Back then, the case was unclear because so little stuff existed outside of the MPI & graphics worlds. Today, for most engineering leaders today, the CUDA ecosystem, and emerging productivity layers like RAPIDS is quite rich. They make OpenCL a niche & risky call. The goal should be writing ~100X more code than the OpenCL ecosystem is giving you, it's just surface level.
Good to know. I have a laptop with the Ryzen 3500U and the Vega 8 integrated graphics. I love it for what I use it for (light gaming) but I'm not familiar with the higher-end mobile GPUs.
> What's stopping them from using OpenCL? Whether by using an ML library that actually supports OpenCL outright (e.g. Caffe, PlaidML) or using something like Hipify to convert from CUDA to OpenCL, that shouldn't be a limiting factor.
I assume just familiarity? That's just what the ML people that I know use.
I would really like to see OpenCL take a dominant position in the GPU compute space, regardless of the GPU manufacturer that has best support for it.
I think we're still a little ways off from a tipping point towards OpenCL. I think it will be accelerated with Intel's launch of the Xe discrete graphics.
> Are the mobile AMD GPU options all that great?
I haven't had issues with them, even with high-performance stuff like gaming and CAD (can't speak much to machine learning, though).
> Nvidia has CUDA, which is important for (among other things) folks that use machine learning frameworks
What's stopping them from using OpenCL? Whether by using an ML library that actually supports OpenCL outright (e.g. Caffe, PlaidML) or using something like Hipify to convert from CUDA to OpenCL, that shouldn't be a limiting factor.