I posted this few weeks ago (remember the C Sharp thread?) but it went
unnoticed on the large mass of posts, so let me retry. Here I get Python+
Psyco twice as fast as optimized C, so I would like to now if something
is wrong on my old laptop and if anybody can reproduce my results.
Here are I my numbers for calling the error function a million times
(Python 2.3, Psyco 1.0, Red Hat Linux 7.3, Pentium II 366 MHz):
$ time p23 erf.py
real 0m0.614s
user 0m0.551s
sys 0m0.029s
This is twice as fast as optimized C:
$ gcc erf.c -lm -o3
$ time ./a.out
real 0m1.125s
user 0m1.086s
sys 0m0.006s
Here is the situation for pure Python
$time p23 erf.jy
real 0m25.761s
user 0m25.012s
sys 0m0.049s
and, just for fun, here is Jython performance:
$ time jython erf.jy
real 0m42.979s
user 0m41.430s
sys 0m0.361s
The source code follows (copied from Alex Martelli's post):
----------------------------------------------------------------------
$ cat erf.py
import math
import psyco
psyco.full()
def erfc(x):
exp = math.exp
p = 0.3275911
a1 = 0.254829592
a2 = -0.284496736
a3 = 1.421413741
a4 = -1.453152027
a5 = 1.061405429
t = 1.0 / (1.0 + p*x)
erfcx = ( (a1 + (a2 + (a3 +
(a4 + a5*t)*t)*t)*t)* t ) * exp(-x*x)
return erfcx
def main():
erg = 0.0
for i in xrange(1000000) :
erg += erfc(0.456)
if __name__ == '__main__':
main()
--------------------------------------------------------------------------
# python/jython version = same without "import psyco; psyco.full()"
--------------------------------------------------------------------------
$cat erf.c
#include <stdio.h>
#include <math.h>
double erfc( double x )
{
double p, a1, a2, a3, a4, a5;
double t, erfcx;
p = 0.3275911;
a1 = 0.254829592;
a2 = -0.284496736;
a3 = 1.421413741;
a4 = -1.453152027;
a5 = 1.061405429;
t = 1.0 / (1.0 + p*x);
erfcx = ( (a1 + (a2 + (a3 +
(a4 + a5*t)*t)*t)*t)* t ) * exp(-x*x);
return erfcx;
}
int main()
{
double erg=0.0;
int i;
for(i=0; i<1000000; i++)
{
erg = erg + erfc(0.456);
}
return 0;
}
Michele Simionato, Ph. D. Mi************* *@libero.it http://www.phyast.pitt.edu/~micheles
--- Currently looking for a job ---
Jul 18 '05
18 4914
On Sun, 24 Aug 2003 00:31:15 +0100, John J. Lee wrote: Irmen de Jong <irmen@-NOSPAM-REMOVETHIS-xs4all.nl> writes:
P@draigBrady.co m wrote:
.... but Python+Psyco still wins :-)
So, the interesting part is: why?
John
My suspicion is that when psyco looks at erfc, it
finds that nothing changes and so replaces the
function call with the resulting number (am i right? it's the
same each time?). This is what a "specializi ng compiler"
would do, me thinks. So, try using a different number
with each call.
Simon.
Lawrence Oluyede wrote: P@draigBrady.co m wrote:
If you want to try different machines then http://www.pixelbeat.org/scripts/gcccpuopt will give you the appropriate machine specific gcc options to use.
Very cool script, thanks :) Anyway it didn't change so much with erf.c erfCPU is compiled with the flags suggested by gcccpuopt script:
$ gcccpuopt -march=athlon-xp -mfpmath=sse -msse -mmmx -m3dnow
You still need some -O optimization flags. The -m options just let gcc
generate some nice instructions specific to your Athlon CPU.
Also, I don't think that script is all that useful because at least some
(if not all) of those -m options are already implied by -march=athlon-xp
(I don't recall which ones off the top of my head but I'll find a
reference for anyone interested... you can also find out by looking at
the gcc command line option parsing code).
Anyone who wants some other good ideas for the best flags on their
machine check out ccbench: http://www.rocklinux.net/packages/ccbench.html
The problem here of course is that not all applications behave like the
benchmarks :(
Van Gale
Van Gale wrote: You still need some -O optimization flags. The -m options just let gcc generate some nice instructions specific to your Athlon CPU.
