I think a much more sensible approach than mandating a limit because "who
knows, it might speed something up" would be finding the speedup first.

Probably that means one limit at a time too. E.g. maybe some patch imposes
the 1 million LOC limit and demonstrates a repeatable benchmark improvement
because of some coffee change that allows. That cold be interesting.

Even them, I wouldn't want some arbitrary round number just for its own
sake. For example, if taking 10 bits away from a word that holds a LOC
index speeds something up, make the LOC limit 4,194,304 (2**22)... Maybe.
If you only need 9 bits for that use, make the limit twice as much.

On Mon, Dec 9, 2019, 12:53 PM Mark Shannon <m...@hotpy.org> wrote:

>
> On 09/12/2019 2:15 pm, Chris Angelico wrote:
> > On Tue, Dec 10, 2019 at 1:09 AM Mark Shannon <m...@hotpy.org> wrote:
> >> Bear in mind that the costs of higher limits are paid by everyone, but
> >> the benefits are gained by few.
> >
> > Can we get some stats on what the costs of higher limits (or having no
> > limit at all) is? Or, putting it the other way: Since CPython
> > currently does not enforce limits, what are the benefits of placing
> > those limits? Merely having a limit doesn't in itself give a benefit
> > (and is a (minor) cost); it would help the discussion if we knew
> > exactly WHAT the costs of the higher limits were.
>
> Given there is an infinite number of potential optimizations that it
> would enable, it is a bit hard to put a number on it :)
>
> It is also impossible to put precise numbers on the speedups of a
> particular optimizations unless it is implemented. I suspect no one is
> going to do that unless paid to do so, or are guaranteed that the work
> won't be thrown away because the PEP is rejected.
>
> Cheers,
> Mark.
>
> >
> > ChrisA
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