On 08/29/2010 08:13 PM, Alan Reiner wrote: > 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 > Hi Alan, CUDA comes with a free but propietary license (far away from GPL ideology),is strictly system dependent (there are not a freebsd version for example) and is not opensource. It's important to say that CUDA is enabled on certain number of NVIDIA chip and not on generic accelerated graphic card,there are a big restrictions. Increase speed but to sacrify portability and code openness adding close source library is not a good idea for me, in addiction NVIDIA have a two face with opensource community, i don't trust that is reliable if there are any problems or suggestions. At the current day i have see only problematic NVIDIA closed source products like gpu drivers and too few interest in the opensource world and their users. Jacopo _______________________________________________ Gimp-developer mailing list Gimp-developer@xxxxxxxxxxxxxxxxxxxxxx https://lists.XCF.Berkeley.EDU/mailman/listinfo/gimp-developer
