Welcome to your comprehensive CUDA programming journey!

cd ~/cuda-learning
./START

• Lesson 01: First Kernel - Your first GPU program
• Lesson 02: Thread & Blocks - Understanding parallelism
• Lesson 03: Array Operations - Parallel data processing
• Lesson 04: Memory Model - GPU memory hierarchy
• Lesson 05: Vector Addition - Complete application

• Lesson 06: Shared Memory - Fast on-chip memory
• Lesson 07: Parallel Reduction - Tree-based algorithms
• Lesson 08: CUDA Streams - Asynchronous execution
• Lesson 09: Atomic Operations - Thread-safe operations
• Lesson 10: Texture Memory - Specialized caching

• Lesson 11: Warp Primitives - Low-level optimizations
• Lesson 12: Parallel Scan - Prefix sum algorithms
• Lesson 13: Tensor Cores - AI acceleration hardware

• Lesson 14: Dynamic Parallelism - GPU launches kernels
• Lesson 15: CUDA Graphs - Eliminate launch overhead
• Lesson 16: Multi-GPU - Scale beyond single GPU

• Project 01: Image Blur - Real-world image processing
• Project 02: Tokenizer & Embeddings - NLP fundamentals
• Project 03: Attention Mechanism - Transformer building blocks
• Project 04: GPU Hash Table - High-performance data structures
• Project 05: Graph Algorithms - BFS, PageRank, SSSP
• Project 06: Sparse Matrices - Scientific computing
• Project 07: GPU Regex - Text processing at scale
• Project 08: CNN - Deep learning from scratch
• Project 09: Monte Carlo Finance - Financial simulations
• Project 10: Scientific FFT - Signal processing

• Lesson 17: Profiling & Optimization - Systematic tuning
• Lesson 18: CUTLASS & Templates - Reusable kernels
• Lesson 19: Error Handling - Robust GPU code
• Lesson 20: Deployment - Production systems

Basics (1-5) → Memory (6,9) → Tensor Cores (13) → 
Projects (2,3,8) → Multi-GPU (16) → Production (17-20)

Basics (1-5) → Memory (6-10) → Optimization (11-13) →
Projects (5,6,10) → Advanced (14-16) → Production (17-20)

Basics (1-5) → Atomic Ops (9) → Warp Primitives (11) →
Projects (4,7) → CUDA Graphs (15) → Production (18-20)

• 20+ Comprehensive Lessons: From basics to production
• 10 Major Projects: Real-world applications
• 500+ Exercises: Hands-on practice
• 10-1000x Performance: Proven speedups
• Modern GPU Features: Including Tensor Cores, CUDA Graphs
• Production Skills: Deployment, profiling, error handling

# Basic compilation
nvcc -O3 lesson.cu -o lesson

# With debugging
nvcc -g -G lesson.cu -o lesson

# With libraries
nvcc -O3 lesson.cu -lcublas -lcusparse -o lesson

# For Tensor Cores (Volta+)
nvcc -O3 -arch=sm_70 lesson.cu -o lesson

• Vector Operations: 10-100x speedup
• Matrix Operations: 50-500x with optimization
• Deep Learning: Understanding how PyTorch/TensorFlow work
• Text Processing: Millions of strings/second
• Scientific Computing: Real-time simulations

• Progress Tracker - Track your learning journey
• Complete Overview - Full curriculum details
• How to Start - Detailed starting guide
• Welcome Guide - Setup confirmation & overview
• Cheatsheet - CUDA quick reference
• Free Resources - Additional learning materials
• Week 1 Guide - Fundamentals overview
• Week 2 Guide - Memory optimization
• Week 3 Guide - Advanced optimization
• Week 4 Guide - Advanced features
• Week 5 Guide - Projects overview
• Week 6 Guide - Production guide

By completing this curriculum, you'll:

• ✅ Master GPU architecture and programming
• ✅ Build production-ready GPU applications
• ✅ Understand how AI frameworks work internally
• ✅ Join the elite group of GPU programmers

1. Start Simple: Don't skip the basics
2. Measure Everything: Always profile before optimizing
3. Think Parallel: Redesign algorithms for GPU
4. Hardware First: Understand the hardware limits
5. Practice Daily: Consistency is key

Ready to accelerate your code by 10-1000x? Start with Lesson 1! 🚀