91–100 of 132 posts
What are the common applications for these sorts of GPU-accelerated FFTs? We mostly just solved problems analytically in undergrad, and the little bit of naive coding we did seemed pretty fast. I feel like this must be used for problems I would have learned about in grad school, if I had continued in electrical engineering.
> Support for big FFT dimension sizes. Current limits: C2C - (2^24, 2^15, 2^15), What about bigger than big? > 2^29 or so ? Are these sizes for double precision ?
What are the common applications for these sorts of GPU-accelerated FFTs? We mostly just solved problems analytically in undergrad, and the little bit of naive coding we did seemed pretty fast. I feel like this must be used for problems I would have learned about in grad school, if I had continued in electrical engineering.
Likely any HPC application that has an FFT somewhere in its pipeline and is otherwise amenable to being run on a GPU. Fluid flow, heat transfer, and other such physical phenomena that you might want to simulate. Phase correlation in image processing is another example. ( https://en.wikipedia.org/wiki/Phase_correlation ) MD simulations rely on FFT but I'm not sure how much is typically (or can be) done on the GPU. For…
What are the common applications for these sorts of GPU-accelerated FFTs? We mostly just solved problems analytically in undergrad, and the little bit of naive coding we did seemed pretty fast. I feel like this must be used for problems I would have learned about in grad school, if I had continued in electrical engineering.
What are the common applications for these sorts of GPU-accelerated FFTs? We mostly just solved problems analytically in undergrad, and the little bit of naive coding we did seemed pretty fast. I feel like this must be used for problems I would have learned about in grad school, if I had continued in electrical engineering.
There are no error bars on the graphs, so it's very hard to judge if the minor differences are significant. I work in research, so probably I'm peculiar about this point, but: I'd expect better from anyone who's taken basic statistics. But from a quick look, it seems like the performance is pretty much just "on par".
It would also be nice to know how performance is on other hardware. I'm assuming it's tuned to nvidida GPUs (or maybe even the specific GPU mentioned). But how does this perform on Intel or AMD hardware? How does it compare to `rocFFT` or Intel's own implementation?
Earlier quoted context omitted.
I don't think it's that easy? LGPLv3 has an explicit carve-out for headers which makes that scenario easy, but this is 2.1...
Paragraph 5 of the LGPL version 2.1 states: A program that contains no derivative of any portion of the Library, but is designed to work with the Library by being compiled or linked with it, is called a "work that uses the Library". Such a work, in isolation, is not a derivative work of the Library, and therefore falls outside the scope of this License.
> However, linking a "work that uses the Library" with the Library creates an executable that is a derivative of the Library (because it contains portions of the Library), rather than a "work that uses the library". The executable is therefore covered by this License. Section 6 states terms for distribution of such executables.
When a "work that uses the Library" uses material from a header file that is part of the Library, the object code for the work may be a derivative work of the Library even though the source code is not. Whether this is true is especially significant if the work can be linked without the Library, or if the work is itself a library. The threshold for this to be true is not precisely defined by law.
That certainly does have consequences for what you can do with the software - the object code of your compiled program will include parts of the library.
"VkFFT aims to provide community with an open-source alternative to Nvidia's cuFFT library, while achieving better performance." There are no error bars on the graphs, so it's very hard to judge if the minor differences are significant. I work in research, so probably I'm peculiar about this point, but: I'd expect better from anyone who's taken basic statistics. But from a quick look, it seems like the performance is…
I have tested VkFFT on Intel UHD620 GPU and the performance scaled on the same rate as most benchmarks do. There are a couple of parameters that can be modified for different GPUs (like the amount of memory coalesced, which is 32bits on Nvidia GPUs after Pascal and is 64bits for Intel). I have no access to an AMD machine, otherwise I would have refined the lauch configuration parameters for it too. I have not tested other libraries than cuFFT yet.
"VkFFT aims to provide community with an open-source alternative to Nvidia's cuFFT library, while achieving better performance." There are no error bars on the graphs, so it's very hard to judge if the minor differences are significant. I work in research, so probably I'm peculiar about this point, but: I'd expect better from anyone who's taken basic statistics. But from a quick look, it seems like the performance is…
The FFT and iFFT are performed consecutively up to 1000 times and then each run is done 5 more times. The total result is averaged both for VkFFT and cuFFT and stays roughly the same between launches. The minor performance gains (5-20%) are noticeable. If you have a better testing technique, I am open to the suggestions. I have tested VkFFT on Intel UHD620 GPU and the performance scaled on the same rate as most bench…
Also: I should've said this in my first post already, which in hindsight might sound too negative: I think this is a cool project and you did a great job! I just thought this might improve the presentation of your results a bit.
Earlier quoted context omitted.
The FFT and iFFT are performed consecutively up to 1000 times and then each run is done 5 more times. The total result is averaged both for VkFFT and cuFFT and stays roughly the same between launches. The minor performance gains (5-20%) are noticeable. If you have a better testing technique, I am open to the suggestions. I have tested VkFFT on Intel UHD620 GPU and the performance scaled on the same rate as most bench…
Thanks for the further clarification! If you ran this several times, you could calculate standard deviations or confidence intervals. It would be nice if you could report one such measure, so it's clearer that the differences are not just some random fluctuations. E.g. you could include them as error bars in your plots. You could also run a statistical test (in this case, a t-test is very easy to do) and report the p…