Velocity Reviews > "pow" (power) function

# "pow" (power) function

Russ
Guest
Posts: n/a

 03-15-2006
I have a couple of questions for the number crunchers out there:

Does "pow(x,2)" simply square x, or does it first compute logarithms
(as would be necessary if the exponent were not an integer)?

Does "x**0.5" use the same algorithm as "sqrt(x)", or does it use some
other (perhaps less efficient) algorithm based on logarithms?

Thanks,
Russ

sam
Guest
Posts: n/a

 03-16-2006
I not shure which algorithm,but I am assumeing that all Python does,is
to call the underlying C pow() function.

Sam

Paul Rubin
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Posts: n/a

 03-16-2006
Schüle Daniel <(E-Mail Removed)-karlsruhe.de> writes:
> >>> timeit.Timer("111**0.3").timeit()

> 2.3824679851531982
> >>> timeit.Timer("pow(111,0.3)").timeit()

> 4.2945041656494141
>
> interesting result
> seems that ** computates faster

Maybe "111**0.3" parses faster than pow(111,0.3), if timeit uses eval.
Also, pow() may incur more subroutine call overhead--better check
the bytecode for both versions.

Ben Cartwright
Guest
Posts: n/a

 03-16-2006
Russ wrote:
> I have a couple of questions for the number crunchers out there:

Sure, but the answers depend on the underlying Python implementation.
And if we're talking CPython, they also depend on the underlying C
implementation of libm (i.e., math.h).

> Does "pow(x,2)" simply square x, or does it first compute logarithms
> (as would be necessary if the exponent were not an integer)?

The former, using binary exponentiation (quite fast), assuming x is an
int or long.

If x is a float, Python coerces the 2 to 2.0, and CPython's float_pow()
function is called. This function calls libm's pow(), which in turn
uses logarithms.

> Does "x**0.5" use the same algorithm as "sqrt(x)", or does it use some
> other (perhaps less efficient) algorithm based on logarithms?

The latter, and that algorithm is libm's pow(). Except for a few
special cases that Python handles, all floating point exponentation is
left to libm. Checking to see if the exponent is 0.5 is not one of
those special cases.

Objects/floatobject.c, and check out float_pow(). The binary
exponentation algorithms are in Objects/intobject:int_pow() and
Objects/longobject:long_pow().

The 0.5 special check (and any other special case optimizations) could,
in theory, be performed in the platform's libm. I'm not familiar
enough with any libm implementations to comment on whether this is ever
done, or if it's even worth doing... though I suspect that the 0.5 case
is not.

Hope that helps,
--Ben

=?ISO-8859-1?Q?Sch=FCle_Daniel?=
Guest
Posts: n/a

 03-16-2006
Russ wrote:
> I have a couple of questions for the number crunchers out there:
>
> Does "pow(x,2)" simply square x, or does it first compute logarithms
> (as would be necessary if the exponent were not an integer)?
>
> Does "x**0.5" use the same algorithm as "sqrt(x)", or does it use some
> other (perhaps less efficient) algorithm based on logarithms?

you can try and timeit

>>> 111**111

10736201288847422580121456504669550195985072399422 48048047759111756250761957833470224912261700936346 21466103743092986967777786330067310159463303558666 91009102601778558729553962214205731543706973022937 5357546494103400699864397711L
>>> timeit.Timer("pow(111,111)").timeit()

40.888447046279907
>>> timeit.Timer("111**111").timeit()

39.732122898101807
>>> timeit.Timer("111**0.5").timeit()

2.0990891456604004
>>> timeit.Timer("pow(111,0.5)").timeit()

4.1776390075683594
>>> timeit.Timer("111**0.3").timeit()

2.3824679851531982
>>> timeit.Timer("pow(111,0.3)").timeit()

4.2945041656494141

interesting result
seems that ** computates faster

Russ
Guest
Posts: n/a

 03-16-2006
Ben Cartwright wrote:
> Russ wrote:

> > Does "pow(x,2)" simply square x, or does it first compute logarithms
> > (as would be necessary if the exponent were not an integer)?

>
>
> The former, using binary exponentiation (quite fast), assuming x is an
> int or long.
>
> If x is a float, Python coerces the 2 to 2.0, and CPython's float_pow()
> function is called. This function calls libm's pow(), which in turn
> uses logarithms.

I just did a little time test (which I should have done *before* my
original post!), and 2.0**2 seems to be about twice as fast as
pow(2.0,2). That seems consistent with your claim above.

I'm a bit surprised that pow() would use logarithms even if the
exponent is an integer. I suppose that just checking for an integer
exponent could blow away the gain that would be achieved by avoiding
logarithms. On the other hand, I would think that using logarithms
could introduce a tiny error (e.g., pow(2.0,2) = 3.9999999996 <- made
up result) that wouldn't occur with multiplication.

