why use cudamemcpy instead of a simple for loop

Accelerated Computing CUDA CUDA Programming and Performance
#1

A newbie question: why we use “cudamemcpy” to initialize a device variable instead of using a for loop. Both seem to work for me. For example,

cudaMemcpy(a_d,a_h, n*sizeof(int), cudaMemcpyHostToDevice)
versus.
for(int i=0;i<n;i++) a_d[i]=a_h[i].

Thanks…

#2

Unless you’re using device emulation, your method will not work at all–the GPU’s memory is not directly addressable by the CPU. It’ll probably segfault and crash.

#3

You are right. Thanks tmurray again!

I have another question regarding how to use shared memory. To simplify the case, say we have two arrays float A[10] and float B[10]. Each element value in B depends on values of three consecutive elements in A, e,g. B[1]=a0A[0]+a1A[1]+a2A[2], B[2]=a1A[1]+a2A[2]+a3A[3]… How do I allocate appropriate shared memory for array A?

Thanks!

#4

If you look at the separable convolution example in the SDK it addresses a similar problem. A solution is to add additional threads to your thread blocks. Each thread is responsible for reading a single value into the shared memory array. Then put in a conditional for the summing step to have the extra threads sit out for the rest of the kernel.

#5

Thanks… jgoffeney. let me give it a try…