I'm done ranting about Google and GPUs at last. From a technical standpoint, I believe TensorFlow vindicates my viewpoint and that's enough for me. This is cl

I'm done ranting about Google and GPUs at last. From a technical standpoint, I believe TensorFlow vindicates my viewpoint and that's enough for me. This is closure I really needed, OK? And besides, now I can concentrate my energy on asking WTH happened to Android performance between 4.x and 5.x J/K? And from a professional standpoint, I moved on and fixed the damage I let them inflict on my career. I eventually built exactly what I was told couldn't and shouldn't be done with GPUs. But back then, the argument Google made to me was that too much of the workloads I wanted to run on GPUs were serialized (hence brawny versus wimpy). And from a classical parallel algorithms perspective, they were correct. The problem is that from a GPU programming perspective, they weren't even wrong(tm). And at the time, I had ~5 years of CUDA programming (very early adopter) and 30,000+ lines of CUDA code to back that up whereas the people I tried to convince had zero such experience so there was just no way. My argument boiled down to TLDR: O(n^2) or higher probably belongs on GPUs. Would anyone disagree these days?