Question

I have the next for nested loop and I would like to port it to CUDA to be run on a GPU

int current=0;
int ptr=0;

for (int i=0; i < Nbeans; i++){
  for(int j=0;j< NbeamletsPerbeam[i];j++){

     current = j + ptr;

     for(int k=0;k<Nmax;k++){
        ......
     }

     ptr+=NbeamletsPerbeam[i];
 }   
}

I would be very happy if any body has an idea of how to do it or how can be done.
We are talking about Nbeams=5, NbeamletsPerBeam around 200 each.

This is what I currently have but I am not sure it is right…

 for (int i= blockIdx.x; i < d_params->Nbeams; i += gridDim.x){
            for (int j= threadIdx.y; j < d_beamletsPerBeam[i]; j+= blockDim.y){
                 currentBeamlet= j+k;
                 for (int ivoxel= threadIdx.x; ivoxel < totalVoxels; ivoxel += blockDim.x){

Answer 1

I would suggest this idea. But you might need to do some minor modifications based on your code.

dim3 blocks(NoOfThreads, 1);
dim3 grid(Nbeans, 1);

kernel<<grid, blocks, 1>>()

__global__ kernel()
{
   int noOfBlocks = ( NbeamletsPerbeam[blockIdx.x] + blockDim.x -1)/blockDim.x;

   for(int j=0; j< noOfBlocks;j++){
     //  use threads and compute.... 
     if( (threadIdx.x * j) < NbeamletsPerbeam[blockIdx.x]) {
       current = (threadIdx.x * j) + ptr;

       for(int k=0;k<Nmax;k++){
          ......
       }

       ptr+=NbeamletsPerbeam[blockIdx.x];
    }
 }   
} 

This should do the trick and gives you better parallelization.