I know of a couple reasons offhand.

JavaScript is JIT’ed where CPython is not. Pypy has JIT and is faster, but I think is incompatible with C extensions.

I think Pythons threading model also adds complexity to optimizing where JavaScripts single thread is easier to optimize.

I would also say there’s generally less impetus to optimize CPython. At least until WASM, JavaScript was sort of stuck with the performance the interpreter had. Python had more off-ramps. You could use pypy for more pure Python stuff, or offload computationally heavy stuff to a C extension.

I think there are some language differences that make JavaScript easier to optimize, but I’m not super qualified to speak on that.

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pansa2 15 hours ago | parent [-]

> I would also say there’s generally less impetus to optimize CPython

Nonetheless, Microsoft employed a whole "Faster CPython" team for 4 years - they targeted a 5x speedup but could only achieve ~1.5x. Why couldn't they make a significantly faster Python implementation, especially given that PyPy exists and proves it's possible?

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everforward 13 hours ago | parent [-]

Pypy has much slower C interop than CPython, which I believe is part of the tradeoff. Eg data analysis pipelines are probably still faster in numpy on CPython than pypy.

Not an expert here, but my understanding is that Python is dynamic to the point that optimizing is hard. Like allowing one namespace to modify another; last I used it, the Stackdriver logging adapter for Python would overwrite the stdlib logging library. You import stackdriver, and it changes logging to send logs to stackdriver.

All package level names (functions and variables) are effectively global, mutable variables.

I suspect a dramatically faster Python would involve disabling some of the more unhinged mutability. Eg package functions and variables cannot be mutated, only wrapped into a new variable.

	▲	pjmlp 3 hours ago \| parent [-]
		See Smalltalk, Self and Common Lisp, and you will find languages that are even more dynamic than Python, and are in the genesis of high performance JIT research.