I wish more attention would be towards open source alternatives for CUDA, such as AMD's ROCm[1][2] and Julia framework using it - AMDGPU.jl[3]. It's sad to see so many people praise NVIDIA which is the enemy of open source, openly hostile to anything except their oversized proprietary binary blobs.
AMD has done great work in a very short amount of time, but let's not forget that they're still very new to the GPU compute game. The ROCm stack is overall still pretty buggy, and definitely hard to build in ways other than what AMD deems officially supported.
As AMDGPU.jl's maintainer, I do certainly appreciate more users using AMDGPU.jl if they have the ability to, but I don't want people to think that it's anywhere close in terms of maturity, overall performance, and feature-richness compared to CUDA.jl. If you already have access to an NVIDIA GPU, it's painless to setup and should work really well for basically anything you want to with it. I can't say the same about AMDGPU.jl right now (although we are definitely getting there).
Most people hack on this stuff for work, and time is money. OpenCL is just a lot less productive than CUDA for most tasks. The NVidia price premium isn't big enough to make people switch over.
Agree, been doing some cuda as part of my job since 2009, and it works very smooth, you could write non trivial programs after less than one week of learning (if you know C before). When opencl got a little hype, I had a look, I didn't manage to run a simple example, and I was asking myself: do I really want to have to use all this boilerplate code? Also at least a while back there is no good fft outside of cuda, maybe that changed though.
This is really the main problem. As far as I know, ROCm requires a quite expensive GPU for data centers (if you want to have a current GPU) which makes it quite difficult to build a community around ROCm.
I would love to, however for practically all tasks I need only the CUDA backend is available, not ROCm - an that is of course bad not just for open source, but also for the owners of AMD GPUs. The popularity of Nvidia's solution is so overwhelming AMD would need to do something revolutionary in order to catch up (supporting Windows and macOS wouldn't hurt either).
As AMDGPU.jl's maintainer, I do certainly appreciate more users using AMDGPU.jl if they have the ability to, but I don't want people to think that it's anywhere close in terms of maturity, overall performance, and feature-richness compared to CUDA.jl. If you already have access to an NVIDIA GPU, it's painless to setup and should work really well for basically anything you want to with it. I can't say the same about AMDGPU.jl right now (although we are definitely getting there).