> First time for ML that is not deep learning
What do you mean by this? Virtually all "classic" or "shallow" ML can be GPU-accelerated, from linear regression to SVM to GBM.
What do you mean by this? Virtually all "classic" or "shallow" ML can be GPU-accelerated, from linear regression to SVM to GBM.
Modern GPUs are GP-GPUs: where GP means "general purpose": you can run any code on GPGPUs. But if you want to gain real speed-ups you will have to program in an awkward style ("data parallel"). I am not aware of GPU acceleration of the work-horses of symbolic AI, such as Prolog, or SMT solving. There has been a lot of work on running SAT-solvers on GPUs, but I don't think this has really succeeded so far.
I'm not saying symbolic AI has been GPU accelerated in the past, but that non-deep ML has been.
Can you point towards papers that report substantial GPU acceleration on what you call "non-deep ML"?
So ML is AI, but just a small part of it.
As for papers, here's one showing GPU speedups for the 3 big GBM packages: https://arxiv.org/pdf/1809.04559.pdf. Their setup shows a 3-7x speedup for XGBoost.