{"id":24142935,"url":"https://github.com/Awrsha/Advanced-CUDA-Programming-GPU-Architecture","last_synced_at":"2025-09-19T11:31:42.162Z","repository":{"id":262657948,"uuid":"886913801","full_name":"Awrsha/CUDA-GPUs-and-Triton-Adcanced-Review","owner":"Awrsha","description":"This repository provides a comprehensive guide to optimizing GPU kernels for performance, with a focus on NVIDIA GPUs. It covers key tools and techniques such as CUDA, PyTorch, and Triton, aimed at improving computational efficiency for deep learning and scientific computing tasks.","archived":false,"fork":false,"pushed_at":"2024-11-13T15:38:57.000Z","size":26358,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2024-11-13T16:18:43.756Z","etag":null,"topics":["cuda-programming","gpu-programming","jit","kernels","matmul","mojo-language","multiprocessing","multithreading","torchquantum","triton"],"latest_commit_sha":null,"homepage":"","language":"Cuda","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Awrsha.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-11-11T20:47:14.000Z","updated_at":"2024-11-13T15:39:01.000Z","dependencies_parsed_at":"2024-11-13T16:28:54.976Z","dependency_job_id":null,"html_url":"https://github.com/Awrsha/CUDA-GPUs-and-Triton-Adcanced-Review","commit_stats":null,"previous_names":["awrsha/cuda-gpus-and-triton-adcanced-review"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Awrsha%2FCUDA-GPUs-and-Triton-Adcanced-Review","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Awrsha%2FCUDA-GPUs-and-Triton-Adcanced-Review/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Awrsha%2FCUDA-GPUs-and-Triton-Adcanced-Review/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Awrsha%2FCUDA-GPUs-and-Triton-Adcanced-Review/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Awrsha","download_url":"https://codeload.github.com/Awrsha/CUDA-GPUs-and-Triton-Adcanced-Review/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":233566888,"owners_count":18695290,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["cuda-programming","gpu-programming","jit","kernels","matmul","mojo-language","multiprocessing","multithreading","torchquantum","triton"],"created_at":"2025-01-12T05:13:57.006Z","updated_at":"2025-09-19T11:31:42.153Z","avatar_url":"https://github.com/Awrsha.png","language":"Cuda","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 🚀 Advanced CUDA Programming \u0026 GPU Architecture\n\n\u003e *Unlocking the Power of Parallel Computing*\n\n## 🎯 Course Mission\nTransform complex GPU programming concepts into practical skills for high-performance computing professionals. Master CUDA programming through hands-on projects and real-world applications.\n\n## 🛠️ Core Technologies\n- **CUDA** - NVIDIA's parallel computing platform\n- **PyTorch** - Deep learning framework with CUDA support\n- **Triton** - Open-source GPU programming language\n- **cuBLAS \u0026 cuDNN** - GPU-accelerated libraries\n\n## 📚 Curriculum Roadmap\n\n### Phase 1: Foundations\n#### 1. Deep Learning Ecosystem Deep Dive\n- Modern GPU Architecture Overview\n- Memory Hierarchy \u0026 Data Flow\n- CUDA in the ML Stack\n- Hardware Accelerator Landscape (GPU vs TPU vs DPU)\n\n#### 2. Development Environment Setup\n- 🐧 Linux Environment Configuration\n- 🐋 Docker Containerization\n- 🔧 CUDA Toolkit Installation\n- 📊 Monitoring \u0026 Profiling Tools\n\n#### 3. Programming Language Mastery\n- C/C++ Advanced Concepts\n- Python High-Performance