It looks like 2-3 seconds of the 5-6 seconds slowdown in dependency
analysis is due to the way I resolved a weird cyclic import problem (one
that we didn't have in IP1 but due to the spaghetti like imports in that
part of our code I can't reproduce on its own with IP2). I moved an
import into the body of a function that is used heavily - moving that
and resolving the problem another way saves about 2-3 seconds.
In IronPython (1 & 2) reimporting a module that has already been
imported is much more expensive than in CPython.
That now accounts for almost all of the extra time in the dependency
analysis - now onto tracking down what looks like overhead in our main
execution loop. (Executing the spreadsheet code itself is no slower in
IP2 but the framework around it seems to be suffering.)
Michael
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
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http://www.resolversystems.com
http://www.ironpythoninaction.com
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