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Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
ciso8601
- GitHub - closeio/ciso8601: Fast ISO8601 date time parser for Python written in C
- ciso8601: Fast ISO8601 date time parser for Python written in C
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Python 3.11 is 25% faster than 3.10 on average
I see your point, but it directly conflicts with the effort many people put into producing extremely fast libraries for specific purposes, such as web frameworks (benchmarked extensively), ORMs and things like json and date parsing, as seen in the excellent ciso8601 [1] for example.
[1] https://github.com/closeio/ciso8601
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
What are some alternatives?
faster-cpython - How to make CPython faster.
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.
cosmopolitan - build-once run-anywhere c library
nogil - Multithreaded Python without the GIL
Pyjion - Pyjion - A JIT for Python based upon CoreCLR
mypyc - Compile type annotated Python to fast C extensions
pyenv-virtualenv - a pyenv plugin to manage virtualenv (a.k.a. python-virtualenv)
genomics_viz
jnumpy - Writing Python C extensions in Julia within 5 minutes.
sbcl - Mirror of Steel Bank Common Lisp (SBCL)'s official repository