Computing\n- Mojo Language Introduction\n- R for GPU Computing\n\n### Phase 2: Core CUDA Concepts\n#### 4. GPU Architecture \u0026 Computing\n- SM Architecture Deep Dive\n- Memory Coalescing\n- Warp Execution Model\n- Shared Memory \u0026 L1/L2 Cache\n\n#### 5. CUDA Kernel Development\n- Thread Hierarchy\n- Memory Management\n- Synchronization Primitives\n- Error Handling \u0026 Debugging\n\n#### 6. Advanced CUDA APIs\n- cuBLAS Optimization\n- cuDNN for Deep Learning\n- Thrust Library\n- NCCL for Multi-GPU\n\n### Phase 3: Optimization \u0026 Performance\n#### 7. Matrix Operations Optimization\n- Tiled Matrix Multiplication\n- Memory Access Patterns\n- Bank Conflicts Resolution\n- Warp-Level Primitives\n\n#### 8. Modern GPU Programming\n- Triton Programming Model\n- Automatic Kernel Tuning\n- Memory Access Optimization\n- Performance Comparison with CUDA\n\n#### 9. PyTorch CUDA Extensions\n- Custom CUDA Kernels\n- C++/CUDA Extension Development\n- JIT Compilation\n- Performance Profiling\n\n### Phase 4: Applied Projects\n#### 10. Capstone Project\n- MNIST MLP Implementation\n- Custom CUDA Kernels\n- Performance Optimization\n- Multi-GPU Scaling\n\n#### 11. Advanced Topics\n- Ray Tracing\n- Fluid Simulation\n- Cryptographic Applications\n- Scientific Computing\n\n## 🎓 Learning Outcomes\nBy the end of this course, you will be able to:\n- Design and implement efficient CUDA kernels\n- Optimize GPU memory usage and access patterns\n- Develop custom PyTorch extensions\n- Profile and debug GPU applications\n- Deploy multi-GPU solutions\n\n## 🔍 Prerequisites\n### Required:\n- Strong Python programming skills\n- Basic understanding of C/C++\n- Computer architecture fundamentals\n\n### Recommended:\n- Linear algebra basics\n- Calculus (for backpropagation)\n- Basic ML/DL concepts\n\n## 💻 Hardware Requirements\n### Minimum:\n- NVIDIA GTX 1660 or better\n- 16GB RAM\n- 50GB free storage\n\n### Recommended:\n- NVIDIA RTX 3070 or better\n- 32GB RAM\n- 100GB SSD storage\n\n## 📚 Learning Resources\n\n### Official Documentation\n- [NVIDIA CUDA Documentation](https://docs.nvidia.com/cuda/)\n- [PyTorch CUDA Documentation](https://pytorch.org/docs/stable/cuda.html)\n- [Triton Documentation](https://triton-lang.org/)\n\n### Community Resources\n- 💬 NVIDIA Developer Forums\n- 🤝 Stack Overflow CUDA tag\n- 🎮 Discord: CUDAMODE community\n\n### Video Learning\n#### Fundamentals\n- 🎥 [GPU Architecture Deep Dive](https://www.youtube.com/watch?v=h9Z4oGN89MU)\n- 🎥 [CUDA Programming Essentials](https://www.youtube.com/watch?v=QQceTDjA4f4)\n\n#### Advanced Topics\n- 🎥 [Matrix Multiplication Optimization](https://www.youtube.com/watch?v=DpEgZe2bbU0)\n- 🎥 [Multi-GPU Programming](https://www.youtube.com/watch?v=4APkMJdiudU)\n\n## 🌟 Course Philosophy\nWe believe in:\n- Hands-on learning through practical projects\n- Understanding fundamentals before optimization\n- Building real-world applicable skills\n- Community-driven knowledge sharing\n\n## 📈 Industry Applications\n- 🤖 Deep Learning \u0026 AI\n- 🎮 Graphics \u0026 Gaming\n- 🌊 Scientific Simulation\n- 📊 Data Analytics\n- 🔐 Cryptography\n- 🎬 Media Processing\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FAwrsha%2FAdvanced-CUDA-Programming-GPU-Architecture","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FAwrsha%2FAdvanced-CUDA-Programming-GPU-Architecture","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FAwrsha%2FAdvanced-CUDA-Programming-GPU-Architecture/lists"}