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Python 3.15's JIT is now back on track

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Re: Python 3.15's JIT is now back on track

#31

What is wrong with the Python code base that makes this so much harder to implement than seemingly all other code bases? Ruby, PHP, JS. They all seemed to add JITs in significantly less time. A Python JIT has been asked for for like 2 decades at this point.

Are you forgetting about PyPy, which has existed for almost 2 decades at this point?

Re: Python 3.15's JIT is now back on track

#32
post #20

What is wrong with the Python code base that makes this so much harder to implement than seemingly all other code bases? Ruby, PHP, JS. They all seemed to add JITs in significantly less time. A Python JIT has been asked for for like 2 decades at this point.

Some languages are much harder to compile well to machine code. Some big factors (for any languages) are things like: lack of static types and high "type uncertainty", other dynamic language features, established inefficient extension interfaces that have to be maintained, unusual threading models...

That makes sense if you're comparing with Java or C#, but not Ruby, which is way more dynamic than Python.

The more likely reason is that there simply hasn't been that big a push for it. Ruby was dog slow before the JIT and Rails was very popular, so there was a lot of demand and room for improvement. PHP was the primary language used by Facebook for a long time, and they had deep pockets. JS powers the web, so there's a huge incentive for companies like Google to make it faster. Python never really had that same level of investment, at least from a performance standpoint.

To your point, though, the C API has made certain types of optimizations extremely difficult, as the PyPy team has figured out.

Re: Python 3.15's JIT is now back on track

#33
post #15

Earlier quoted context omitted.

The names of the benchmark runners. https://doesjitgobrrr.com/about

So the biggest gains so far are on Windows 11 Pro of (x86_64) ~20%? Is that because Windows was bad as a baseline (promethius)? It doesn't seem like the x86_64/Linux has improved as dramatically ~5% (ripley). I'm just surprised OS has that much of an effect that can be attributed to JIT vs other OS issues.

It's hard to say whether it's Windows related since the two x86_64 machines don't just run different OSes, they also have different processors, from different manufacturers. I don't know whether an AMD Ryzen 5 3600X versus Intel i5-8400 have dramatically different features, but unlike a generic static binary for x86_64, a JIT could in principle exploit features specific to a given manufacturer.

Re: Python 3.15's JIT is now back on track

#35

What is wrong with the Python code base that makes this so much harder to implement than seemingly all other code bases? Ruby, PHP, JS. They all seemed to add JITs in significantly less time. A Python JIT has been asked for for like 2 decades at this point.

Are you forgetting about PyPy, which has existed for almost 2 decades at this point?

That's a completely separate codebase that purposefully breaks backwards compatibility in specific areas to achieve their goals. That's not the same as having a first-class JIT in CPython, the actual Python implementation that ~everyone uses.

Re: Python 3.15's JIT is now back on track

#36

What is wrong with the Python code base that makes this so much harder to implement than seemingly all other code bases? Ruby, PHP, JS. They all seemed to add JITs in significantly less time. A Python JIT has been asked for for like 2 decades at this point.

The Python C api leaks its guts. Too much of the internal representation was made available for extensions and now basically any change would be guaranteed to break backwards compatibility with something.

Re: Python 3.15's JIT is now back on track

#37
post #7

I'm been occasionally glancing at PR/issue tracker to keep up to date with things happening with the JIT, but I've never seen where the high level discussions were happening; the issues and PRs always jumped right to the gritty details. Is there anywhere a high-level introduction/example of how trace projection vs recording work and differ? Googling for the terms often returns CPython issue tracker as the first resul…

UPDATE: I misunderstood the question :-/ You can ignore this.

I love playing with compilers for fun, so maybe I can shed some light. I’ll explain it in a simplified way for everyone’s benefit (going to ignore the stack):

When an object is passed between functions in Python, it doesn’t get copied. Instead, a reference to the object’s memory address is sent. This reference acts as a pointer to the object’s data. Think of it like a sticky note with the object’s memory address written on it. Now, imagine throwing away one sticky note every time a function that used a reference returns.

When an object has zero references, it can be freed from memory and reused. Ensuring the number of references, or the “reference count” is always accurate is therefore a big deal. It is often the source of memory leaks, but I wouldn’t attribute it to a speed up (only if it replaces GC, then yes).

Re: Python 3.15's JIT is now back on track

#38
post #7

I'm been occasionally glancing at PR/issue tracker to keep up to date with things happening with the JIT, but I've never seen where the high level discussions were happening; the issues and PRs always jumped right to the gritty details. Is there anywhere a high-level introduction/example of how trace projection vs recording work and differ? Googling for the terms often returns CPython issue tracker as the first resul…

have you read the dev mailing list? There the developers of python discuss lots.

Re: Python 3.15's JIT is now back on track

#39

What is wrong with the Python code base that makes this so much harder to implement than seemingly all other code bases? Ruby, PHP, JS. They all seemed to add JITs in significantly less time. A Python JIT has been asked for for like 2 decades at this point.

Money.
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