Amaury Forgeot d'Arc <amaur...@gmail.com> added the comment:
The multiprocessing module indeed has some overhead:
- the processes are spawned when needed. Before you perform performance
timings, you should "warm up" the Pool with a line like
pool.map(f, range(mul.cpu_count()))
(starting a process is a slowish operation specially on Windows)
This reduces timings by a factor of two.
- the dispatch overhead of multiprocessing is certainly greater than a
single multiplication. multiprocessing is for CPU-bound functions!
And do not forget that you have *tree* processes here: two from the
Pool, and your main program.
As Antoine said, try with this function instead:
def f(x):
for i in range(10):
x = x * x
return x
And the timings are much better...
----------
assignee: jnoller ->
nosy: +amaury.forgeotdarc
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Python tracker <rep...@bugs.python.org>
<http://bugs.python.org/issue5000>
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