[IronPython] Performance of IronPython 2 Beta 4 and IronPython 1
Dino Viehland
dinov at microsoft.com
Fri Aug 15 20:09:23 CEST 2008
Ok, I looked into a bunch of these and here's what I've discovered so far and other random comments...
Exceptions (100000): 40% slower
IP1: 4703
IP2: 6125
Py: 266
I haven't looked at this one yet. I do know that we have a number of bug fixes for our exception handling which will slow it down though. I don't consider this to be a high priority though. If we wanted to focus on exception perf I think we'd want to do something radical rather than small tweaks to the existing code. If there's certain scenarios where exception perf is critical though it'd be interesting to hear about those and if we can do anything to improve them.
Engine execution: 8000% slower!!
IP1: 1600
IP2: 115002
This is just a silly bug. We're doing a tree re-write of the AST and we do that every time through. Caching that re-write gets us back to 1.x performance. I have a fix for this.
Create function: 25% slower
IP1: 2828
IP2: 3640
Py: 2766
Part of this is from a bug fix but the fix could be more efficient. In 1.x we don't look up __module__ from the global scope. In 2.x we do this lookup but it searches all scopes - which isn't even correct. But we can do a direct lookup which is a little faster - so I have a partial fix for this. This will still be a little slower than 1.x though.
Define oldstyle (1 000 000): 33% slower
IP1: 1781
IP2: 2671
Py: 2108
Is this critical? I'd rather just live w/ the slowness rather than fixing something that will be gone in 3.x :)
Lists (10 000): 50% slower
IP1: 10422
IP2: 16109
Py: 6094
The primary issue here is that adding 2 lists ends up creating a new list whose storage is the exact size needed for storing the two lists. When you append to it after adding it we need to allocate a brand new array - and you're not dealing with small arrays here. We can add a little extra space depending on the size of the array to minimize the chance of needing a re-size. That gets us to about 10% slower than CPython. I'm also going to add a strongly typed extend overload which should make those calls a little faster.
Sets2 (100 000): 500% slower
IP1: 4984
IP2: 30547
Py: 1203
This one I actually cannot repro yet (I've tried it on 3 machines but they've all been Vista). I'm going to try next on a Srv 2k3 machine and see if I can track it down. But more information would be useful.
Comparing (== and !=):
IP1: 278597
IP2: 117662
This one is actually pretty interesting (even though we're faster in 2.x) - there's an issue with the test here. You've defined "__neq__" instead of "__ne__". That causes the != comparison to ultimately compare based upon object identity - which is extremely slow. There might be some things we can do to make the object identity comparison faster (For example recognizing that we're doing equality and just need a eq or ne answer rather than a 1, -1, 0 comparison value). But I'm going to assume comparing on object identity isn't very important right now - let me know if I'm wrong. But switching this to __ne__ causes us to be a little faster than CPython. They have a great advantage on object identity comparisons - they can just use the objects address.
I was also curious what happens to this case if we use __slots__. That identified yet another massive performance regression which I have a fix for - creating instances that have __slots__ defined is horribly slow. With that bug fixed and using slots and __ne__ instead of __neq__ we can actually run this over 2x faster than CPython (on Vista x86 .NET 3.5SP1 on a 2.4ghz Core 2 w/ 4gb of RAM).
-----Original Message-----
From: users-bounces at lists.ironpython.com [mailto:users-bounces at lists.ironpython.com] On Behalf Of Michael Foord
Sent: Thursday, August 14, 2008 9:42 AM
To: Discussion of IronPython
Subject: Re: [IronPython] Performance of IronPython 2 Beta 4 and IronPython 1
Just for fun I also compared with CPython. The results are interesting, I'll turn it into a blog post of course...
Results in milliseconds with a granularity of about 15ms and so an accuracy of +/- ~60ms.
All testing with 10 000 000 operations unless otherwise stated.
The version of Python I compared against was Python 2.4.
