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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
by Reelin 6y ago
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 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 example, NAMD employs cuFFT on the GPU in some cases. (https://aip.scitation.org/doi/10.1063/5.0014475 https://aip.scitation.org/doi/10.1063/5.0014475)
- amelius 6y agoMachine learning uses CNNs, which are directly based on FFTs.
- hikarudo 6y agoHow are CNNs directly based on FFTs? Sure you can use CNNs with FFT features, but in my experience this is not common.
- amelius 6y agoConvolutions are typically computed using FFTs. https://en.wikipedia.org/wiki/Convolution_theorem https://en.wikipedia.org/wiki/Convolution_theorem
- DTolm 6y agoHe is not wrong, convolutions between an image and a small kernel can be done faster by direct multiplication than by padding the kernel and performing FFT + iFFT. This is what tensor cores are aiming to do really fast. However, doing a convolution betwen an image and a kernel with the similar size is the general use case for the convolution theorem and is the thing that is currently implemented in VkFFT.