Seems like some of it is still CUDA-only, from the AliceVision INSTALL.md [0].

I have to wonder how AMD is letting NVIDIA win so big in academia, when their GPUs are actually quite competitive. I'm beginning to think that they have some management practice or culture which makes it difficult for them to retain software talent, otherwise I can't really understand how they always seem to have a glaring weakness somewhere that they're not addressing.

[0]: https://github.com/alicevision/AliceVision/blob/develop/INST...

I don't think it's AMD's fault really, CUDA just as an insane amount of inertia in the space and there's a whole lot of incentive for anyone to do anything about it.

As much as I hate it I've been vendor locked to NV for years and as a result I don't really care anymore. I'll never buy a AMD card because I need to get work done and get paid. It sucks, but not enough for me to go through the pain of trying to work with the far far inferior OpenCL based software for rendering/photogrammetry etc. I don't even touch ML, but I assume the story is the same there.

What's the value proposition for writing software that works with OpenCL when your entire market already has NV cards? The price differential is tiny too, maybe if AMD came down like 30% for the equivalent computational power.

Nvidia had (has?) CUDA centers of excellence for more than a decade, they gave GPUs to universities, they've had their own compute related conference for years, they have a huge set of libraries.

During all the time, AMD did essentially nothing. (They weren't financially in the position to do so.) Then they missed the AI boom.

Now they are trying, right at the time where the compute specific performance lead that they once had is gone and when their perf/W is terrible (which is not a good thing for datacenters.)

There is really no mystery here.