This is a long message, so let me start with the punchline: *I have a lot of CUDA code that harnesses a user's GPU to accelerate very tedious image processing operations, potentially 200x speedup. I am ready to donate this code to the GIMP project. This code can be run on Windows or Linux, and probably Mac, too.* *It only works on NVIDIA cards, but can detect at runtime whether the user has acceptable hardware, and disables itself if not.* Hi all, I'm new here. I work on real-time image processing applications that must run at 60-240Hz, which is typically too fast for doing things like convolutions on large images. However, the new fad is to use CUDA to harness the parallel computing power of your graphics card to do computations, instead of just rendering graphics. The speed ups are phenomenal. For instance, I implemented a basic convolution algorithm (blurring), which operates on a 4096x4096 image with a 15x15 kernel/PSF. On my CPU it took *27 seconds* (AMD Athlon X3 440). When running the identical algorithm in CUDA, I get it done in *0.1 to 0.25 seconds*, so between 110x to 250x speedup (NVIDIA GTX 460). Which side of the spectrum you are on depends on whether the memory already resides in the GPU device memory, of it needs to be copied in/out on each operation. Any kind of operation that resembles convolution, such as edge detection, blurring, morphology operations, etc, are all highly parallelizable and ideal for GPU-acceleration. *I have a lot of this code already written for grayscale images, and can be donated to the GIMP project.* I would be interested to expand the code to work on color images (though, I suspect just doing it three times on each channel would probably not be ideal), and I don't think it will be that hard to integrate into the existing GIMP project (only a couple extra libraries need to be added for a user's computer to benefit from it). Additionally, the CUDA comes with convenient functions for determining whether a user has a CUDA-enabled GPU, and can default to regular CPU operations if they don't have one. It can determine how many cards they have, select the fastest one, and adjust the function calls to accommodate older GPU cards. Therefore, I believe the code can safely be integrated and dynamically enable itself only if it can be used. My solution is for any image size (within the limit of GPU memory), but the kernel/PSF size must be odd and no larger than 25x25. It's not to say larger kernel sizes can't be done in CUDA, but my solution is "elegant" for sizes smaller than that, due to having a limited amount of shared memory. I believe it will still work up to a 61x61 kernel but with substantial slowdown (though, probably still much faster than CPU). Beyond that, I believe a different algorithm is needed. I have implemented basic convolution (which assumes 0s outside the edge of the image), bilateral filter (which is blurring without destroying edges), and most of the basic binary morphological operations (kernel-based erode, dilate, opening, closing). I believe it would be possible to develop a morphology plugin, that allows you to start with a binary image, and click buttons for erode, dilate, opening, etc, and have it respond immediately. This would allow someone to start with an image, and try lots of different combinations of morphological operations to determine if their problem can be solved with morphology (which usually requires a long and complex sequence of morph ops). Unfortunately, I don't have much time to become a GIMP developer, but I feel like I can still contribute. I will happily develop the algorithms to be run on the GPU, as they will probably benefit my job, too (I'm open to suggestions for functions that operate on the whole image, but independently). And I can help with the linking to CUDA libraries, which NVIDIA claims can be done quickly by someone with no CUDA experience. Please let me know if anyone is interested to work with me on this: etotheipi@xxxxxxxxx -Alan _______________________________________________ Gimp-developer mailing list Gimp-developer@xxxxxxxxxxxxxxxxxxxxxx https://lists.XCF.Berkeley.EDU/mailman/listinfo/gimp-developer
