I am currently going through the tutorial examples on http://code.google.com/p/stanford-cs193g-sp2010/ to learn CUDA. The code which demostrates __global__
functions is given below. It simply creates two arrays, one on the CPU and one on the GPU, populates the GPU array with the number 7 and copies the GPU array data into the CPU array.
#include <stdlib.h>
#include <stdio.h>
__global__ void kernel(int *array)
{
int index = blockIdx.x * blockDim.x + threadIdx.x;
array[index] = 7;
}
int main(void)
{
int num_elements = 256;
int num_bytes = num_elements * sizeof(int);
// pointers to host & device arrays
int *device_array = 0;
int *host_array = 0;
// malloc a host array
host_array = (int*)malloc(num_bytes);
// cudaMalloc a device array
cudaMalloc((void**)&device_array, num_bytes);
int block_size = 128;
int grid_size = num_elements / block_size;
kernel<<<grid_size,block_size>>>(device_array);
// download and inspect the result on the host:
cudaMemcpy(host_array, device_array, num_bytes, cudaMemcpyDeviceToHost);
// print out the result element by element
for(int i=0; i < num_elements; ++i)
{
printf("%d ", host_array[i]);
}
// deallocate memory
free(host_array);
cudaFree(device_array);
}
My question is why have they worded the cudaMalloc((void**)&device_array, num_bytes);
statement with a double pointer? Even here definition of cudamalloc() on says the first argument is a double pointer.
Why not simply return a pointer to the beginning of the allocated memory on the GPU, just like the malloc
function does on the CPU?
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