New submission from 0x666 <vasiliauskas.agn...@gmail.com>:
I think something wrong with implementation of multiprocessing module.
I`ve run this very simple test on my machine (core 2, vista):
import multiprocessing as mul
from time import time
def f(x):
return x*x
if __name__ == '__main__':
print "-------- testing multiprocessing on ",mul.cpu_count(),"cores
----------"
print ""
elements = 100000
pool = mul.Pool(processes=mul.cpu_count())
t1 = time()
res_par = pool.map(f, range(elements))
t2 = time()
res_seq = map(f, range(elements))
t3 = time()
res_app = [pool.apply_async(f,(x,)) for x in range(elements)]
res_app = [result.get() for result in res_app]
t4 = time()
print len(res_seq),"elements","map() time",(t3-t2),"s"
print len(res_par),"elements","pool.map() time",(t2-t1),"s"
print len(res_app),"elements","pool.apply_async() time", (t4-t3),"s"
print
raw_input("press enter to exit...")
__________________________________________
Results:
-------- testing multiprocessing on 2 cores -----------
100000 elements map() time 0.0269 s
100000 elements pool.map() time 0.108 s
100000 elements pool.apply_async() time 10.567 s
--------------------------------------------------------
IMHO, execution on 2 cores should be 1.x - 2 times faster than compared
with non-parallel execution. (at least in simple cases).
If you dont believe in this, check http://www.parallelpython.com/
module (demo example sum_primes.py), which fits very well this idea.
So how it can be that parallel pool.map() method executes in about 5
times SLOWER, than ordinary map() function ?
So please correct multiprocessing package to work in more-less
perfomance predictable way (like parallelpython).
----------
components: Library (Lib)
messages: 80168
nosy: 0x666
severity: normal
status: open
title: multiprocessing - Pool.map() slower about 5 times than map() on 2 cores
machine
type: performance
versions: Python 2.6
_______________________________________
Python tracker <rep...@bugs.python.org>
<http://bugs.python.org/issue5000>
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