Scaling NumPy on Free-Threaded Python

This is well-written. I could follow along quite nicely, from the setup through the bottlenecks and onto the resolution of the performance bug. Even the PRs are very pleasant to read: the majority of them is just a handful of changed lines with an added tests and a bit of documentation.

I was taken aback for a moment that this work originated from a report on StackOverflow. I had thought SO was effectively dead and abandoned by its community. But maybe I shouldn't project my own experience onto everyone else.

I thought NumPy was already releasing the GIL. On regular non-free-threaded Python, you can run threaded parallel Numpy operations and have multiple cores doing 100%, I've relied on that. Maybe not the case with the operations this article focuses on (sin/cos).

> only acquire the lock when the flag needs to be updated

Unclear why you still need the lock here in that case. The idea that this flag may get updated during runtime and impacts how the software works when set seems to clash with the idea we need take no action having performed a relaxed (ie non-synchronising) load and seen it wasn't set at some previous time.

Maybe there's something I don't understand about these internals, which may be as simple as "It's just advisory so if we don't trace when we should no big deal".

Nice to see the performance improvements work.

I know this is more or less expected, but the improvement induced by adding a worker diminishes very rapidly... I guess it's not the cpython/numpy's fault but rather the CPU.

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