There are large, large gaps of parallel stuff that GPUs can't do fast. Anything sparse (or even just shuffled) is one example. There are lots of architectures that are theoretically superior but aren't popular due to not being GPU friendly.
That’s not a flaw in parallelism. The mathematical reality remains that independent operations scale better than sequential ones. Even if we were stuck with current CPU designs, transformers would have won out over RNNs.
Unless you are pushing back on my comment "all kinds" - if so, I meant "all kinds" in the way someone might say "there are all kinds of animals in the forest", it just means "lots of types".
I was pushing back against "all kinds". The reason is that I've been seeing a number of inherently parallel architectures, but existing GPUs don't like some aspect of them (usually the memory access pattern).
