📖 References

Official Documentation

• NVIDIA CUDA Documentation
• CUDA C++ Programming Guide
• CUDA Best Practices Guide

Learning Resources

• GPU Computing Gems
• NVIDIA Developer Blog
• Udacity's Intro to Parallel Programming

Books

• Professional CUDA C Programming - John Cheng, Max Grossman, Ty McKercher
• CUDA by Example - Jason Sanders, Edward Kandrot
• Programming Massively Parallel Processors - David Kirk, Wen-mei Hwu

🤝 Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/NewOptimization)
3. Commit your changes (git commit -m 'Add new optimization technique')
4. Push to the branch (git push origin feature/NewOptimization)
5. Open a Pull Request with a clear description

Guidelines

• Follow CUDA coding best practices
• Include benchmarking results for optimizations
• Add comments explaining the technique
• Verify correctness before submitting

📝 License

This project is licensed under the MIT License. See the LICENSE file for details.

📧 Contact & Support

• GitHub: @bnvai
• Issues: GitHub Issues
• Discussions: GitHub Discussions

🎓 Learning Path

Beginner:

1. Start with 01_vec_add.py - Understand basic CUDA workflow
2. Study 02_softmax.cu - Learn kernel structure and memory management
3. Explore atomicAdd.cu - Understand thread synchronization

Intermediate:

1. Analyze naive_matmul.cu - Basic matrix operations
2. Study tiled_matmul.cu - Shared memory optimization
3. Benchmark unrolling_example.cu - Understand loop optimization

Advanced:

1. Profile with NVTX in nvtx_matmul.cu
2. Implement stream_advanced.cu - Asynchronous execution
3. Create custom kernels for your use cases

---

Made with ❤️ for GPU Computing Enthusiasts

Happy CUDA Learning! 🚀🎓