GPU and CUDA Programming

Overview

Graphics Processing Units (GPUs) have become the backbone of modern machine learning systems. Understanding GPU architecture and CUDA programming is essential for anyone working in the MLSys field.

GPU Architecture

Modern GPUs consist of thousands of cores designed for parallel processing. Unlike CPUs which are optimized for sequential tasks, GPUs excel at performing the same operation on large amounts of data simultaneously.

Key Concepts

• Streaming Multiprocessors (SMs): The basic building blocks of NVIDIA GPUs
• CUDA Cores: Individual processing units within each SM
• Memory Hierarchy: Global memory, shared memory, registers, and caches

CUDA Programming Basics

CUDA (Compute Unified Device Architecture) is NVIDIA’s parallel computing platform that allows developers to harness GPU power.

Hello World in CUDA

#include <stdio.h>

__global__ void hello_cuda() {
    printf("Hello from GPU thread %d!\n", threadIdx.x);
}

int main() {
    hello_cuda<<<1, 10>>>();
    cudaDeviceSynchronize();
    return 0;
}