PyPy deserves much more credit (and much wider use) than it gets. The underperformance of the Faster CPython project [0] shows how difficult it is to optimize a Python implementation, and highlights just how impressive PyPy really is.
[0] The article says "Python has gotten nearly 50% faster in less than four years", but the original goal was a 5x speedup in the same timeframe [https://github.com/markshannon/faster-cpython/blob/master/pl...].
> The article says "Python has gotten nearly 50% faster in less than four years", but the original goal was a 5x speedup in the same timeframe
IIRC they originally expected the JIT to be the single focus on CPython performance improvement. But then another front was opened to tackle the GIL in parallel[1]. Perhaps the overhead of two major "surgeries" in the CPython codebase at the same time contributed to slower progress than originally predicted.
[1] https://peps.python.org/pep-0703/
The main culprit is not wanting to change the C ABI of the VM.
Other equally dynamic languages have long shown the way.
But what do people actually use Python for the most, at least as far as industry is concerned? Interfacing with those C extensions.
PyPy does have an alternative ABI that integrates with the JIT and also works on CPython, so if people cared that much about those remaining bits of performance, they could support it.
That is the sad part of it all.
The culture that sees writing C as Python, and for them to care, Microsoft and Facebook had to step in.
Now with Microsoft out of the loop, lets see how much support the whole CPython JIT project will keep getting.