Training material seems to be especially rich re GPU kernels and SIMD.

I wonder if there is extra effort put into this because they are useful for the researchers working on the models or just a sub-domain that language models are a great fit for and humans have trouble with?

Well, GPU kernels are co-designed really hard. A lot of it is, async tile pipelines + spam my MMA primtives.

Obviously it's still hard, but the point is that, by construction (cause like, NVIDIA literally releases primitives like this, and/or people like TK build slightly higher-level primitives over the base hardware primitives), if you learn the complicated language, you can get really good results, and on some level you "know" you're right by construction even before you go to the actual empirical tests (since you're operating over a higher-level "language", and not arbitrary byte accesses).

Honestly a lot of interfaces and frameworks you could argue are like that, so it's not really a point for GPU kernels relative to other things. But maybe a hint as to what I personally think is important in the AI era - finding the right cuts, the right high leverage abstractions, as otherwise AI is going to produce spaghetti nonsense.

Anecdotally, I saw Opus 5 come up with a complicated loop unrolling technique when I asked it to implement a simple biquad in SIMD, missing a simpler solution. Maybe it was a downgraded session, who knows. That SIMD instruction set, the one for the ESP32-P4, is not very popular and all the documentation it has is a couple of blog posts. So I'm pretty sure it has at most seen some code for a predecessor during its training. However, the LLM was able to derive a full listing of the operations and their arguments from gcc to get us started, and that's why I was able to come up with my own implementation. Along the way, it also came up with insights about possible gotchas. Then, when implementing algorithms, it has been able to reason things out and get things working, despite the ISA not being extremely well known.

They are easily verifiable and hill-climbable.

Because pre-LLMs humans partially "autogenerated" kernels through hyperparameter search and in some sense eating the code complexity in return for performance, and thus built tools for the same automatic verifiability that is useful for LLMs.

In some other tasks, we never built the same level of automatic verifiability since the level of automation in creation being much lower meant it's not giving you as much of a marginal benefit. We prefer code readability and simplicity and such in say, web services, because, say, the database IO time is going to dominate. Here getting an LLM to write a cromulent C# web service is more difficult since it's not easy to automatically verify whether code is cromulent or not. So if you put up LLMs to it, you end up with slop (which works).

OTOH, in kernel design, you give it access to every perf counter, every observable possible and have it optimise all of them. And all are verifiable/hill-climbable and you generally don't give a crap if the code is readable or reusable.