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ideas
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Type information for faster Python C extensions
Lower latency native calls in Python would be extremely useful, thank you for your work! Is the following GitHub issue the right place to monitor progress? https://github.com/faster-cpython/ideas/issues/546
I'm open to doing some benchmarking. Several of my libraries have pure CPython bindings (StringZilla, UCall, SimSIMD), and all perform low-latency SIMD-accelerated ops, so might be a good testing ground :)
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How Many Lines of C It Takes to Execute a and B in Python?
Recent CPython development has been towards optimizations and addressing use cases that benefit from optimizations, some coming from the faster CPython initiative. You might just get your JIT[1].
[1] https://github.com/faster-cpython/ideas/wiki/Workflow-for-3....
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GIL removal and the Faster CPython project
The faster-cpython folks seem to be working towards a JIT (https://github.com/faster-cpython/ideas/tree/main/3.13) and both pyston and cinder have JITs. So I don't think anyone has ruled one out.
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Our Plan for Python 3.13
faster-cpython team has done a lot of work to experiment on it: https://github.com/faster-cpython/ideas/issues/485#issuecomm...
It kind of sounds like migration to register based is a foregone conclusion, but it's not very clear to me.
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Faster CPython at PyCon, part two
lots of big ideas are still remaining to be done. One example is the register based interpreter, see https://github.com/faster-cpython/ideas/issues/485
A previous plan called for the beginning of a JIT in 3.12, seen as "Trace optimized interpreter" here: https://github.com/faster-cpython/ideas/wiki/Workflow-for-3....
- EdgeDB – A graph-relational database built on top of Postgres
- Python 3.12 Nogil Benchmark
hpy
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RustPython
There is a merge request up to add autogen rust bindings to hpy
https://github.com/hpyproject/hpy/pull/457
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Ruby 3.2’s YJIT is Production-Ready
Are you referencing https://github.com/hpyproject/hpy?
I do hope it takes off.
- HPy - A better C API for Python
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Codon: A high-performance Python compiler
The HPy project [0] seems like a promising way out of this.
[0] https://hpyproject.org/
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New record breaking for Python in TechEmPower
socketify.py breaks the record for Python no other Python WebFramework/Server as able to reach 6.2 mi requests per second before in TechEmPower Benchmarks, this puts Python at the same level of performance that Golang, Rust and C++ for web development, in fact Golang got 5.2 mi req/s in this same round. Almost every server or web framework tries to use JIT to boost the performance, but only socketify.py deliveries this level of performance, and even without JIT socketify.py is twice as fast any other web framework/server in active development, and still can be much more optimized using HPy (https://hpyproject.org/). Python will get even faster and faster in future!
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Is it time to leave Python behind? (My personal rant)
I think Propose a better messaging for Python is the option and a lot of languages will learn it from Rust, because rust erros are the best described errors I see in my life lol. Cargo is amazing and I think we will need a better poetry/pip for sure, HPy project will modernize extensions and packages 📦 too https://hpyproject.org/
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A Look on Python Web Performance at the end of 2022
It also show that PyPy3 will not magically boost your performance, you need to integrate in a manner that PyPy3 can optimize and delivery CPU performance, with a more complex example maybe it can help more. But why socketify is so much faster using PyPy3? The answer is CFFI, socketify did not use Cython for integration and cannot delivery the full performance on Python3, this will be solved with HPy.
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socketify.py - Bringing WebSockets, Http/Https High Peformance servers for PyPy3 and Python3
HPy integration to better support CPython, PyPy and GraalPython
- HPy: A better C API for Python
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Your Data Fits in RAM
Absolutely everything in CPython is a PyObject, and that can’t be changed without breaking the C API. A PyObject contains (among other things) a type pointer, a reference count, and a data field; none of these things can be changed without (again) breaking the C API.
There have definitely been attempts to modernize; the HPy project (https://hpyproject.org/), for instance, moves towards a handle-oriented API that keeps implementation details private and thus enables certain optimizations.
What are some alternatives?
Nuitka - Nuitka is a Python compiler written in Python. It's fully compatible with Python 2.6, 2.7, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 3.10, and 3.11. You feed it your Python app, it does a lot of clever things, and spits out an executable or extension module.
nogil - Multithreaded Python without the GIL
faster-cpython - How to make CPython faster.
graalpython - A Python 3 implementation built on GraalVM
Pyjion - Pyjion - A JIT for Python based upon CoreCLR
py2js
pyenv-virtualenv - a pyenv plugin to manage virtualenv (a.k.a. python-virtualenv)
cinder - Cinder is Meta's internal performance-oriented production version of CPython.
jnumpy - Writing Python C extensions in Julia within 5 minutes.
pgcopy - fast data loading with binary copy