🚀 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
• PyTorch CUDA Documentation
• Triton Documentation

Community Resources

• 💬 NVIDIA Developer Forums
• 🤝 Stack Overflow CUDA tag
• 🎮 Discord: CUDAMODE community

Video Learning

Fundamentals

• 🎥 GPU Architecture Deep Dive
• 🎥 CUDA Programming Essentials

Advanced Topics

• 🎥 Matrix Multiplication Optimization
• 🎥 Multi-GPU Programming

🌟 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