How can I use GPU acceleration in C++?

How can I use GPU acceleration in C++?

You can use libraries like CUDA or OpenCL to leverage GPU acceleration in C++. Would you like a tutorial on one of these?

Yes, can you provide a basic CUDA tutorial?

Sure! First, ensure you have a compatible NVIDIA GPU and the CUDA toolkit installed. Then, you can start with a simple vector addition example.

What does the vector addition example look like?

Here's a simple example:

#include <iostream>
#include <cuda.h>

__global__ void add(int *a, int *b, int *c) {
    int index = threadIdx.x;
    c[index] = a[index] + b[index];
}

int main() {
    const int ARRAY_SIZE = 5;
    int a[ARRAY_SIZE] = {1, 2, 3, 4, 5};
    int b[ARRAY_SIZE] = {10, 20, 30, 40, 50};
    int c[ARRAY_SIZE];
    int *d_a, *d_b, *d_c;

    cudaMalloc(&d_a, ARRAY_SIZE * sizeof(int));
    cudaMalloc(&d_b, ARRAY_SIZE * sizeof(int));
    cudaMalloc(&d_c, ARRAY_SIZE * sizeof(int));

    cudaMemcpy(d_a, a, ARRAY_SIZE * sizeof(int), cudaMemcpyHostToDevice);
    cudaMemcpy(d_b, b, ARRAY_SIZE * sizeof(int), cudaMemcpyHostToDevice);

    add<<<1, ARRAY_SIZE>>>(d_a, d_b, d_c);

    cudaMemcpy(c, d_c, ARRAY_SIZE * sizeof(int), cudaMemcpyDeviceToHost);

    for(int i = 0; i < ARRAY_SIZE; i++) {
        std::cout << c[i] << " ";
    }
    cudaFree(d_a);
    cudaFree(d_b);
    cudaFree(d_c);
    return 0;
}

This code adds two arrays using GPU acceleration.

What are some common errors I might encounter?

Common errors include memory allocation failures, kernel launch failures, and issues with data transfer between host and device. Always check the return status of CUDA API calls for debugging.