Mark wrote, in reply to me:

> On 09/12/2019 3:01 pm, Paddy McCarthy wrote:
> > "Bear in mind that the costs of higher limits are paid by everyone, but
> > the benefits are gained by few."
> >
> > Is there some evidence for the above statement? One of the issues with C
> > programming is the need for limits Its hard to choose the right limits
> > leading to so many failures. You propose limiting Python for performance
> > gains but loose in ease of use and security.
>
> How does this make anything less secure?
>
Many basic exploits have components taking advantage of limits known as
buffer overflows

Suppose we can improve the interpreter in such a way that it takes
> advantage of the one million bytecode per code object and speeds it up
> by 0.1% (I can do a *lot* better than that).
> What would the performance gain save globally? $100k, $1M p.a.?
> What does it cost? A few developer hours.


You are looking to unproven execution gains. Adding an arbitrary limit
might make it 0.1% faster to get the *wrong *result .
Optimisation isn't just speedThe less mind-clutter spent on such* micro
optimisations* might leave one able to think of a better algorithm
or a better data structure that could give thousands of % speedups.
You should only quote C or C++ compiler limitations as a warning of what *not
*to do. Python wins by being different; by being more
orthoganal; by having *less *arbitrary restrictions.

Case in point:
http://paddy3118.blogspot.com/2019/11/quasi-random-sobol-sequence.html
Itook decade old C++ library, converted it
to straight python, Python allowed me to see the opportunity for
improvement leading to a Python version that is half the speed of
the C++ code and generates points on demand. That's how Python wins big
time. Having to worry about stifling limits is the wrong
direction for the language to take.

On Mon, 9 Dec 2019 at 21:26, David Mertz <me...@gnosis.cx> wrote:

> 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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