It's unfortunate that a technically exciting new feature is being killed partly because of what is essentially bureaucracy (and the lack of the developer's personal bandwidth). On the other hand, it's pretty safe to say that the strict adherence to the PEP policy and all of the bureaucracy that comes with it is what makes Python a great language, for better or worse.
Not to say that the technical objections were without merit, they likely very much weren't, and the debugger and instrumentation support and maintenance work sounds fairly painful on its own.
There are already many way of addressing Python performance (leaving aside whether Python is a good choice if performance matters) such as Cython, Numba, Pypy and C extensions.
Python is a mature production language with a lot of users. Even before it was huge - like 20 years ago when I was in college - getting a small backwards compatible API change into a python extension module (gnu readline in my case) required convincing GvR personally that the API change was necessary.
Every JIT attempt I've seen for python just tries to cut too deep in the first pass, and scope themselves too large.
I believe any successful attempt at optimizing python performance from JIT will FIRST start with a 100% semantically compatible hidden type model for optimizing object property access and method dispatch.
The single biggest performance delta in most dynamic language jits come from that, not from optimizing guards off of bytecode operations or from compiling to machine code. It's from converting hashtable lookups into constant-time guard + offset lookups.
The hidden type model is also a hard nut to crack. It needs to perserve dict semantics which have very dynamic behaviour via the various `__*__` methods, and handle exotics scope objects since scoped name lookups are the most common ones which have their own exotic behaviours (I remember python providing some ability for callee frames to inspect caller scopes in some limited ways).
Interesting, makes sense. JIT in CPython main is a big deal, glad they’re proceeding with caution. Overall looks like they added it as an experimental feature for two years, then decided to back it out. Perfectly reasonable outcome.
It's unfortunate that a technically exciting new feature is being killed partly because of what is essentially bureaucracy (and the lack of the developer's personal bandwidth). On the other hand, it's pretty safe to say that the strict adherence to the PEP policy and all of the bureaucracy that comes with it is what makes Python a great language, for better or worse.
Not to say that the technical objections were without merit, they likely very much weren't, and the debugger and instrumentation support and maintenance work sounds fairly painful on its own.
If you look at the reasons for this it seems like a good decision: https://discuss.python.org/t/pep-836-jit-go-brrr-the-path-to...
There are already many way of addressing Python performance (leaving aside whether Python is a good choice if performance matters) such as Cython, Numba, Pypy and C extensions.
Gonna dump a technical hot take here.
Python is a mature production language with a lot of users. Even before it was huge - like 20 years ago when I was in college - getting a small backwards compatible API change into a python extension module (gnu readline in my case) required convincing GvR personally that the API change was necessary.
Every JIT attempt I've seen for python just tries to cut too deep in the first pass, and scope themselves too large.
I believe any successful attempt at optimizing python performance from JIT will FIRST start with a 100% semantically compatible hidden type model for optimizing object property access and method dispatch.
The single biggest performance delta in most dynamic language jits come from that, not from optimizing guards off of bytecode operations or from compiling to machine code. It's from converting hashtable lookups into constant-time guard + offset lookups.
The hidden type model is also a hard nut to crack. It needs to perserve dict semantics which have very dynamic behaviour via the various `__*__` methods, and handle exotics scope objects since scoped name lookups are the most common ones which have their own exotic behaviours (I remember python providing some ability for callee frames to inspect caller scopes in some limited ways).
Interesting, makes sense. JIT in CPython main is a big deal, glad they’re proceeding with caution. Overall looks like they added it as an experimental feature for two years, then decided to back it out. Perfectly reasonable outcome.