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.
MicheleSimionat o@libero.it
--- Currently looking for a job ---
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.
MicheleSimionat o@libero.it
--- Currently looking for a job ---
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