>
> > Does "x**0.5" use the same algorithm as "sqrt(x)", or does it use some
> > other (perhaps less efficient) algorithm based on logarithms?

>
> The latter, and that algorithm is libm's pow(). Except for a few
> special cases that Python handles, all floating point exponentation is
> left to libm. Checking to see if the exponent is 0.5 is not one of
> those special cases.

I just did another little time test comparing 2.0**0.5 with sqrt(2.0).
Surprisingly, 2.0**0.5 seems to take around a third less time.

None of these differences are really significant unless one is doing
super-heavy-duty number crunching, of course, but I was just curious.
Thanks for the information.

Ben Cartwright
Guest
Posts: n/a

 03-16-2006
Russ wrote:
> Ben Cartwright wrote:
> > Russ wrote:

>
> > > Does "pow(x,2)" simply square x, or does it first compute logarithms
> > > (as would be necessary if the exponent were not an integer)?

> >
> >
> > The former, using binary exponentiation (quite fast), assuming x is an
> > int or long.
> >
> > If x is a float, Python coerces the 2 to 2.0, and CPython's float_pow()
> > function is called. This function calls libm's pow(), which in turn
> > uses logarithms.

>
> I just did a little time test (which I should have done *before* my
> original post!), and 2.0**2 seems to be about twice as fast as
> pow(2.0,2). That seems consistent with your claim above.

Actually, the fact that x**y is faster than pow(x, y) has nothing do to
with the int vs. float issue. It's actually due to do the way Python
parses operators versus builtin functions. Paul Rubin hit the nail on
the head when he suggested you check the bytecode:

>>> import dis
>>> dis.dis(lambda x, y: x**y)

6 BINARY_POWER
7 RETURN_VALUE
>>> dis.dis(lambda x, y: pow(x,y))

9 CALL_FUNCTION 2
12 RETURN_VALUE

especially when you're doing it a million times (which, coincidentally,
timeit does).

Anyway, if you want to see the int vs. float issue in action, try this:

>>> from timeit import Timer
>>> Timer('2**2').timeit()

0.12681011582321844
>>> Timer('2.0**2.0').timeit()

0.33336011743438121
>>> Timer('2.0**2').timeit()

0.36681835556112219
>>> Timer('2**2.0').timeit()

0.37949818370600497

As you can see, the int version is much faster than the float version.
The last two cases, which also use the float version, have an
additional performance hit due to type coercion. The relative speed
differences are similar when using pow():

>>> Timer('pow(2, 2)').timeit()

0.33000968869157532
>>> Timer('pow(2.0, 2.0)').timeit()

0.50356362184709269
>>> Timer('pow(2.0, 2)').timeit()

0.55112938185857274
>>> Timer('pow(2, 2.0)').timeit()

0.55198819605811877

> I'm a bit surprised that pow() would use logarithms even if the
> exponent is an integer. I suppose that just checking for an integer
> exponent could blow away the gain that would be achieved by avoiding
> logarithms. On the other hand, I would think that using logarithms
> could introduce a tiny error (e.g., pow(2.0,2) = 3.9999999996 <- made
> up result) that wouldn't occur with multiplication.

These are good questions to ask an expert in floating point arithmetic.
Which I'm not.

> > > Does "x**0.5" use the same algorithm as "sqrt(x)", or does it use some
> > > other (perhaps less efficient) algorithm based on logarithms?

> >
> > The latter, and that algorithm is libm's pow(). Except for a few
> > special cases that Python handles, all floating point exponentation is
> > left to libm. Checking to see if the exponent is 0.5 is not one of
> > those special cases.

>
> I just did another little time test comparing 2.0**0.5 with sqrt(2.0).
> Surprisingly, 2.0**0.5 seems to take around a third less time.

Again, this is because of the operator vs. function lookup issue.
pow(2.0, 0.5) vs. sqrt(2.0) is a better comparison:

>>> from timeit import Timer
>>> Timer('pow(2.0, 0.5)').timeit()

0.51701437102815362
>>> Timer('sqrt(2.0)', 'from math import sqrt').timeit()

0.46649096722239847

> None of these differences are really significant unless one is doing
> super-heavy-duty number crunching, of course, but I was just curious.
> Thanks for the information.

Welcome.

--Ben

Mike Ressler
Guest
Posts: n/a

 03-16-2006
On Wed, 2006-03-15 at 18:46 -0800, Ben Cartwright wrote:

> Anyway, if you want to see the int vs. float issue in action, try this:
>
> >>> from timeit import Timer
> >>> Timer('2**2').timeit()

> 0.12681011582321844
> >>> Timer('2.0**2.0').timeit()

> 0.33336011743438121
> >>> Timer('2.0**2').timeit()

> 0.36681835556112219
> >>> Timer('2**2.0').timeit()

> 0.37949818370600497
>
> As you can see, the int version is much faster than the float version.

