I am trying to implement some operations in Torch, using GPU, however they are way slower than what they suppose to be, and I am assuming that is due to overhead of having it all in python.
Basically, I am working with DSP, and basically the only transform available is the FFT. Actually, I have to use other types of transforms, such as DCT, however I sort of implement them either via a linear layer or via FFT, see torch_dct 1.
I am considering implementing them as a CUDA kernel, not sure how, yet.
I started taking a look on Writing a Mixed C Cuda Extension 1, which I have a hard time to understand. I do understand some of it, but I also wonder where I could find more documentation on what function calls I could make.
Then I read CUDA C/C++ Basics 1. Maybe the next document to digest would be CUDA C++ PROGRAMMING GUIDE
I am not really sure if I am following the right path, so I am asking for some directions.