Parallelize four and more nested loops with CUDA - c++

Question

I am working on a compiler generating parallel C++ code. I am new to CUDA programming but I am trying to parallelize the C++ code with CUDA.
Currently if I have the following sequential C++ code:
for(int i = 0; i < a; i++) {
for(int j = 0; j < b; j++) {
for(int k = 0; k < c; k++) {
A[i*y*z + j*z + k*z +l] = 1;
}
}
}
and this results in the following CUDA code:
__global__ void kernelExample() {
int _cu_x = ((blockIdx.x*blockDim.x)+threadIdx.x);
int _cu_y = ((blockIdx.y*blockDim.y)+threadIdx.y);
int _cu_z = ((blockIdx.z*blockDim.z)+threadIdx.z);
A[_cu_x*y*z + _cu_y*z + _cu_z] = 1;
}
so each loop nest is mapped to one dimension, but what would be the correct way to parallelize four and more nested loops:
for(int i = 0; i < a; i++) {
for(int j = 0; j < b; j++) {
for(int k = 0; k < c; k++) {
for(int l = 0; l < d; l++) {
A[i*x*y*z + j*y*z + k*z +l] = 1;
}
}
}
}
Is there any similar way? Noteworthy: all loop dimensions are parallel and there are no dependencies between iterations.
Thanks in advance!
EDIT: the goal is to map all iterations to CUDA threads, since all iterations are independent and could be executed concurrently.

Answer 1

You could keep the outer loop unchanged. Also it is better to use .x as inner most loop so you can access the global memory efficiently.
__global__ void kernelExample() {
int _cu_x = ((blockIdx.x*blockDim.x)+threadIdx.x);
int _cu_y = ((blockIdx.y*blockDim.y)+threadIdx.y);
int _cu_z = ((blockIdx.z*blockDim.z)+threadIdx.z);
for(int i = 0; i < a; i++) {
A[i*x*y*z + _cu_z*y*z + _cu_y*z + _cu_x] = 1;
}
}
However if your a,b,c,d are all very small, you may not be able to get enough parallelism. In that case you could convert a linear index to n-D indices.
__global__ void kernelExample() {
int tid = ((blockIdx.x*blockDim.x)+threadIdx.x);
int i = tid / (b*c*d);
int j = tid / (c*d) % b;
int k = tid / d % c;
int l = tid % d;
A[i*x*y*z + j*y*z + k*z + l] = 1;
}
But be careful that calculating i,j,k,l may introduce a lot of overhead as integer division and mod are slow on GPU. As an alternative you could map i,j to .z and .y, and calculate only k,l and more dimensions from .x in a similar way.