Issue 5000: multiprocessing - