I’m designing a CUDA app to process some video. The algorithm I’m using calls for filling in blank pixels in a way that’s not unlike Conway’s game of life: if the pixels around another pixels are all filled and all of similar values, the specific pixel gets filled in with the surrounding value. This iterates until all the number of pixels to fix is equal to the number of pixels to fix in the last iteration (ie, when nothing else can be done).
My quandary is this: the previous and next part of the processing pipeline are both implemented in CUDA on the GPU. It would be expensive to transfer the entire image back to RAM, process it on the CPU, then transfer it back to the GPU. Even if it’s slower, I would like to implement the algorithm in CUDA.
However, the nature of this problem requires synchronization between all threads to update the global image between each iteration. I thought about just calling the Kernel for each iteration multiple times, but I cannot determine when the process is “done” unless I transfer data back to the CPU between each iteration, which would introduce a large inefficiency because of the memory transfer latency through the PCI-e interface.
Does anyone with some experience with parallel algorithms have any suggestions? Thanks in advance.
It sounds like you need an extra image buffer, so that you can keep the unmodified input image in one buffer and write the processed output image into the second buffer. That way each thread can process a single output pixel (or small block of output pixels) without worrying about synchronization etc.