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🚀 Advanced CUDA Programming & GPU Architecture
Amir M. Parvizi edited this page Nov 19, 2024
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Transform complex GPU programming concepts into practical skills for high-performance computing professionals. Master CUDA programming through hands-on projects and real-world applications.
- CUDA - NVIDIA's parallel computing platform
- PyTorch - Deep learning framework with CUDA support
- Triton - Open-source GPU programming language
- cuBLAS & cuDNN - GPU-accelerated libraries
- Modern GPU Architecture Overview
- Memory Hierarchy & Data Flow
- CUDA in the ML Stack
- Hardware Accelerator Landscape (GPU vs TPU vs DPU)
- 🐧 Linux Environment Configuration
- 🐋 Docker Containerization
- 🔧 CUDA Toolkit Installation
- 📊 Monitoring & Profiling Tools
- C/C++ Advanced Concepts
- Python High-Performance Computing
- Mojo Language Introduction
- R for GPU Computing
- SM Architecture Deep Dive
- Memory Coalescing
- Warp Execution Model
- Shared Memory & L1/L2 Cache
- Thread Hierarchy
- Memory Management
- Synchronization Primitives
- Error Handling & Debugging
- cuBLAS Optimization
- cuDNN for Deep Learning
- Thrust Library
- NCCL for Multi-GPU
- Tiled Matrix Multiplication
- Memory Access Patterns
- Bank Conflicts Resolution
- Warp-Level Primitives
- Triton Programming Model
- Automatic Kernel Tuning
- Memory Access Optimization
- Performance Comparison with CUDA
- Custom CUDA Kernels
- C++/CUDA Extension Development
- JIT Compilation
- Performance Profiling
- MNIST MLP Implementation
- Custom CUDA Kernels
- Performance Optimization
- Multi-GPU Scaling
- Ray Tracing
- Fluid Simulation
- Cryptographic Applications
- Scientific Computing
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
- Strong Python programming skills
- Basic understanding of C/C++
- Computer architecture fundamentals
- Linear algebra basics
- Calculus (for backpropagation)
- Basic ML/DL concepts
- NVIDIA GTX 1660 or better
- 16GB RAM
- 50GB free storage
- NVIDIA RTX 3070 or better
- 32GB RAM
- 100GB SSD storage
- 💬 NVIDIA Developer Forums
- 🤝 Stack Overflow CUDA tag
- 🎮 Discord: CUDAMODE community
We believe in:
- Hands-on learning through practical projects
- Understanding fundamentals before optimization
- Building real-world applicable skills
- Community-driven knowledge sharing
- 🤖 Deep Learning & AI
- 🎮 Graphics & Gaming
- 🌊 Scientific Simulation
- 📊 Data Analytics
- 🔐 Cryptography
- 🎬 Media Processing