_d, C_d, M, N, K);\n```\n\n### Python Usage\n```python\nfrom cuda_core import matrix_ops\n\n# Use CUDA-accelerated operations\nresult = matrix_ops.multiply(A, B)\n```\n\n## 📚 Documentation\n\n### Kernels\n\n| Kernel | Description | Performance | Optimization Level |\n|--------|-------------|-------------|-------------------|\n| MatMul | Matrix multiplication | 95% peak FLOPS | Advanced |\n| Reduction | Parallel reduction | 90% bandwidth | Optimized |\n| Convolution | 2D convolution | 85% efficiency | Intermediate |\n\n### Memory Optimization\n- Coalesced memory access\n- Shared memory utilization\n- Bank conflict avoidance\n- Memory transfer optimization\n\n### Benchmarks\nPerformance across different operations:\n\n| Operation | Size | GPU | Time (ms) | Bandwidth (GB/s) |\n|-----------|------|-----|-----------|------------------|\n| Matrix Mult | 4096x4096 | A100 | 1.2 | 1250 |\n| Reduction | 1M elements | V100 | 0.3 | 850 |\n| Convolution | 1024x1024 | 3090 | 0.8 | 760 |\n\n## 🤝 Contributing\n- [Contributing Guidelines](CONTRIBUTING.md)\n- [Code of Conduct](CODE_OF_CONDUCT.md)\n- [Development Guide](DEVELOPMENT.md)\n\n## 📌 Versioning\nWe use [SemVer](http://semver.org/) for versioning. For available versions, see the [tags on this repository](https://github.com/BjornMelin/cuda-core-projects/tags).\n\n## ✍️ Authors\n**Bjorn Melin**\n- GitHub: [@BjornMelin](https://github.com/BjornMelin)\n- LinkedIn: [Bjorn Melin](https://linkedin.com/in/bjorn-melin)\n\n## 📝 Citation\n```bibtex\n@misc{melin2024cudacoreprojects,\n  author = {Melin, Bjorn},\n  title = {CUDA Core Projects: Advanced GPU Computing Implementations},\n  year = {2024},\n  publisher = {GitHub},\n  url = {https://github.com/BjornMelin/cuda-core-projects}\n}\n```\n\n## 📄 License\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n## 🙏 Acknowledgments\n- NVIDIA for CUDA toolkit and documentation\n- GPU Computing community for optimization insights\n- Open source contributors for testing and feedback\n\n---\nMade with 🎯 and ❤️ by Bjorn Melin\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbjornmelin%2Fcuda-core-projects","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbjornmelin%2Fcuda-core-projects","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbjornmelin%2Fcuda-core-projects/lists"}