Path: csiph.com!fu-berlin.de!uni-berlin.de!not-for-mail
From: Mark Lawrence <breamoreboy@yahoo.co.uk>
Newsgroups: comp.lang.python
Subject: Re: Pyhon 2.x or 3.x, which is faster?
Date: Tue, 8 Mar 2016 03:48:50 +0000
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Message-ID: <mailman.34.1457409006.15725.python-list@python.org>
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On 08/03/2016 03:39, Terry Reedy wrote:
> On 3/7/2016 8:33 PM, BartC wrote:
>>> On Tue, Mar 8, 2016 at 12:00 PM, BartC <bc@freeuk.com> wrote:
>
>>>> def whiletest():
>>>> |   i=0
>>>> |   while i<=100000000:
>>>> |   |   i+=1
>>>>
>>>> whiletest()
>>>>
>>>> Python 2.7:  8.4 seconds
>>>> Python 3.1: 12.5 seconds
>>>> Python 3.4: 18.0 seconds
>>>>
>>>> Even if you don't care about speed, you must admit that there appears
>>>> to be
>>>> something peculiar going on here: why would 3.4 take more than twice
>>>> as long
>>>> as 2.7? What do they keep doing to 3.x to cripple it on each new
>>>> version?
>
>> Let me ask you a follow-on question first: how slow does a new Python
>> version have to be before even you would take notice?
>
> We now run a suite of benchmarks daily on latest versions of 2.7 and
> default (3.6) to compare not to each other but to previous days to
> detect changes within either branch.
>
> I verified your result with installed 64 bit 2.7.11 versus 3.5.1
>
> import time
> def test():
>      i=0
>      while i<=100000000:
>          i+=1
> start = time.time()
> test()
> print(time.time() - start)
>
> 4.4 (2.7) versus 10.7 (3.5)
>
> Running loop at top level instead of inside the function doubled the
> time.  Replacing globals dict lookup with function locals array lookup
> really helps.
>
> Next, I replaced the body of the function with
>      for i in range(100000000): pass
>
> This time, 3.5 wins: 3.8 versus 2.7.  Whoops, unfair. Change range to
> xrange in 2.7 and the time is 1.5.
>
> Neither version optimizes the do-nothing loop away.  A further version
> of CPython might.  There are people working on improving the speed of
> CPython.  Integer operations are not their focus, though, because that
> can be done in numpy.  Text operations have been getting work, and at
> least one person is actively working on an ast optimizer.
>

For reference all of the tips of the trade are given here 
https://wiki.python.org/moin/PythonSpeed/PerformanceTips

-- 
My fellow Pythonistas, ask not what our language can do for you, ask
what you can do for our language.

Mark Lawrence