Question

I have the following code in Java:

float in[][] = new float[2][2];

float B[] = new float[2]; 
float A[] = new float[2]; 
float res[] = new float[A[0].length];

for(float r : res){
    r = 0;
}

for (int i = 0; i < A[0].length; i++) {
    for (int j = 0; j < B[0].length; j++) {
        res[i] += A[j] * in[j][i];
}

I simplified it at most, so you should not search for a real logic in there :).

I struggle for some hours converting this in CUDA because of the += statement in the loop.

I started with something like this :

extern "C"
__global__ void filter(float* in, float* A, float* B, float* res, int in_size){

    unsigned int x = blockIdx.x*blockDim.x + threadIdx.x;
    unsigned int y = blockIdx.y*blockDim.y + threadIdx.y;

    res[x] = A[y] * in[x + y * in_width];

}

but quickly realized it couldn’t work because of all the threads trying to set the same variable.

I read the example of the dot product in this presentation,
but I don’t really see how to adapt that with my need of two dimensions for in.

I don’t ask for a complete solution, but any direction would definitely be appreciated.

Thx,

Answer 1

Too much CUDA killed my head.

I found a partial solution by unrolling one of the loops inside my kernel.
Here it what it looks like right now :

extern "C"
__global__ void filter(float* in, float* A, float* res, const int in_width, const int sizeB){
    unsigned int x = blockIdx.x*blockDim.x + threadIdx.x;
    int i = 0;

    for(i = 0; i < sizeB; i++){
        res[x] += A[i] * in[i + x * in_width];
    }

}

I am sure I can find better, but I think I’ll stick with this for today 🙂