Question

Working on a filter following, I am having a problem of doing these pieces of codes for processing an image in GPU:

for(int h=0; h<height; h++) {
    for(int w=1; w<width; w++) {
    image[h][w] = (1-a)*image[h][w] + a*image[h][w-1];
    }
}

If I define:

dim3 threads_perblock(32, 32)

then each block I have: 32 threads can be communicated. The threads of this block can not communicate with the threads from other blocks.

Within a thread_block, I can translate that pieces of code using shared_memory however, for edge (I would say): image[0,31] and image[0,32] in different threadblocks. The image[0,31] should get value from image[0,32] to calculate its value. But they are in different threadblocks.

so that is the problem.

How would I solve this?

Thanks in advance.

Answer 1

If image is in global memory then there is no problem – you don’t need to use shared memory and you can just access pixels directly from image without any problem.

However if you have already done some processing prior to this, and a block of image is already in shared memory, then you have a problem, since you need to do neighbourhood operations which are outside the range of your block. You can do one of the following – either:

• write shared memory back to global memory so that it is accessible to neighbouring blocks (disadvantage: performance, synchronization between blocks can be tricky)

or:

• process additional edge pixels per block with an overlap (1 pixel in this case) so that you have additional pixels in each block to handle the edge cases, e.g. work with a 34×34 block size but store only the 32×32 central output pixels (disadvantage: requires additional logic within kernel, branches may result in warp divergence, not all threads in block are fully used)

Unfortunately neighbourhood operations can be really tricky in CUDA and there is always a down-side whatever method you use to handle edge cases.