Empty loop (overhead):
IP1: 422
IP2: 438
Py: 3578
Create instance newstyle:
IP1: 20360
IP2: 1109
Py: 4063
Create instance oldstyle:
IP1: 3766
IP2: 3359
Py: 4797
Function call:
IP1: 937
IP2: 906
Py: 3313
Create function: 25% slower
IP1: 2828
IP2: 3640
Py: 2766
Define newstyle (1 000 000):
IP1: 42047
IP2: 20484
Py: 23921
Define oldstyle (1 000 000): 33% slower
IP1: 1781
IP2: 2671
Py: 2108
Comparing (== and !=):
IP1: 278597
IP2: 117662
Py: 62423
Sets:
IP1: 37095
IP2: 30860
Py: 8047
Lists (10 000): 50% slower
IP1: 10422
IP2: 16109
Py: 6094
Recursion (10 000):
IP1: 1125
IP2: 1000
Py: 3609
Sets2 (100 000): 500% slower
IP1: 4984
IP2: 30547
Py: 1203
func_with_args:
IP1: 6312
IP2: 5906
Py: 11250
method_with_args:
IP1: 20594
IP2: 11813
Py: 14875
method_with_kwargs:
IP1: 27953
IP2: 11187
Py: 20032
import: 15% slower
IP1: 28469
IP2: 32000
Py: 25782
global: 20% slower
IP1: 1047
IP2: 1203
Py: 4141
Exceptions (100000): 40% slower
IP1: 4703
IP2: 6125
Py: 266
Engine execution: 8000% slower!!
IP1: 1600
IP2: 115002
Michael Foord wrote:
> Hello all,
>
> I've ported Resolver One to run on IronPython 2 Beta 4 to check for
> any potential problems (we will only do a *proper* port once IP 2 is
> out of beta).
>
> The basic porting was straightforward and several bugs have been fixed
> since IP 2 B3 - many thanks to the IronPython team.
>
> The good news is that Resolver One is only 30-50% slower than Resolver
> One on IronPython 1! (It was 300 - 400% slower on top of IP 2 B3.)
> Resolver One is fairly heavily optimised around the performance
> hotspots of IronPython 1, so we expect to have to do a fair bit of
> profiling and refactoring to readjust to the performance profile of IP 2.
>
> Having said that, there are a few oddities (and the areas that slow
> down vary tremendously depending on which spreadsheet we use to
> benchmark it - making it fairly difficult to track down the hotspots).
>
> We have one particular phase of spreadsheet calculation that takes
> 0.4seconds on IP1 and around 6 seconds on IP2, so I have been doing
> some micro-benchmarking to try and identify the hotspot. I've
> certainly found part of the problem.
>
> For those that are interested I've attached the very basic
> microbenchmarks I've been using. The nice thing is that in *general*
> IP2 does outperform IP1.
>
> The results that stand out in the other direction are:
>
> Using sets with custom classes (that define '__eq__', '__ne__' and
> '__hash__') seems to be 6 times slower in IronPython 2.
>
> Adding lists together is about 50% slower.
>
> Defining functions seems to be 25% slower and defining old style
> classes about 33% slower. (Creating instances of new style classes is
> massively faster though - thanks!)
>
> The code I used to test sets (sets2.py) is as follows:
>
> from System import DateTime
>
> class Thing(object):
> def __init__(self, val):
> self.val = val
> def __eq__(self, other):
> return self.val == other.val
>
> def __neq__(self):
> return not self.__eq__(other)
> def __hash__(self):
> return hash(self.val)
> def test(s):
> a = set()
> for i in xrange(100000):
> a.add(Thing(i))
> a.add(Thing(i+1))
> Thing(i) in a
> Thing(i+2) in a
> return (DateTime.Now -s).TotalMilliseconds
> s = DateTime.Now
> print test(s)
>
>
> Interestingly the time taken is exactly the same if I remove the
> definition of '__hash__'.
>
> The full set of results below:
>
> Results in milliseconds with a granularity of about 15ms and so an
> accuracy of +/- ~60ms.
> All testing with 10 000 000 operations unless otherwise stated.
>
> Empty loop (overhead):
> IP1: 421.9
> IP2: 438
> Create instance newstyle:
> IP1: 20360
> IP2: 1109
> Create instance oldstyle:
> IP1: 3766
> IP2: 3359
> Function call:
> IP1: 937
> IP2: 906
> Create function: 25% slower
> IP1: 2828
> IP2: 3640
> Define newstyle (1 000 000):
> IP1: 42047
> IP2: 20484
> Define oldstyle (1 000 000): 33% slower
> IP1: 1781
> IP2: 2671
>
> Comparing (== and !=):
> IP1: 278597
> IP2: 117662
> Sets (with numbers):
> IP1: 37095
> IP2: 30860
>
> Lists (10 000): 50% slower
> IP1: 10422
> IP2: 16109
>
> Recursion (10 000):
> IP1: 1125
> IP2: 1000
>
> Sets2 (100 000): 600% slower
> IP1: 4984
> IP2: 30547
>
>
> I'll be doing more as the 600% slow down for sets and the 50% slow
> down for lists accounts for some of the dependency analysis problem
> but not all of it.
>
> Many Thanks
>
> Michael Foord
> --
> http://www.resolversystems.com
> http://www.ironpythoninaction.com
>
>
>
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>
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