Skip to content

Latest commit

 

History

History
150 lines (123 loc) · 4 KB

README.md

File metadata and controls

150 lines (123 loc) · 4 KB
Raw

🚀 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

Community Resources

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

Video Learning

Fundamentals

Advanced Topics

🌟 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