Some simple performace tests (long)

**Michael Hoffman** · Aug 6 '05, 02:05 PM

Re: Some simple performace tests (long)

TPJ wrote:
[color=blue]
> I decided to measure the performance of range and xrange...
>
> The results were as follows:
>
> n rprint xrprint
>
> 10^4 0.37 s 0.34 s <- (1)
> 10^5 4.26 s 4.25 s
> 10^6 42.57 s 42.57 s
> 10^7 431.94 s 438.32 s <- (2)
>
> n rpass xpass
>
> 10^4 0.0012 s 0.0011 s
> 10^5 0.0220 s 0.0139 s
> 10^6 0.1463 s 0.1298 s
> 10^7 1.4818 s 1.1807 s
>
> The values are the average times printed by tested functions.[/color]

The number of replicates and standard deviations would be useful in
analyzing these results.
--
Michael Hoffman

**Terry Reedy** · Aug 6 '05, 10:45 PM

Re: Some simple performace tests (long)

"TPJ" <tprimke@interi a.pl> wrote in message
news:1123334835 .548075.129430@ z14g2000cwz.goo glegroups.com.. .[color=blue]
> "The advantage of xrange() over range() is minimal (since xrange()
> still has to create the values when asked for them) except when a very
> large range is used on a memory-starved machine or when all of the
> range's elements are never used (such as when the loop is usually
> terminated with break)." - from Python Library Reference.[/color]

Nothing in your results, properly interpreted, contradicts this.
[color=blue]
> I decided to measure the performance of range and xrange.[/color]

On one machine, with one binary. Relative performance on different systems
can vary by, say, 10%. But of course, if you are optimizing a program to
run on just one machine, then the results on that machine are all that
matters.
[color=blue]
> I did it with the following functions:
>
> def rprint( n ):
> a = time.time()
> for i in range(n): print i
> print time.time() - a
>
> def xrprint( n ):
> a = time.time()
> for i in xrange(n): print i
> print time.time() - a[/color]

To compare two things, one wants to remove all possible noise factors. If
one wants relative performance (ratios, percentages) then constant factors
should also be removed, when possible, to make the apparent difference as
close to the real difference as possible. Print is a large constant +
large noise factor that you *DO NOT* want for the stated comparison. It
requires a call through lots of OS code with variable execution times.
[color=blue]
> def rpass( n ):
> a = time.time()
> for i in range(n): pass
> print time.time() - a
>
> def xrpass( n ):
> a = time.time()
> for i in xrange(n): pass
> print time.time() - a[/color]

This is the right comparison for the reasons noted.
[color=blue]
> The results were as follows:
>
> n rprint xrprint
>
> 10^4 0.37 s 0.34 s <- (1)
> 10^5 4.26 s 4.25 s
> 10^6 42.57 s 42.57 s
> 10^7 431.94 s 438.32 s <- (2)[/color]

These times with print are about 300x the results below without print.
They are useless for comparing range and xrange. The differences above are
differences in printing (display) times and not in range versus xrange.

Think about it. When the differences between runs with print are several
times as large as the total time without, then those large differences
cannot have anything to do with the minor difference in looping method.
[color=blue]
> n rpass xpass
>
> 10^4 0.0012 s 0.0011 s
> 10^5 0.0220 s 0.0139 s
> 10^6 0.1463 s 0.1298 s
> 10^7 1.4818 s 1.1807 s[/color]

The looks more coherent.
[color=blue]
> The values are the average times printed by tested functions.
>
> Conclusions:
>
> 1) According to (1) I could say that xrange might be somewhat faster
> than range with lower numbers of iterations.
> 2) According to (2) I could say that xrange might be slower than range
> with higher number of iterations.[/color]

You could, but you would be wrong. The second table shows that xrange is
slightly faster on your machine over the range tested.
[color=blue]
> The test with pass is not so important as the test with print (because
> we usually do something inside of loops).[/color]

This is completely backwards for the reasons already given. The point
about doing something inside loops in relevant in so far as it says the
that the minor difference between range and xrange, whichever way it goes
on a particular system, will matter relatively even less in application.
So the choice hardly matters unless space is a considerations. Which is
what the quoted docs more or less said.
[color=blue]
> import array, random, time[/color]

To reduce random noise from random, the generator should be reinitialized
with the seed(someint) at the beginning of each function. This is the
purpose of seed(x). But for the comparisons you are making, randomness is
irrelevance and the time for calls to random a nuisance.
[color=blue]
> def test1( n, size ):
> a = time.time()
> for i in xrange(n):
> l = []
> for i in xrange(size):
> l.append( random.randint( 1,10 ) )
> e = sum(l) / float(size) # just for taking some time[/color]

This is exactly what you DO NOT WANT TO DO for comparing anything else.
[color=blue]
> print time.time() - a[/color]
[color=blue]
> def test2( n, size ):
> a = time.time()
> l = []
> for i in xrange(n):
> del l[:]
> for i in xrange(size):
> l.append( random.randint( 1,10 ) )
> e = sum(l) / float(size)
> print time.time() - a[/color]

This is trivially different. To test 'l = []' versus 'del l[:]', test just
that.
[color=blue]
> def test3( n, size ):
> a = time.time()
> l = range(size)
> for i in xrange(n):
> for i in xrange(size):
> l[i] = random.randint( 1,10 )
> e = sum(l) / float(size)
> print time.time() - a[/color]

It is not surprising that allocating a list just once and filling it in is
faster than starting empty and expanding and copying multiple times. In
any case, if you want to test that, test just that without all the noise
and masking of other stuff.

I did not look at the slicing stuff.

Terry J. Reedy

**Justin Azoff** · Aug 7 '05, 12:05 AM

Re: Some simple performace tests (long)

How much ram does your machine have?
the main point is "except when a very large range is used on a
memory-starved machine"

run
x = range(10 ** 6)
and look at the memory usage of python..

what happens when you run this program:

import time

def t(func, num):
s = time.time()
for x in func(num):
pass
return time.time() - s

def run(func, num):
times = []
for x in range(5):
times.append(t( func,num))
return min(times), max(times), sum(times)/5

def main():
x = 10 ** 6
while 1:
print "trying", x
for s, f in ('xr', xrange), (' r', range):
print s + " %.3f %.3f %.3f" % run(f, x)
x *= 1.5
x = int(x)

if __name__ == "__main__":
main()

I get (columns are mix/max/average):

trying 1000000
xr 0.110 0.115 0.111
r 0.101 0.186 0.119
trying 1500000
xr 0.082 0.087 0.083
r 0.152 0.158 0.154
trying 2250000
xr 0.124 0.138 0.128
r 0.228 0.235 0.230
trying 3375000
xr 0.184 0.189 0.186
r 0.344 0.352 0.346
trying 5062500
xr 0.276 0.284 0.279
r 0.515 0.528 0.519
trying 7593750
xr 0.415 0.421 0.416
r 0.774 0.795 0.779
trying 11390625
xr 0.623 0.634 0.626
r 1.163 1.246 1.180
trying 17085937
xr 0.934 0.941 0.937
Killed

The "Killed" is from the linux OOM killing the python process.. notice
that the xrange for that number worked fine.

Some simple performace tests (long)

Some simple performace tests (long)

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