ideas VS Pyjion

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 :)

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....

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.

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.

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....

It exists, was created by microsoft employees, and is referenced in the article: https://www.trypyjion.com/

I've actually come across and started using Pyjion recently (https://github.com/tonybaloney/pyjion); how does Pypy compare, both in terms of performance and purpose? There seems to be a lot of overlap...

Python is capable of doing things at runtime that are really hard to statically compile around, such as monkeypatching methods onto existing objects. You can compile it, but it's complicated. One strategy is to use a JIT that can observe application state at runtime and then invalidate code as it becomes obsoleted by changes, but it's complicated. See pyjion for an example.

When I say "Python" I am referring to the standard CPython interpreter which most people use. But there is also PyPy, which includes a Just In Time compile that compiles selected code into machine language on the fly, as needed. pyjion is another JIT compiler that generates machine language on the fly, and you can install it with pip. Or you could work for Facebook and use Cinder. Cython, Nuitka and Pyston are other alternatives.

and if you need more speed you can try https://www.pypy.org/ or https://github.com/tonybaloney/Pyjion or https://www.pyston.org/

Finally, there is also Pyjion which based on its website is “A drop-in JIT Compiler for Python 3.10” (https://www.trypyjion.com/). We will be covering it on a separate writeup. See you next time ;-).

Though some exciting stuff happening over the next few years, Python is getting faster, has been for awhile, and stuff like Pyjion https://www.trypyjion.com/, a drop in C# powered JIT compiler is starting to approach usable. Rust and Python seem to be best buds right now, so more extension libraries in rust, a newer more approachable language than say C/C++ but with a similar speed. Sign me up!

Pyjion Easy to use, small compiler. Increase performance of our 🐌 CPython.