I would like to implement the following algorithm :
- Save all 3x3 matrices in 1 dimensional array
- Every 9 elements form a matrix
- Send the array to kernel
- Every thread finds the determinant of 1 matrix
Here is the (incomplete) variant of a code that I tried to write:
Could you give me some advice on how can I correct the code to work according to the given task?
Thanks in advance!
Yoan
#include “cuda_runtime.h”
#include “device_launch_parameters.h”
#include <stdio.h>
//method for calculating the determinant of a matrix (3rd) → calculateMatrixWithCuda
cudaError_t calculateMatrixWithCuda(int *c, const int *a, const int *b, unsigned int size);
//method for sending to kernel
global void calculateDeterminantOfAMatrixKernel(int *c, const int *a, const int *b)
{
int i = threadIdx.x;
int y = threadIdy.y;
//writing the extensive logic here…
}
int main()
{
//setting up value of the array size, each matrix with dimension 3x3 → 45 elements in total
const int arraySize = 9*5;
//on the next lines - the matrices follow - 5 in total
const int matrix[arraySize] = {
//0 1 2 3 4 5 6 7 8
34,23,245,231,345,235,2,8,43,
33,990,48,84,38,384,23,40,4,
67,33,356,8,7,34,43,656,345,
8,12,65,45,567,78,65,67,8,
90,567,34,67,756,767,457,74,66
};
//array for storing the result of the operation
int c[arraySize/9] = { 0 };
// Add vectors in parallel.
cudaError_t cudaStatus = calculateMatrixWithCuda(c, a, b, arraySize);
if (cudaStatus != cudaSuccess) {
fprintf(stderr, "calculateDeterminantWithCuda failed!");
return 1;
}
printf("det_matrices = %d}\n",
c[0], c[1], c[2], c[3], c[4]);
// cudaDeviceReset must be called before exitъing in order for profiling and
// tracing tools such as Nsight and Visual Profiler to show complete traces.
cudaStatus = cudaDeviceReset();
if (cudaStatus != cudaSuccess) {
fprintf(stderr, "cudaDeviceReset failed!");
return 1;
}
return 0;
}
// Helper function for CUDA to add vectors in parallel.
cudaError_t addWithCuda(int *c, const int *a, const int *b, unsigned int size)
{
int *dev_a = 0;
int *dev_b = 0;
int *dev_c = 0;
cudaError_t cudaStatus;
// Choose which GPU to run on, change this on a multi-GPU system.
cudaStatus = cudaSetDevice(0);
if (cudaStatus != cudaSuccess) {
fprintf(stderr, "cudaSetDevice failed! Do you have a CUDA-capable GPU installed?");
goto Error;
}
// Allocate GPU buffers for three vectors (two input, one output) .
cudaStatus = cudaMalloc((void**)&dev_c, size * sizeof(int));
if (cudaStatus != cudaSuccess) {
fprintf(stderr, "cudaMalloc failed!");
goto Error;
}
cudaStatus = cudaMalloc((void**)&dev_a, size * sizeof(int));
if (cudaStatus != cudaSuccess) {
fprintf(stderr, "cudaMalloc failed!");
goto Error;
}
cudaStatus = cudaMalloc((void**)&dev_b, size * sizeof(int));
if (cudaStatus != cudaSuccess) {
fprintf(stderr, "cudaMalloc failed!");
goto Error;
}
// Copy input vectors from host memory to GPU buffers.
cudaStatus = cudaMemcpy(dev_a, a, size * sizeof(int), cudaMemcpyHostToDevice);
if (cudaStatus != cudaSuccess) {
fprintf(stderr, "cudaMemcpy failed!");
goto Error;
}
cudaStatus = cudaMemcpy(dev_b, b, size * sizeof(int), cudaMemcpyHostToDevice);
if (cudaStatus != cudaSuccess) {
fprintf(stderr, "cudaMemcpy failed!");
goto Error;
}
// Launch a kernel on the GPU with one thread for each element.
addKernel<<<1, size>>>(dev_c, dev_a, dev_b);
// Check for any errors launching the kernel
cudaStatus = cudaGetLastError();
if (cudaStatus != cudaSuccess) {
fprintf(stderr, "addKernel launch failed: %s\n", cudaGetErrorString(cudaStatus));
goto Error;
}
// cudaDeviceSynchronize waits for the kernel to finish, and returns
// any errors encountered during the launch.
cudaStatus = cudaDeviceSynchronize();
if (cudaStatus != cudaSuccess) {
fprintf(stderr, "cudaDeviceSynchronize returned error code %d after launching calculateMatrixKernel!\n", cudaStatus);
goto Error;
}
// Copy output vector from GPU buffer to host memory.
cudaStatus = cudaMemcpy(c, dev_c, size * sizeof(int), cudaMemcpyDeviceToHost);
if (cudaStatus != cudaSuccess) {
fprintf(stderr, "cudaMemcpy failed!");
goto Error;
}
//free the resources
Error:
cudaFree(dev_c);
cudaFree(dev_a);
cudaFree(dev_b);
return cudaStatus;
}