|Thus Spake Jacek Generowicz On the now historical date of Thu, 08 Jan

2004 11:43:01 +0100|

> Samuel Walters <(E-Mail Removed)> writes:

>

>> If you'd like to see an example of both the psyco and profile modules

>> in action, let me know and I'll give you some more understandable code

>> that I once wrote to see what types of things psyco is good at

>> optimizing.

>

> I think this is generally interesting, and would be curious to see it,

> if you'd care to share.
Sure thing. The functions at the top are naive prime enumeration

algorithms. I chose them because they're each of a general "looping"

nature and I understand the complexity and methods of each of them. Some

use lists (and hence linearly indexed) methods and some use dictionary(

and hence are has bound). One of them, sieve_list is commented out because

it has such dog performance that I decided I wasn't interested in

how well it optimized.

These tests are by no means complete, nor is this probably a good example

of profiling or the manner in which psyco is useful. It's just from an

area where I understood the algorithmic bottlenecks to begin with.

Sam Walters.

--

Never forget the halloween documents.

http://www.opensource.org/halloween/
""" Where will Microsoft try to drag you today?

Do you really want to go there?"""

from math import sqrt

def primes_list(Limits = 1,KnownPrimes = [ 2 ]):

RetList = KnownPrimes

for y in xrange(2,Limits + 1):

w = y

p, r = 0,0

for x in RetList:

if x*x > w:

RetList.append(w)

break

p,r = divmod(y,x)

if r == 0:

w = p

return RetList

def primes_dict(Limits = 1,KnownPrimes = [ 2 ]):

RetList = KnownPrimes

RetDict = {}

for x in KnownPrimes:

RetDict[x] = 1

w = x + x

n = 2

while w <= Limits + 1:

RetDict[w] = n

w += x

n += 1

p, r = 0,0

for y in xrange(2, Limits + 1):

for x, z in RetDict.iteritems():

if x*x > y:

RetDict[y] = 1

break

p,r = divmod(y,x)

if r == 0:

RetDict[y] = p

break

return RetList

def sieve_list(Limits = 1, KnownPrimes = [ 2 ]):

RetList = KnownPrimes

CompList = [ ]

for y in xrange(2, Limits + 1):

if y not in CompList:

w = y

n = 1

while w <= Limits:

CompList.append(w)

w += y

n += 1

return RetList

def sieve_list_2(Limits = 1, KnownPrimes = [ 2 ]):

SieveList = [ 1 ]*(Limits )

RetList = [ ]

for y in xrange(2, Limits + 1):

if SieveList[y-2] == 1:

RetList.append(y)

w = y + y

n = 2

while w <= Limits + 1:

SieveList[w - 2] = n

w += y

n += 1

return RetList

def sieve_dict(Limits = 1, KnownPrimes = [ 2 ]):

SieveDict = { }

RetList = KnownPrimes

for x in KnownPrimes:

SieveDict[x] = 1

w = x + x

n = 2

while w <= Limits + 1:

SieveDict[w] = n

n += 1

w += x

for y in xrange(2, Limits + 1):

if not SieveDict.has_key(y):

RetList.append(y)

w = y

n = 1

while w <= Limits + 1:

SieveDict[w] = n

w += y

n += 1

return RetList

if __name__ == "__main__":

import sys

import profile

import pstats

import psyco

#this function wraps up all the calls that we wish to benchmark.

def multipass(number, args):

for x in xrange(1, number + 1):

primes_list(args, [ 2 ])

print ".",

sys.stdout.flush()

primes_dict(args, [ 2 ])

print ".",

sys.stdout.flush()

#Do not uncomment this line unless you have a *very* long time to wait.

#sieve_list(args)

sieve_dict(args, [ 2 ])

print ".",

sys.stdout.flush()

sieve_list_2(args, [ 2 ])

print "\r \r%i/%i"%(x, number),

sys.stdout.flush()

print "\n"

#number of times through the test

passes = 5

#find all primes up to maximum

maximum = 1000000

#create a profiling instance

#adjust the argument based on your system.

pr = profile.Profile( bias = 7.5e-06)

#run the tests

pr.run("multipass(%i, %i)"%(passes,maximum))

#save them to a file.

pr.dump_stats("primesprof")

#remove the profiling instance so that we can get a clean comparison.

del pr

#create a profiling instance

#adjust the argument based on your system.

pr = profile.Profile( bias = 7.5e-06)

#"recompile" each of the functions under consideration.

psyco.bind(primes_list)

psyco.bind(primes_dict)

psyco.bind(sieve_list)

psyco.bind(sieve_list_2)

psyco.bind(sieve_dict)

#run the tests

pr.run("multipass(%i, %i)"%(passes,maximum))

#save them to a file

pr.dump_stats("psycoprimesprof")

#clean up our mess

del pr

#load and display each of the run-statistics.

pstats.Stats('primesprof').strip_dirs().sort_stats ('cum').print_stats()

pstats.Stats('psycoprimesprof').strip_dirs().sort_ stats('cum').print_stats()