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On 18 October 2017 at 06:25, Guido van Rossum <guido@python.org> wrote:
That
means the general language level performance pay-offs for alternative
implementations come from working out how to make the abstraction layers
cheaper, as experience shows that opt-in ahead-of-time techniques like Cython, vectorisation,
and binary extension modules can do a much better job of dealing with the
clearly identifiable low level performance bottlenecks (Readers that aren't familiar with the concept may be interested in \[1\] as a good recent example of the effectiveness of the latter approach).
It takes courage to admit failures like this! I think this is a good call. It echoes the experiences with Unladen Swallow and Pyston.
And Armin Rigo's experience with psyco before that.
Despite what people may think, CPython really isn't slow, given the large set of constraints on the implementation.
Antonio Cuni had a good PyPy presentation at
EuroPython indirectly talking about the fact that when folks say "Python
is slow", what they often mean is "Many of Python's conceptual
abstractions come at a high runtime cost in the reference
implementation":
https://speakerdeck.com/antocuni/the-joy-of-pypy-jit-abstractions-for-free
Cheers,
Nick.