It's definitely both.

I'm sure plenty of professors use CUDA in their courses because it's what they actually use. At the same time, in 2013 when I was in college I took a course on "parallel computing" as a CS elective. The professor told us on day 1 that NVidia was sponsoring the course and had donated a bunch of GPUs to the clusters we could remotely connect into for the sake of the class. Naturally we used CUDA exclusively.

I know for a fact that this happened at a lot of schools. I don't know if it's still happening since I'm not in that world anymore, but don't see why it would have stopped.

CUDA is extremely simple, the classes might as well be on rails. OpenCL is like impossible without graphics and/or CUDA/distributed computing/operating system experience.

I'm not sure if I really agree - the level of abstraction used for each is extremely similar. There's not really any "Graphics Pipeline Specifics" pollution in OpenCL or CUDA.

You can pretty much translate something like https://github.com/jcupitt/opencl-experiments/blob/master/Op... with string replace function names.

You get free access to hardware for courses if you teach CUDA courses.