How can I convolution image in CUDA - cuda

Question

I have a question about image convolution in CUDA. When I test it with small maxtrix (16*16) evething is ok. But with larger matrix, the result is always change when I run.
I think problem is 2 for loops into kernel.
__global__ void image_convolution_kernel(float *input, float *out, float *kernelConv,
int img_width, const int img_height,
const int kernel_width, const int kernel_height )
{
int x = threadIdx.x + blockIdx.x * blockDim.x;
int y = threadIdx.y + blockIdx.y * blockDim.y;
float sum = 0;
for ( int j = 0; j < kernel_height; j++ )
{
for ( int i = 0; i < kernel_width; i++ )
{
int dX = x + i - kernel_width / 2;
int dY = y + j - kernel_height / 2;
if ( dX < 0 )
dX = 0;
if ( dX >= img_width )
dX = img_width - 1;
if ( dY < 0 )
dY = 0;
if ( dY >= img_height )
dY = img_height - 1;
const int idMat = j * kernel_width + i;
const int idPixel = dY * img_width + dX;
sum += (float)input[idPixel] * kernelConv[idMat];
}
}
const int idOut = y * img_width + x;
out[idOut] = abs(sum);
}
void image_convolution(float * input,float* output, int img_height, int img_width)
{
int kernel_height = 3;
int kernel_width = 3;
float kernel[] ={ 0,-0.25,0,
-0.25,1,-0.25,
0,-0.25,0
};
float * mask = new float[kernel_height*kernel_width];
for (int i = 0; i < kernel_height*kernel_width; i++)
{
mask[i] = kernel[i];
}
float * d_input, * d_output, * d_kernel;
cudaMalloc(&d_input, img_width*img_height*sizeof(float));
cudaMalloc(&d_output, img_width*img_height*sizeof(float));
cudaMalloc(&d_kernel, kernel_height*kernel_width*sizeof(float));
cudaMemcpy(d_input, input, img_width*img_height*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(d_kernel, mask, kernel_height*kernel_width*sizeof(float), cudaMemcpyHostToDevice);
dim3 blocksize(16,16);
dim3 gridsize;
gridsize.x=(img_width+blocksize.x-1)/blocksize.x;
gridsize.y=(img_height+blocksize.y-1)/blocksize.y;
image_convolution_kernel<<<gridsize,blocksize>>>(d_input,d_output,d_kernel,img_width,img_height,kernel_width,kernel_height);
cudaMemcpy(output, d_output, img_width*img_height*sizeof(float), cudaMemcpyDeviceToHost);
for (int i=0; i < img_width*img_height; i++)
{
printf("%d, ",(int)output[i]);
}
printf("\n\n");
}
Here is my result, I test it with 24*24 image, I run it 2 time, and I also write simple function to compared the output.
And here is result when I compare the output, there are 32 differents,at index 240, 241 ....

Answer 1

You have made a fairly common error in your program. When you create a grid of threads like this:
dim3 blocksize(16,16);
dim3 gridsize;
gridsize.x=(img_width+blocksize.x-1)/blocksize.x;
gridsize.y=(img_height+blocksize.y-1)/blocksize.y;
you are intentionally creating (usually) extra threads in each dimension, so as to fully cover the problem space (i.e. image size). There is nothing wrong with this.
However, it means we will be launching extra threads, which are outside the valid image dimension. We must ensure that these threads do nothing. The usual approach is to add a thread check to the kernel, so that threads outside the valid image dimensions do nothing. Here's a modified kernel and fully worked example showing that change:
$ cat t1219.cu
#include <iostream>
#include <cstdlib>
const int iw = 1025;
const int ih = 1025;
const int rng = 10;
__global__ void image_convolution_kernel(float *input, float *out, float *kernelConv,
int img_width, const int img_height,
const int kernel_width, const int kernel_height )
{
int x = threadIdx.x + blockIdx.x * blockDim.x;
int y = threadIdx.y + blockIdx.y * blockDim.y;
if ((x < img_width) && (y < img_height)){ // thread check
float sum = 0;
for ( int j = 0; j < kernel_height; j++ )
{
for ( int i = 0; i < kernel_width; i++ )
{
int dX = x + i - kernel_width / 2;
int dY = y + j - kernel_height / 2;
if ( dX < 0 )
dX = 0;
if ( dX >= img_width )
dX = img_width - 1;
if ( dY < 0 )
dY = 0;
if ( dY >= img_height )
dY = img_height - 1;
const int idMat = j * kernel_width + i;
const int idPixel = dY * img_width + dX;
sum += (float)input[idPixel] * kernelConv[idMat];
}
}
const int idOut = y * img_width + x;
out[idOut] = abs(sum);
}
}
void image_convolution(float * input,float* output, int img_height, int img_width)
{
int kernel_height = 3;
int kernel_width = 3;
float kernel[] ={ 0,-0.25,0,
-0.25,1,-0.25,
0,-0.25,0
};
float * mask = new float[kernel_height*kernel_width];
for (int i = 0; i < kernel_height*kernel_width; i++)
{
mask[i] = kernel[i];
}
float * d_input, * d_output, * d_kernel;
cudaMalloc(&d_input, img_width*img_height*sizeof(float));
cudaMalloc(&d_output, img_width*img_height*sizeof(float));
cudaMalloc(&d_kernel, kernel_height*kernel_width*sizeof(float));
cudaMemcpy(d_input, input, img_width*img_height*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(d_kernel, mask, kernel_height*kernel_width*sizeof(float), cudaMemcpyHostToDevice);
dim3 blocksize(16,16);
dim3 gridsize;
gridsize.x=(img_width+blocksize.x-1)/blocksize.x;
gridsize.y=(img_height+blocksize.y-1)/blocksize.y;
image_convolution_kernel<<<gridsize,blocksize>>>(d_input,d_output,d_kernel,img_width,img_height,kernel_width,kernel_height);
cudaMemcpy(output, d_output, img_width*img_height*sizeof(float), cudaMemcpyDeviceToHost);
}
int main(){
float *in, *out;
int is = ih*iw;
in = new float[is];
out = new float[is];
for (int i = 0; i < is; i++) {in[i] = rand()%rng; out[i] = -1;}
image_convolution(in,out, ih, iw);
for (int iy = 1; iy < ih-1; iy++)
for (int ix = 1; ix < iw-1; ix++){
float temp = abs(-0.25 * (in[iy*iw + ix -1] + in[iy*iw + ix +1] + in[(iy-1)*iw + ix] + in[(iy+1)*iw + ix]) + in[iy*iw+ix]);
if (out[iy*iw+ix] != temp) {std::cout << "mismatch x: " << ix << " y: " << iy << " was: " << out[iy*iw+ix] << " should be: " << temp << std::endl; return 1;}}
return 0;
}
$ nvcc -o t1219 t1219.cu
$ cuda-memcheck ./t1219
========= CUDA-MEMCHECK
========= ERROR SUMMARY: 0 errors
$
For image dimensions which are exact multiples of the block size (16,16) (which was true for my previous test case) this problem won't show up -- the code will work correctly. For all other test cases, we need such a thread check.