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Comment by bvrmn | Hacker News Reader

It's nice to have atomic guarantees of GILed implementation. For me current CPython semantic is a golden middle: you have pretty fast interpreter without race conditions (for huge part of user code). Due to GIL.

I always like to ask for what kind of task one needs GILless python?

CPU bound? If you are using native python code for CPU bound tasks you are already in a bad place. C extensions can release GIL. For example numpy.

What else?

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> I always like to ask for what kind of task one needs GILless python?

> CPU bound? If you are using native python code for CPU bound tasks you are already in a bad place.

While I think you may have misread my comment for a value judgement on Python's GIL, I don't see as particularly useful dismissing major potential (multithreaded) performance gains for a slow language just because there's faster languages. Languages that are better in some way - like speed - should stand as a benchmark and a goal, not a reason to give up improvements.

I'm not against faster interpreter also. It's history now, but IMHO super slow GILless python with Java-esk data races in 199x would not gain such broad community and ecosystem.

Anything with a hard or soft real-time constraint (audio, video, industrial automation, etc). You'd be shocked how many algorithmic prototypes are written in numpy then hand converted to C++ for realization.

NumPy releases GIL. I love NumPy myself and created complex interactive realtime synths. It always was more convenient to slap Cython or C code to achieve desired 100x performance instead of getting potentially 10x (4x on my laptop) with multithreading.

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