[scottlegrand2]

At a previous gig we tried to do this: port a computational graph that wasn't a neural network to tensorflow. It was a disaster. Tensorflow is very tightly optimized for the things Google think are important. if you fall off of those paths tensorflow is a god-awful slow tool to use. We saw a ~20x regression in performance.

in contrast, when we wrote bespoke GPU code for the graph, we saw a ~25x performance increase over relying on CPU plus MKL. I am being deliberately vague here and I cannot give further detail.

[ec109685]

You are somewhat uniquely qualified to do so:

> possibly the world's first or second (full-time) CUDA programmer, with 14 filed patents, and the world's fastest implementations of molecular Dynamics (CUDA ports of Folding@Home and AMBER).

[scottlegrand2]

Yes, compared to someone who insists on doing all of their computation from python alone, I have a unique (and in my opinion absurd) advantage.

Because I think that's insane. It's one thing if you don't care about speed and you care more about time-to-market. It's another thing if you're complaining about things being too slow but you're not willing to learn about anything that would let you do anything about it. I run into far more of the latter.