I have a counterexample. In the original timeit example, 111**111 was
used. When I run that

>>> timeit.Timer("pow(111,111)").timeit()

10.968398094177246
>>> timeit.Timer("111**111").timeit()

10.04007887840271
>>> timeit.Timer("111.**111.").timeit()

0.36576294898986816

The pow and ** on integers take 10 seconds, but the float ** takes only
0.36 seconds. (The pow with floats takes ~ 0.7 seconds). Clearly
typecasting to floats is coming in here somewhere. (Python 2.4.1 on
Linux FC4.)

Mike

Dennis Lee Bieber
Guest
Posts: n/a

 03-16-2006
On Thu, 16 Mar 2006 09:23:57 -0800, Mike Ressler
<(E-Mail Removed)> declaimed the following in comp.lang.python:

>
> >>> timeit.Timer("pow(111,111)").timeit()

> 10.968398094177246
> >>> timeit.Timer("111**111").timeit()

> 10.04007887840271
> >>> timeit.Timer("111.**111.").timeit()

> 0.36576294898986816
>
> The pow and ** on integers take 10 seconds, but the float ** takes only
> 0.36 seconds. (The pow with floats takes ~ 0.7 seconds). Clearly

The integer only case is performing long-int computations, which are
algorithms... The float case is using hardware floating point

PythonWin 2.3.5 (#62, Feb 9 2005, 16:17:0 [MSC v.1200 32 bit (Intel)]
on win32.
Portions Copyright 1994-2001 Mark Hammond ((E-Mail Removed)) -
>>> 111**111

10736201288847422580121456504669550195985072399422 48048047759111756250761957833470224912261700936346 21466103743092986967777786330067310159463303558666 91009102601778558729553962214205731543706973022937 5357546494103400699864397711L
>>> 111.**111

1.0736201288847422e+227
>>>

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> ================================================== ============ <
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Ben Cartwright
Guest
Posts: n/a

 03-17-2006
Mike Ressler wrote:
> >>> timeit.Timer("pow(111,111)").timeit()

> 10.968398094177246
> >>> timeit.Timer("111**111").timeit()

> 10.04007887840271
> >>> timeit.Timer("111.**111.").timeit()

> 0.36576294898986816
>
> The pow and ** on integers take 10 seconds, but the float ** takes only
> 0.36 seconds. (The pow with floats takes ~ 0.7 seconds). Clearly
> typecasting to floats is coming in here somewhere. (Python 2.4.1 on
> Linux FC4.)

No, there is not floating point math going on when the operands to **
are both int or long. If there were, the following two commands would
have identical output:

>>> 111**111

10736201288847422580121456504669550195985072399422 4804804775911
17562507619578334702249122617009363462146610374309 2986967777786
33006731015946330355866691009102601778558729553962 2142057315437
069730229375357546494103400699864397711L
>>> int(111.0**111.0)

10736201288847422472001804610489313089074203814505 4486592605938
34891423167097288759427928321358541274379933928055 2157756096410
83975202085309998368049933481542266918440896141131 9810030383904
886446681757296875373689157536249282560L

The first result is accurate. Work it out by hand if you don't believe
me. The second suffers from inaccuracies due to floating point's
limited precision.

Of course, getting exact results with huge numbers isn't cheap,
computationally. Because there's no type in C to represent arbitrarily
huge numbers, Python implements its own, called "long". There's a fair
amount of memory allocation, bit shifting, and other monkey business
going on behind the scenes in longobject.c.

Whenever possible, Python uses C's built-in signed long int type (known
simply as "int" on the Python side, and implemented in intobject.c).
On my platform, C's signed long int is 32 bits, so values range from
-2147483648 to 2147483647. I.e., -(2**31) to (2**31)-1.

As long as your exponentiation result is in this range, Python uses
int_pow(). When it overflows, long_pow() takes over. Both functions
use the binary exponentiation algorithm, but long_pow() is naturally
slower:

>>> from timeit import Timer
>>> Timer('2**28').timeit()

0.24572032043829495
>>> Timer('2**29').timeit()

0.25511642791934719
>>> Timer('2**30').timeit()

0.27746782979170348
>>> Timer('2**31').timeit() # overflow: 2**31 > 2147483647

2.8205724462504804
>>> Timer('2**32').timeit()

2.2251812151589547
>>> Timer('2**33').timeit()

2.4067177773399635

Floating point is a whole 'nother ball game:

>>> Timer('2.0**30.0').timeit()

0.33266301963840306
>>> Timer('2.0**31.0').timeit() # no threshold here!

0.33437446769630697

--Ben