Memory allocation is one of the most time consuming operations in a GPU so I wanted to allocate 2 arrays by calling cudaMalloc
once using the following code:
int numElements = 50000;
size_t size = numElements * sizeof(float);
//declarations-initializations
float *d_M = NULL;
err = cudaMalloc((void **)&d_M, 2*size);
//error checking
// Allocate the device input vector A
float *d_A = d_M;
// Allocate the device input vector B
float *d_B = d_M + size;
err = cudaMemcpy(d_A, h_A, size, cudaMemcpyHostToDevice);
//error checking
err = cudaMemcpy(d_B, h_B, size, cudaMemcpyHostToDevice);
//error checking
The original code is inside the samples folder of the cuda toolkit named vectorAdd.cu so you can assume h_A, h_B are properly initiated and the code works without the modification I made.
The result was that the second cudaMemcpy returned an error with message invalid argument.
It seems that the operation "d_M + size" does not return what someone would expect as device memory behaves differently but I don't know how.
Is it possible to make my approach (calling cudaMalloc once to allocate memory for two arrays) work? Any comments/answers on whether this is a good approach are also welcome.
UPDATE
As the answers of Robert and dreamcrash suggested I had to add number of elements (numElements) to the pointer d_M not the size which is the number of bytes. Just for reference there was no observable speedup.