🚀 CUDA Programming Guide: From Basics to Advanced

🎯 Introduction

CUDA (Compute Unified Device Architecture) is NVIDIA's parallel computing platform and programming model. This guide will help you understand and implement CUDA kernels efficiently.

Key Resources

💻 Prerequisites

NVIDIA GPU (Compute Capability 3.0+)
CUDA Toolkit installed
Basic C/C++ knowledge
Understanding of parallel computing concepts

🔍 Core Concepts

Thread Hierarchy

Grid
└── Blocks
    └── Threads

🚀 Getting Started

First CUDA Program: Vector Addition

__global__ void vectorAdd(float *a, float *b, float *c, int n) {
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    if (idx < n) {
        c[idx] = a[idx] + b[idx];
    }
}

Compilation & Execution

nvcc -o vector_add vector_add.cu
./vector_add

🧠 Memory Hierarchy

Memory Type	Scope	Lifetime	Speed
Registers	Thread	Thread	Fastest
Shared Memory	Block	Block	Very Fast
Global Memory	Grid	Application	Slow
Constant Memory	Grid	Application	Fast (cached)

🎯 Best Practices

Memory Coalescing
- Align memory accesses
- Use appropriate data types
Occupancy Optimization
- Balance resource usage
- Optimize block sizes
Warp Efficiency
- Minimize divergent branching
- Utilize warp-level primitives

🔬 Advanced Topics

Matrix Operations

__global__ void matrixMul(float *A, float *B, float *C, int N) {
    int row = blockIdx.y * blockDim.y + threadIdx.y;
    int col = blockIdx.x * blockDim.x + threadIdx.x;
    
    if (row < N && col < N) {
        float sum = 0.0f;
        for (int k = 0; k < N; k++) {
            sum += A[row * N + k] * B[k * N + col];
        }
        C[row * N + col] = sum;
    }
}

Performance Monitoring

nvprof ./your_program  # Profile CUDA applications

📈 Optimization Tips

Memory Transfer
- Minimize host-device transfers
- Use pinned memory for better bandwidth
Kernel Configuration
- Choose optimal block sizes
- Consider hardware limitations
Algorithm Design
- Design for parallelism
- Reduce sequential dependencies