Complicated for loop to be ported to a CUDA kernel - cuda

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.