Performance penalty for using classes?
Miki_Tebeka at amat.com
Miki_Tebeka at amat.com
Sun Jan 12 04:58:41 EST 2003
Hello Tim,
10x. I'll give it a try.
(BTW: How much time did the other tests took on your machine?)
Bye.
-----------------------------------------------------------------------------
Smile, damn it, smile.
lambda msg: {
'name' : 'Miki Tebeka',
'email' : 'miki_tebeka at amat.com',
'url' : 'http://www.cs.bgu.ac.il/~tebeka',
'quote' : 'The only difference between children and adults is the
price of the toys'
}[msg]
---------------------------------------------------------------------------------------------------------------------------
To: Miki Tebeka <tebeka at cs.bgu.ac.il>
cc: python-list at python.org
Tim Churches Subject: Re: Performance penalty for using classes?
<tchur at optushome.com.au>
01/13/03 08:33 AM
On Sat, 2003-01-11 at 22:04, Miki Tebeka wrote:
> Hello All,
>
> I need my M.Sc. to run fast but I don't want to recode it in C.
> I've noticed that using classes can cause my code to run about 30%
> slower.
> (See the below tests)
>
> Since I don't need OO much in this project is it worth moving to a
> non-OO programming style?
>
> Miki
----snip-----
Why not use Psyco (see psyco.sf.net):
#----class_test.py--------
#!/usr/bin/env python
import random, time
class Dot:
def __init__(self, x, y):
self._x = x
self._y = y
def x(self):
return self._x
def set_x(self, x):
self._x = x
def y(self):
return self._y
def set_y(self, y):
self._y = y
def test_class(iterations):
while iterations > 0:
d = Dot(random.random(), random.random())
x, y = d.x(), d.y()
d.set_x(y)
d.set_y(x)
iterations -= 1
if __name__ == '__main__':
from sys import argv, stdout
if len(argv) > 1:
iterations = long(argv[1])
else:
iterations = 100000
print 'Performing %d iterations without Psyco' % iterations
stdout.flush()
start = time.time()
test_class(iterations)
end = time.time()
print '* Done'
print "That took %.3f seconds" % (end - start)
print
import psyco
psyco.bind(Dot)
psyco.bind(test_class)
print 'Performing %d iterations with Psyco' % iterations
stdout.flush()
start = time.time()
test_class(iterations)
end = time.time()
print '* Done'
print "That took %.3f seconds" % (end - start)
#----class_test.py--------
produces this output:
$ python psyco-test.py
Performing 100000 iterations without Psyco
* Done
That took 1.985 seconds
Performing 100000 iterations with Psyco
* Done
That took 1.198 seconds
$
That's a lot easier than rewriting all your code...
Tim C
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