I see this response often and I have to say, "just throw it on the GPU" is phrase only uttered by someone who has never personally attempted to do that. The sim

I see this response often and I have to say, "just throw it on the GPU" is phrase only uttered by someone who has never personally attempted to do that. The simplicity of CUDA and `pip install tensorflow-gpu` belie a huge amount of engineering prowess; go look at the source for XLA (https://github.com/tensorflow/tensorflow/tree/master/tensorflow/compiler/xla https://github.com/tensorflow/tensorflow/tree/master/tensorf...) if you don't believe me. To answer your question, tons of scientific computing tasks parallelize nicely and are a horrible fit for a GPU, because they involve branching, or non-deterministic runtimes, or need a lot of memory bandwidth, or rely on highly optimized legacy numerical code that is written in Fortran, or any one of a number of other reasons. Deep learning is a very narrow use fact and, in fact, GPUs and DL symbiotically co-evolved over the last 15 years to the point where GPU hardware is essentially designed to efficiently train neural nets. (And run games.)