Steven,
Sorry about taking a few days to get back to you. Here is the exact code I
ran:
def test():
n = 0
def inner():
global g
nonlocal n
n = g
x = n
g = x
y = x
z = y
return x
for i in range(100):
a = inner()
from timeit import Timer
g = 0
t = Timer("test()", setup="from __main__ import test; g=0")
res = t.repeat(repeat=20)
from statistics import mean, stdev
print(f"ALl: {res}")
print(f"mean {mean(res)}, std {stdev(res)}")
For my branch the results were:
ALl: [8.909581271989737, 8.897892987995874, 9.055693186994176,
9.103679533989634, 9.0795843389933, 9.12056165598915, 9.125767157005612,
9.117257817997597, 9.113885553990258, 9.180963805003557, 9.239156291994732,
9.318854127981467, 9.296847557998262, 9.313092978001805, 9.284125670004869,
9.259817042999202, 9.244616173004033, 9.271513198997127, 9.335984965000534,
9.258596728992416]
mean 9.176373602246167, std 0.12835175852148933
The results for mainline python 3.7 are:
ALl: [9.005807315988932, 9.081591005000519, 9.12138073798269,
9.174804927984951, 9.233709035004722, 9.267144601995824, 9.323436667007627,
9.314979821007, 9.265707976999693, 9.24289796501398, 9.236994076985866,
9.310381392017007, 9.206289929017657, 9.211337374988943, 9.206687778991181,
9.215082932991209, 9.221178130013868, 9.213595701992745, 9.206646608014125,
9.224334346014075]
mean 9.214199416250631, std 0.07610134120369169
There is some variation in the mean and std for each of these each time the
program is run, which I suspect is due to scheduling on which core the job
is launched. These results are typical however. I can launch each job a
number of times to create meta runtime distributions, but I felt that was
better left to proper bench-marking programs. It seems like these numbers
are within statistical uncertainty of each other.
Nate
On Thu, Jun 27, 2019 at 3:11 PM Steven D'Aprano <st...@pearwood.info> wrote:
> On Thu, Jun 27, 2019 at 12:23:24PM -0400, nate lust wrote:
>
> > If you have a
> > bench mark you prefer I would be happy to run it against my changes and
> > mainline python 3.7 to see how they compare.
>
> Ultimately it will probably need to run against this:
>
> https://github.com/python/pyperformance
>
> for 3.8 and 3.9 alpha but we can start with some quick and dirty tests
> using timeit. Let's say:
>
>
> def test():
> n = 0
> def inner():
> global g
> nonlocal n
> n = g
> x = n
> g = x
> y = x
> z = y
> return x
> for i in range(100):
> a = inner()
>
> from timeit import Timer
> t = Timer("test()", setup="from __main__ import test; g=0")
> t.repeat(repeat=5)
>
>
> I'm not speaking officially, but I would say that if this slows down
> regular assignments by more than 5%, the idea is dead in the water; if
> it slows it down by less than 1%, the performance objection is
> satisfied; between 1 and 5 means we get to argue cost versus benefit.
>
> (The above is just my opinion. Others may disagree.)
>
>
> --
> Steven
> _______________________________________________
> Python-ideas mailing list -- python-ideas@python.org
> To unsubscribe send an email to python-ideas-le...@python.org
> https://mail.python.org/mailman3/lists/python-ideas.python.org/
> Message archived at
> https://mail.python.org/archives/list/python-ideas@python.org/message/SJBOCUI22E36XSIDQBD5GYLATBJRVLIX/
> Code of Conduct: http://python.org/psf/codeofconduct/
>
--
Nate Lust, PhD.
Astrophysics Dept.
Princeton University
_______________________________________________
Python-ideas mailing list -- python-ideas@python.org
To unsubscribe send an email to python-ideas-le...@python.org
https://mail.python.org/mailman3/lists/python-ideas.python.org/
Message archived at
https://mail.python.org/archives/list/python-ideas@python.org/message/TWZ4VIE7XGI5SPHKMEAHRHVKNS2QM3ME/
Code of Conduct: http://python.org/psf/codeofconduct/
