nogil
python-feedstock
nogil | python-feedstock | |
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31 | 2 | |
2,853 | 44 | |
- | - | |
5.7 | 7.8 | |
2 months ago | 2 days ago | |
Python | Shell | |
GNU General Public License v3.0 or later | BSD 3-clause "New" or "Revised" License |
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nogil
- Proof-of-Concept Multithreaded Python Without the GIL
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Our Plan for Python 3.13
This might be a dumb question, but why would removing the GIL break FFI? Is it just that existing no-GIL implementations/proposals have discarded/ignored it, or is there a fundamental requirement, e.g. C programs unavoidably interact directly with the GIL? I know that the C-API is only stable between minor releases [0] compiled in the same manner [1], so it's not like the ecosystem is dependent upon it never changing.
I cannot seem to find much discussion about this. I have found a no-GIL interpreter that works with numpy, scikit, etc. [2][3] so it doesn't seem to be a hard limit. (That said, it was not stated if that particular no-GIL implementation requires specially built versions of C-API libs or if it's a drop-in replacement.)
[0]: https://docs.python.org/3/c-api/stable.html#c-api-stability
[1]: https://docs.python.org/3/c-api/stable.html#platform-conside...
[2]: https://github.com/colesbury/nogil
[3]: https://discuss.python.org/t/pep-703-making-the-global-inter...
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Real Multithreading Is Coming to Python
https://github.com/colesbury/nogil does manage to get rid of the GIL, but it's not certain to make it into Python core. The main problem is the amount of existing libraries that depend on the existence of the GIL without realizing it - breaking those would be extremely disruptive.
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[D] The hype around Mojo lang
CPython is also investigating the removal of the GIL (PEP703, nogil). I think requiring the GIL is a wider thing that libraries will need to address anyway. But also, for the same reason as above I'd be surprised if the Modular team thought that saying "you can run all your python code unchanged" was a good idea if there was a secret "except for code that uses numpy" muttered under the breath.
- PEP 684 was accepted – Per-interpreter GIL in Python 3.12
- PEP 703 – Making the Global Interpreter Lock Optional in CPython
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Python 3.11.0 final is now available
I'm worried about the speedup
My understanding is that it's based on the most recent attempt to remove the GIL by Sam Gross
https://github.com/colesbury/nogil
In addition to some ways to try to not have nogil have as much overhead he added a lot of unrelated speed improvements so that python without the gil would still be faster not slower in single thread mode. They seem to have merged those performance patches first that means if they add his Gil removal patches in say python 3.12 it will still be substantially slower then 3.11 although faster then 3.10. I hope that doesn't stop them from removing the gil (at least by default)
- Removed the GIL back in 1996 from Python 1.4, primarily to create a re-entrant Python interpreter.
- I Tried Removing Python's GIL Back in 1996
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Faster CPython 3.12 Plan
Looks like it's still active to me:
https://github.com/colesbury/nogil/
python-feedstock
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Python 3.11.0 final is now available
It's already there:
https://anaconda.org/conda-forge/python
https://github.com/conda-forge/python-feedstock/pull/577
Using mamba to create a new encoding called py311 with python 3.11:
mamba create -n py311 python=3.11
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Notes from the Meeting on Python GIL Removal Between Python Core and Sam Gross
https://news.ycombinator.com/item?id=18040664
Today, conda-forge compiles CPython to relocatable platform+architecture-specific binaries with LLVM. https://github.com/conda-forge/python-feedstock/blob/master/...
Pyodide (JupyterLite) compiles CPython to WASM (or LLVM IR?) with LLVM/emscripten IIRC. Hopefully there's a clear way to implement the new GIL-less multithreading support with Web Workers in WASM, too?
The https://rapids.ai/ org has a bunch a fast Python for HPC; with Dask and pick a scheduler. Less process overhead and less need for interprocess locking of memory handles that transgress contexts due to a new GIL removal approach would be even faster than debuggable one process per core Python.
What are some alternatives?
hpy - HPy: a better API for Python
import-linter - Import Linter allows you to define and enforce rules for the internal and external imports within your Python project.
mypyc - Compile type annotated Python to fast C extensions
django-stubs - PEP-484 stubs for Django
numpy - The fundamental package for scientific computing with Python.
celery-types - :seedling: Type stubs for Celery and its related packages
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
sbcl - Mirror of Steel Bank Common Lisp (SBCL)'s official repository
conda - A system-level, binary package and environment manager running on all major operating systems and platforms.
cosmopolitan - build-once run-anywhere c library
public-conventions - In-house conventions and styles