https://github.com/Awrsha/Advanced-CUDA-Programming-GPU-Architecture
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.
https://github.com/Awrsha/Advanced-CUDA-Programming-GPU-Architecture
cuda-programming gpu-programming jit kernels matmul mojo-language multiprocessing multithreading torchquantum triton
Last synced: 24 days ago
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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.
- Host: GitHub
- URL: https://github.com/Awrsha/Advanced-CUDA-Programming-GPU-Architecture
- Owner: Awrsha
- Created: 2024-11-11T20:47:14.000Z (11 months ago)
- Default Branch: master
- Last Pushed: 2024-11-13T15:38:57.000Z (11 months ago)
- Last Synced: 2024-11-13T16:18:43.756Z (11 months ago)
- Topics: cuda-programming, gpu-programming, jit, kernels, matmul, mojo-language, multiprocessing, multithreading, torchquantum, triton
- Language: Cuda
- Homepage:
- Size: 25.1 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🚀 Advanced CUDA Programming & GPU Architecture
> *Unlocking the Power of Parallel Computing*
## 🎯 Course Mission
Transform complex GPU programming concepts into practical skills for high-performance computing professionals. Master CUDA programming through hands-on projects and real-world applications.
## 🛠️ Core Technologies
- **CUDA** - NVIDIA's parallel computing platform
- **PyTorch** - Deep learning framework with CUDA support
- **Triton** - Open-source GPU programming language
- **cuBLAS & cuDNN** - GPU-accelerated libraries
## 📚 Curriculum Roadmap
### Phase 1: Foundations
#### 1. Deep Learning Ecosystem Deep Dive
- Modern GPU Architecture Overview
- Memory Hierarchy & Data Flow
- CUDA in the ML Stack
- Hardware Accelerator Landscape (GPU vs TPU vs DPU)
#### 2. Development Environment Setup
- 🐧 Linux Environment Configuration
- 🐋 Docker Containerization
- 🔧 CUDA Toolkit Installation
- 📊 Monitoring & Profiling Tools
#### 3. Programming Language Mastery
- C/C++ Advanced Concepts
- Python High-Performance Computing
- Mojo Language Introduction
- R for GPU Computing
### Phase 2: Core CUDA Concepts
#### 4. GPU Architecture & Computing
- SM Architecture Deep Dive
- Memory Coalescing
- Warp Execution Model
- Shared Memory & L1/L2 Cache
#### 5. CUDA Kernel Development
- Thread Hierarchy
- Memory Management
- Synchronization Primitives
- Error Handling & Debugging
#### 6. Advanced CUDA APIs
- cuBLAS Optimization
- cuDNN for Deep Learning
- Thrust Library
- NCCL for Multi-GPU
### Phase 3: Optimization & Performance
#### 7. Matrix Operations Optimization
- Tiled Matrix Multiplication
- Memory Access Patterns
- Bank Conflicts Resolution
- Warp-Level Primitives
#### 8. Modern GPU Programming
- Triton Programming Model
- Automatic Kernel Tuning
- Memory Access Optimization
- Performance Comparison with CUDA
#### 9. PyTorch CUDA Extensions
- Custom CUDA Kernels
- C++/CUDA Extension Development
- JIT Compilation
- Performance Profiling
### Phase 4: Applied Projects
#### 10. Capstone Project
- MNIST MLP Implementation
- Custom CUDA Kernels
- Performance Optimization
- Multi-GPU Scaling
#### 11. Advanced Topics
- Ray Tracing
- Fluid Simulation
- Cryptographic Applications
- Scientific Computing
## 🎓 Learning Outcomes
By the end of this course, you will be able to:
- Design and implement efficient CUDA kernels
- Optimize GPU memory usage and access patterns
- Develop custom PyTorch extensions
- Profile and debug GPU applications
- Deploy multi-GPU solutions
## 🔍 Prerequisites
### Required:
- Strong Python programming skills
- Basic understanding of C/C++
- Computer architecture fundamentals
### Recommended:
- Linear algebra basics
- Calculus (for backpropagation)
- Basic ML/DL concepts
## 💻 Hardware Requirements
### Minimum:
- NVIDIA GTX 1660 or better
- 16GB RAM
- 50GB free storage
### Recommended:
- NVIDIA RTX 3070 or better
- 32GB RAM
- 100GB SSD storage
## 📚 Learning Resources
### Official Documentation
- [NVIDIA CUDA Documentation](https://docs.nvidia.com/cuda/)
- [PyTorch CUDA Documentation](https://pytorch.org/docs/stable/cuda.html)
- [Triton Documentation](https://triton-lang.org/)
### Community Resources
- 💬 NVIDIA Developer Forums
- 🤝 Stack Overflow CUDA tag
- 🎮 Discord: CUDAMODE community
### Video Learning
#### Fundamentals
- 🎥 [GPU Architecture Deep Dive](https://www.youtube.com/watch?v=h9Z4oGN89MU)
- 🎥 [CUDA Programming Essentials](https://www.youtube.com/watch?v=QQceTDjA4f4)
#### Advanced Topics
- 🎥 [Matrix Multiplication Optimization](https://www.youtube.com/watch?v=DpEgZe2bbU0)
- 🎥 [Multi-GPU Programming](https://www.youtube.com/watch?v=4APkMJdiudU)
## 🌟 Course Philosophy
We believe in:
- Hands-on learning through practical projects
- Understanding fundamentals before optimization
- Building real-world applicable skills
- Community-driven knowledge sharing
## 📈 Industry Applications
- 🤖 Deep Learning & AI
- 🎮 Graphics & Gaming
- 🌊 Scientific Simulation
- 📊 Data Analytics
- 🔐 Cryptography
- 🎬 Media Processing