How can I write the memory pointer in CUDA [duplicate] - pointers

Question

This question already has an answer here:
Summing the rows of a matrix (stored in either row-major or column-major order) in CUDA
(1 answer)
Closed 5 years ago.
I declared two GPU memory pointers, and allocated the GPU memory, transfer data and launch the kernel in the main:
// declare GPU memory pointers
char * gpuIn;
char * gpuOut;
// allocate GPU memory
cudaMalloc(&gpuIn, ARRAY_BYTES);
cudaMalloc(&gpuOut, ARRAY_BYTES);
// transfer the array to the GPU
cudaMemcpy(gpuIn, currIn, ARRAY_BYTES, cudaMemcpyHostToDevice);
// launch the kernel
role<<<dim3(1),dim3(40,20)>>>(gpuOut, gpuIn);
// copy back the result array to the CPU
cudaMemcpy(currOut, gpuOut, ARRAY_BYTES, cudaMemcpyDeviceToHost);
cudaFree(gpuIn);
cudaFree(gpuOut);
And this is my code inside the kernel:
__global__ void role(char * gpuOut, char * gpuIn){
int idx = threadIdx.x;
int idy = threadIdx.y;
char live = '0';
char dead = '.';
char f = gpuIn[idx][idy];
if(f==live){
gpuOut[idx][idy]=dead;
}
else{
gpuOut[idx][idy]=live;
}
}
But here are some errors, I think here are some errors on the pointers. Any body can give a help?

Answer 1

The key concept is the storage order of multidimensional arrays in memory -- this is well described here. A useful abstraction is to define a simple class which encapsulates a pointer to a multidimensional array stored in linear memory and provides an operator which gives something like the usual a[i][j] style access. Your code could be modified something like this:
template<typename T>
struct array2d
{
T* p;
size_t lda;
__device__ __host__
array2d(T* _p, size_t _lda) : p(_p), lda(_lda) {};
__device__ __host__
T& operator()(size_t i, size_t j) {
return p[j + i * lda];
}
__device__ __host__
const T& operator()(size_t i, size_t j) const {
return p[j + i * lda];
}
};
__global__ void role(array2d<char> gpuOut, array2d<char> gpuIn){
int idx = threadIdx.x;
int idy = threadIdx.y;
char live = '0';
char dead = '.';
char f = gpuIn(idx,idy);
if(f==live){
gpuOut(idx,idy)=dead;
}
else{
gpuOut(idx,idy)=live;
}
}
int main()
{
const int rows = 5, cols = 6;
const size_t ARRAY_BYTES = sizeof(char) * size_t(rows * cols);
// declare GPU memory pointers
char * gpuIn;
char * gpuOut;
char currIn[rows][cols], currOut[rows][cols];
// allocate GPU memory
cudaMalloc(&gpuIn, ARRAY_BYTES);
cudaMalloc(&gpuOut, ARRAY_BYTES);
// transfer the array to the GPU
cudaMemcpy(gpuIn, currIn, ARRAY_BYTES, cudaMemcpyHostToDevice);
// launch the kernel
role<<<dim3(1),dim3(rows,cols)>>>(array2d<char>(gpuOut, cols), array2d<char>(gpuIn, cols));
// copy back the result array to the CPU
cudaMemcpy(currOut, gpuOut, ARRAY_BYTES, cudaMemcpyDeviceToHost);
cudaFree(gpuIn);
cudaFree(gpuOut);
return 0;
}
The important point here is that a two dimensional C or C++ array stored in linear memory can be addressed as col + row * number of cols. The class in the code above is just a convenient way of expressing this.