filtering an image, best practices

Question

I have an input image "let it be a buffer of 1024 * 1024 pixels, with RGBA color data"

what I want to do for each pixel, is to filter it depending on neighbors , like [-15,15] in x and y directions

so my concern is, doing this with global memory will do like 31 * 31 global memory access for each pixel "which would be very performance bottleneck" , also I'm not sure about the behavior of multiple threads trying to read from the same memory location at the same time "may be some of them fail to read so -> rubbish data in -> rubbish data out"

this question is for CUDA or OpenCL as the concept should be the same I know that shared memory (per work group) or local memory (per thread) won't solve this as I can't read another thread local memory, or another group shared memory "correct me if I misunderstand this concept"

Answer 1

Shared memory is a typical approach to this problem, although the stencil area (31*31) is quite large. Data re-use benefit can still be gained however. Since adjacent pixel computations only extend the region required by one column, in a 16KB shared memory array of 32bit RGBA pixels, you could have enough data for at least 64 threads to cooperatively compute their pixel values out of a single shared memory load.

Regarding the concern about multiple threads reading the same location, there is no possibility for garbage data reads. Certainly there is a possibility for contention leading to a performance impact, but in fact with an orderly for-loop progression in the kernel, no threads will be reading the same location at the same time anyway. With appropriate data organization there will be good opportunity for coalesced reads from global memory and no bank conflicts in shared memory.

This type of problem is well-suited for GPUs e.g. CUDA or OpenCL, and there are many examples of programs like this on SO.