[Reelin]

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 example, NAMD employs cuFFT on the GPU in some cases. (https://aip.scitation.org/doi/10.1063/5.0014475)

[amelius]

Machine learning uses CNNs, which are directly based on FFTs.

[hikarudo]

How are CNNs directly based on FFTs? Sure you can use CNNs with FFT features, but in my experience this is not common.

[amelius]

Convolutions are typically computed using FFTs.

https://en.wikipedia.org/wiki/Convolution_theorem

[DTolm]

He 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.