CUDA Tutorial: From Basics to AI-Relevant Examples

Overview

This tutorial demonstrates CUDA programming concepts, progressing from basic operations to AI-relevant matrix computations.

You can download CUDA toolkit from here.

Files

1. main.cu - Basic Element-wise Addition

What it demonstrates:

• Basic CUDA kernel syntax (__global__)
• Simple parallel execution (10 threads)
• Unified memory (__managed__)
• Synchronization (cudaDeviceSynchronize())

Limitations for AI:

• ❌ Too simple - only element-wise operations
• ❌ Very small parallelism (10 threads)
• ❌ No matrix operations (core of neural networks)
• ❌ No memory optimization techniques

Use case: Learning CUDA fundamentals

2. matrix_multiply.cu - Matrix Multiplication (AI-Relevant)

What it demonstrates:

• ✅ Matrix operations - the foundation of neural networks
• ✅ 2D thread indexing - blockIdx, threadIdx for 2D grids
• ✅ Shared memory - tile-based computation for performance
• ✅ Large-scale parallelism - 1024+ threads working together
• ✅ Synchronization barriers - __syncthreads() for coordination

Why this matters for AI:

• Neural networks are essentially chains of matrix multiplications
• Fully connected layers: output = input × weights + bias
• Convolutional layers: specialized matrix operations
• Transformers: attention mechanisms use matrix multiplications

Key Differences

Feature	Basic Example	AI Example
Operation	Element-wise add	Matrix multiply
Threads	10	1024+
Memory	Global only	Shared + Global
Complexity	O(n)	O(n³)
AI Relevance	Low	High

How Neural Networks Use This

1. Forward Pass: Matrix multiplication between input and weights
2. Backward Pass: Gradient computation via matrix operations
3. Batch Processing: Multiple samples processed in parallel
4. Optimization: Shared memory reduces memory bandwidth

Compilation

# Basic example
nvcc main.cu -o main
./main

# AI-relevant example
nvcc matrix_multiply.cu -o matrix_multiply
./matrix_multiply

Next Steps for AI/ML

To truly understand GPU computing for AI, consider:

1. cuBLAS - Optimized BLAS library for matrix ops
2. cuDNN - Deep neural network primitives
3. Tensor Cores - Specialized hardware for mixed-precision
4. Memory coalescing - Optimizing memory access patterns
5. Multi-GPU - Distributed training across devices