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Re: object().__dict__

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2014-04-23 15:59 GMT+02:00 Phil Connell <pconnell@gmail.com>:

> On Wed, Apr 23, 2014 at 03:48:32PM +0200, Amirouche Boubekki wrote:
> > 2014-04-23 8:11 GMT+02:00 Cameron Simpson <cs@zip.com.au>:
> > > Look up the "__slots__" dunder var in the Python doco index:
> > >
> > >   https://docs.python.org/3/glossary.html#term-slots
> > >
> > > You'll see it as a (rarely used, mostly discouraged) way to force a
> fixed
> > > set of attributes onto a class. As with object, this brings a smaller
> > > memory footprint and faster attribute access, but the price is
> flexibility.
> > >
> >
> > True, still can be the only way to save few MB or... GB without falling
> > back to C or PyPy.
> >
> > Have a look at PyPy to how to save memory (and speed things up) without
> > slots:
> >
> http://morepypy.blogspot.fr/2010/11/efficiently-implementing-python-objects.html
>
> Is there any analysis of how this balances increased memory usage from the
> JIT
> vs the CPython VM (with a reasonable amount of code)?
>
> I'd thought that one of the main disadvantages of PyPy was drastically
> increased memory usage for any decent-sized program. Would be interested to
> know if this was not the case :)
>

I have a similar thought, I don't how that memory consumption increase (a
constant? a factor? probably both...)

but if the program use an absurd amount of memory even in CPython then PyPy
will be able to catchup based on comment #1 of a PyPy core dev in
http://tech.oyster.com/save-ram-with-python-slots/ see also
http://pypy.readthedocs.org/en/latest/interpreter-optimizations.html#dictionary-optimizations

Still it requires more analysis. When does PyPy trigger the optimization?


>
>
> Cheers,
> Phil
>
> --
> https://mail.python.org/mailman/listinfo/python-list
>

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Re: object().__dict__ Amirouche Boubekki <amirouche.boubekki@gmail.com> - 2014-04-23 16:21 +0200
  Re: object().__dict__ CHIN Dihedral <dihedral88888@gmail.com> - 2014-04-23 13:25 -0700

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