[jampekka]

For a lot of cases you don't really need that much performance. Modern processors are plenty fast. It seems that current push to use GPU also pushes people towards GPU oriented solutions, such as using huge NNs for more or less anything, while other approaches would in many cases be magnitudes more efficient and robust.

GPUs (or "wide SIMDs" more generally) have quite profound limitations. Branching is very limited, recursion is more or less impossible and parallelism is possible only for identical operations. This makes for example many recursion-based time-series methods (e.g. Bayesian filtering) very tricky or practically impossible. From what I gather, running recurrent networks is also tricky and/or hacky on GPU.

GPUs are great for some quite specific, yet quite generally applicable, solutions, like tensor operations etc. But being tied to GPUs' inherent limitations also limits the space of approaches that are feasible to use. And in the long run this can stunt the development of different approaches.

[mpfundstein]

> For a lot of cases you don't really need that much performance. Modern processors are plenty fast. It seems that current push to use GPU also pushes people towards GPU oriented solutions, such as using huge NNs for more or less anything, while other approaches would in many cases be magnitudes more efficient and robust.

for instance?

[_coveredInBees]

I still don't get the criticism of Pytorch. If anything, you can get the best of both worlds in many way with their API supporting on GPU and on CPU operations in exactly the same ways.