If I were a hiring person at AMD or Intel, I'd shortlist this guy for a job, as they need help competing against the headstart CUDA has in the GPU-base compute space.
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.
How does using Vulkan for computation fit into the OpenCL/CUDA landscape? Is CUDA's proprietary nature doing meaningful harm, and does Vulkan help?
"VkFFT aims to provide community with an open-source alternative to Nvidia's cuFFT library, while achieving better performance."<p>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".<p>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?
I think this guy will have no problem getting hired. Being conscious enough to push code online works so much better than the CV preparation courses. You know you're on the right path when you are asked to play up your CV abstract than to downplay it.<p>Personally, I would have a hard time hiring anyone without a Github account and less so working in a place where nobody has one.
What is "Native zero padding to model open systems"? And how come it is "up to 2x faster than simply padding input array with zeros"?
Very cool!<p>Seems a bit more feature complete than my take on the problem: <a href="https://github.com/Lichtso/VulkanFFT" rel="nofollow">https://github.com/Lichtso/VulkanFFT</a><p>Still, to beat CUDA with Vulkan a lot is still missing: Scan, Reduce, Sort, Aggregate, Partition, Select, Binning, etc.
> Support for big FFT dimension sizes. Current limits: C2C - (2^24, 2^15, 2^15),<p>What about bigger than big? > 2^29 or so ? Are these sizes for double precision ?
I'm very eager to see GPU acceleration make its way into audio production, which is all still heavily CPU bound.<p>A Free GPUFFT implementation will certainly help! Great work.