Alright, CUDA Dynamic Parallelism the feature that will make your GPU cry tears of joy (or maybe just overheat). This tutorial is for all you Linux lovers out there who want to take their NVIDIA game to the next level.
First: what exactly is CUDA Dynamic Parallelism? It’s a fancy way of saying that your GPU can now spawn and manage its own child kernels, which in turn allows for more complex and efficient parallel processing. This means you can break down larger tasks into smaller sub-tasks and delegate them to different parts of the GPU, resulting in faster overall performance.
Now let’s get our hands dirty. Here are some steps to follow:
1. Make sure your CUDA toolkit is up to date (you should already have this if you’re using a recent version of Linux). You can check by running `nvcc -V` in the terminal.
2. Create a new C++ file and add some basic code for testing purposes:
“`c++
// This script is used for matrix multiplication using CUDA technology.
#include
#include
#include
#include
#include
using namespace std; // use the standard namespace
__global__ void matrixMultiplication(float* A, float* B, float* C) { // define a kernel function for matrix multiplication
int row = blockIdx.y * blockDim.y + threadIdx.y; // calculate the row index of the current thread
int col = blockIdx.x * blockDim.x + threadIdx.x; // calculate the column index of the current thread
if (row < 16 && col < 16) { // limit the matrix size to 16x16 for testing purposes
float sum = 0; // initialize a variable to store the sum of the multiplied elements
for (int k = 0; k < 16; ++k) { // loop through the elements in the row and column
sum += A[row * 16 + k] * B[k * 16 + col]; // multiply and add the elements to the sum
}
C[(row * 16) + col] = sum; // store the sum in the corresponding index of the result matrix
}
}
int main() {
int size = 256; // define the size of the matrices
float* A, *B, *C; // declare pointers for the matrices
cudaMalloc(&A, size * size * sizeof(float)); // allocate memory for matrix A on the GPU
cudaMalloc(&B, size * size * sizeof(float)); // allocate memory for matrix B on the GPU
cudaMalloc(&C, size * size * sizeof(float)); // allocate memory for the result matrix C on the GPU
// initialize matrices with random values (you can use your own data if you prefer)
for (int i = 0; i < size * size; ++i) { // loop through the elements in the matrices
A[i] = rand() / RAND_MAX; // assign a random value to each element in matrix A
B[i] = rand() / RAND_MAX; // assign a random value to each element in matrix B
}
// set up child kernel parameters and launch it with dynamic parallelism
dim3 gridSize(size/16, size/16); // define the grid size for the child kernel
dim3 blockSize(16, 16); // define the block size for the child kernel
matrixMultiplication
// wait for child kernel to finish and free memory
cudaDeviceSynchronize(); // synchronize the CPU with the GPU to ensure all operations are completed
cudaFree(A); // free the memory allocated for matrix A on the GPU
cudaFree(B); // free the memory allocated for matrix B on the GPU
cudaFree(C); // free the memory allocated for the result matrix C on the GPU
return 0; // exit the program
}
“`
3. Compile the code using `nvcc -o matrix_multiplication matrix_multiplication.cu`. Note that you’ll need to replace `matrix_multiplication.cu` with your own file name if it differs.
4. Run the program and watch as your GPU does some serious heavy lifting (you can use `cuda-memcheck` or a profiler like NVIDIA’s NVPROF for more detailed analysis).
5. If you want to take things even further, try modifying the code to perform multiple matrix multiplications in parallel using dynamic parallelism. This involves launching child kernels from within the parent kernel and coordinating their execution. It can be a bit tricky at first, but once you get the hang of it, your GPU will thank you for it.