I didn't mention but I also used -O3 flag. I don't know why but on my
machine C code is faster than psyco code in this test
--
Lawrence "Rhymes" Oluyede http://loluyede.blogspot.com rh****@NOSPAMmy self.com
Van Gale <ne**@exultants .org> wrote in message news:<XK******* **********@news svr27.news.prod igy.com>... Michele Simionato wrote: I posted this few weeks ago (remember the C Sharp thread?) but it went unnoticed on the large mass of posts, so let me retry. Here I get Python+ Psyco twice as fast as optimized C, so I would like to now if something is wrong on my old laptop and if anybody can reproduce my results. Here are I my numbers for calling the error function a million times (Python 2.3, Psyco 1.0, Red Hat Linux 7.3, Pentium II 366 MHz):
$ gcc erf.c -lm -o3
Did you really use "-o3" instead of "-O3"? The lowercase -o3 will produce object code file named "3" instead of doing optimization.
Yes, I used -O3, this was a misprint in the e-email. The compiler was
gcc 2.96.
Michele Simionato, Ph. D. Mi************* *@libero.it http://www.phyast.pitt.edu/~micheles
--- Currently looking for a job ---
I finally came to the conclusion that the exceeding good performance
of Psyco was due to the fact that the function was called a million
times with the *same* argument. Evidently Psyco is smart enough to
notice that. Changing the argument at each call
(erfc(0.456) -> i/1000000.0) slows down Python+Psyco at 1/4 of C speed.
Psyco improves Python performance by an order of magnitude, but still it
is not enough :-(
I was too optimistic!
Here I my numbers for Python 2.3, Psyco 1.0, Red Hat Linux 7.3,
Pentium II 366 MHz:
$ time p23 erf.py
real 0m3.245s
user 0m3.164s
sys 0m0.037s
This is more than four times slower than optimized C:
$ gcc erf.c -lm -O3
$ time ./a.out
real 0m0.742s
user 0m0.725s
sys 0m0.002s
Here is the situation for pure Python
$time p23 erf.jy
real 0m27.470s
user 0m27.162s
sys 0m0.023s
and, just for fun, here is Jython performance:
$ time jython erf.jy
real 0m44.395s
user 0m42.602s
sys 0m0.389s
----------------------------------------------------------------------
$ cat erf.py
import math
import psyco
psyco.full()
def erfc(x):
exp = math.exp
p = 0.3275911
a1 = 0.254829592
a2 = -0.284496736
a3 = 1.421413741
a4 = -1.453152027
a5 = 1.061405429
t = 1.0 / (1.0 + p*x)
erfcx = ( (a1 + (a2 + (a3 +
(a4 + a5*t)*t)*t)*t)* t ) * exp(-x*x)
return erfcx
def main():
erg = 0.0
for i in xrange(1000000) :
erg += erfc(i/1000000.0)
if __name__ == '__main__':
main()
--------------------------------------------------------------------------
# python/jython version = same without "import psyco; psyco.full()"
--------------------------------------------------------------------------
$cat erf.c
#include <stdio.h>
#include <math.h>
double erfc( double x )
{
double p, a1, a2, a3, a4, a5;
double t, erfcx;
p = 0.3275911;
a1 = 0.254829592;
a2 = -0.284496736;
a3 = 1.421413741;
a4 = -1.453152027;
a5 = 1.061405429;
t = 1.0 / (1.0 + p*x);
erfcx = ( (a1 + (a2 + (a3 +
(a4 + a5*t)*t)*t)*t)* t ) * exp(-x*x);
return erfcx;
}
int main()
{
double erg=0.0;
int i;
for(i=0; i<1000000; i++)
{
erg = erg + erfc(i/1000000.0);
}
return 0;
}
Michele Simionato, Ph. D. Mi************* *@libero.it http://www.phyast.pitt.edu/~micheles/
---- Currently looking for a job ----
Michele Simionato wrote: I finally came to the conclusion that the exceeding good performance of Psyco was due to the fact that the function was called a million times with the *same* argument. Evidently Psyco is smart enough to notice that. Changing the argument at each call (erfc(0.456) -> i/1000000.0) slows down Python+Psyco at 1/4 of C speed. Psyco improves Python performance by an order of magnitude, but still it is not enough :-(
This is not suprising. Last I checked, Psyco does not fully compile
floating point expressions. If, I rememeber correctly (though every time
try to delve too deeply into Psyco my brains start oozing out my ears),
there are three ways a in which a given chunk of code evaluated. At one
level, which I'll call #1, Psyco generates the machine code(*) for the
expression. At a second level, Psyco calls out to C helper functions,
but still works with unboxed values. At the third level, Psyco punts and
creates a Python object and hands things off to the interpreter.
Most integer functions operate at level #1, so they tend to be quite
fast. Most floating point operations operate at level #2, so they have a
certain amount of overhead, but are still much faster than unpsyco
(sane?) Python. I believe the reason for this is that x86 floating point
operations are very messy, so Armin punted...
(*) Armin is working on virtual machine implementation of Psyco, so it
should be available on non x86 machines soon.
