EDIT: Perhaps its my inexperience, but is anyone else confused by the OneAPI rollout? There isn't exactly backwards compatiblity with the Classic Intel compiler, and an embarassing amount of time elapsed until I realized "Data Parallel C++" doesn't refer to parallel programming in C++, but rather an Intel-developed API built atop C++.
GPU acceleration is not a magic "go fast" machine. It only works for certain classes of embarrassingly parallel algorithms. In a nutshell, the parallel regions need to be long enough that the speedup from doing them in the GPU's silicon outweighs the relatively high cost of getting data into and out of the GPU.
That's a fairly easy scenario to achieve with neural networks, which have a pretty high math-to-data ratio. Other machine learning algorithms, not necessarily. But basically all of them can benefit from the CPU's vector instructions, because they live in the CPU rather than out on a peripheral, so there's no hole you need to dig yourself out of before they can deliver a net benefit.
I would also say that what academics are doing is not necessarily a good barometer for what others are doing. In another nutshell, academics' professional incentives encourage them to prefer the fanciest thing that could possibly work, because their job is to push the frontiers of knowledge and technology.
Most people out in industry, though, are incentivized to do the simplest thing that could possibly work, because their job is to deliver software that is reliable and delivers a high return on investment.
Like, I would guess that the potential benefit to my team's productivity from eliminating (over)reliance on weakly typed formats such as JSON from our information systems could be orders of magnitude greater.
Problem is... the ASICs are really good for certain classes of ML problems but aren't really all that general.
https://www.intel.com/content/www/us/en/developer/articles/t...