FWIW,
-tim
I was too optimistic!
Here I my numbers for Python 2.3, Psyco 1.0, Red Hat Linux 7.3, Pentium II 366 MHz:
$ time p23 erf.py real 0m3.245s user 0m3.164s sys 0m0.037s
This is more than four times slower than optimized C:
$ gcc erf.c -lm -O3 $ time ./a.out real 0m0.742s user 0m0.725s sys 0m0.002s
Here is the situation for pure Python
$time p23 erf.jy real 0m27.470s user 0m27.162s sys 0m0.023s
and, just for fun, here is Jython performance:
$ time jython erf.jy real 0m44.395s user 0m42.602s sys 0m0.389s
----------------------------------------------------------------------
$ cat erf.py import math import psyco psyco.full()
def erfc(x): exp = math.exp
p = 0.3275911 a1 = 0.254829592 a2 = -0.284496736 a3 = 1.421413741 a4 = -1.453152027 a5 = 1.061405429
t = 1.0 / (1.0 + p*x) erfcx = ( (a1 + (a2 + (a3 + (a4 + a5*t)*t)*t)*t)* t ) * exp(-x*x) return erfcx
def main(): erg = 0.0
for i in xrange(1000000) : erg += erfc(i/1000000.0)
if __name__ == '__main__': main()
--------------------------------------------------------------------------
# python/jython version = same without "import psyco; psyco.full()"
--------------------------------------------------------------------------
$cat erf.c #include <stdio.h> #include <math.h>
double erfc( double x ) { double p, a1, a2, a3, a4, a5; double t, erfcx;
p = 0.3275911; a1 = 0.254829592; a2 = -0.284496736; a3 = 1.421413741; a4 = -1.453152027; a5 = 1.061405429;
t = 1.0 / (1.0 + p*x); erfcx = ( (a1 + (a2 + (a3 + (a4 + a5*t)*t)*t)*t)* t ) * exp(-x*x);
return erfcx; }
int main() { double erg=0.0; int i;
for(i=0; i<1000000; i++) { erg = erg + erfc(i/1000000.0); }
return 0; }
Michele Simionato, Ph. D. Mi************* *@libero.it http://www.phyast.pitt.edu/~micheles/ ---- Currently looking for a job ---- mi**@pitt.edu (Michele Simionato) wrote in message
news:<22******* *************** ****@posting.go ogle.com>... I finally came to the conclusion that the exceeding good performance of Psyco was due to the fact that the function was called a million times with the *same* argument. Evidently Psyco is smart enough to notice that. Changing the argument at each call (erfc(0.456) -> i/1000000.0) slows down Python+Psyco at 1/4 of C speed. Psyco improves Python performance by an order of magnitude, but still it is not enough :-(
It's plenty! A factor of 4 from optimized C, considering the newness
and limited resources behind psyco, is very encouraging, and good
enough for most tasks. Java JIT compilers are still around a factor
of 2 slower than C, and they've had at least 2 orders of magnitude
more whumpage.
This is a far cry from the factor of 10-30 I've been seeing with pure
python. For performance-critical code, this could be the difference
between hand-coding 5% versus 20% of your code.
Excellent news!! da*******@yahoo .com (dan) writes: mi**@pitt.edu (Michele Simionato) wrote in message news:<22******* *************** ****@posting.go ogle.com>...
[...] This is a far cry from the factor of 10-30 I've been seeing with pure python. For performance-critical code, this could be the difference between hand-coding 5% versus 20% of your code.
Excellent news!!
If you care about this a lot, don't forget Pyrex.
John
right, pyrex -- looked at that a while ago. Compiled Python with
C-style type declarations, right? Kinda like common lisp??? (I'm
stretching my memory cells now)
will review jj*@pobox.com (John J. Lee) wrote in message news:<87******* *****@pobox.com >... da*******@yahoo .com (dan) writes:
mi**@pitt.edu (Michele Simionato) wrote in message news:<22******* *************** ****@posting.go ogle.com>... [...] This is a far cry from the factor of 10-30 I've been seeing with pure python. For performance-critical code, this could be the difference between hand-coding 5% versus 20% of your code.
Excellent news!!
If you care about this a lot, don't forget Pyrex.
John This thread has been closed and replies have been disabled. Please start a new discussion. Similar topics |
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I posted this few weeks ago (remember the C Sharp thread?) but it went
unnoticed on the large mass of posts, so let me retry. Here I get Python+
Psyco twice as fast as optimized C, so I would like to now if something
is wrong on my old laptop and if anybody can reproduce my results.
Here are I my numbers for calling the error function a million times
(Python 2.3, Psyco 1.0, Red Hat Linux 7.3, Pentium II 366 MHz):
$ time p23 erf.py
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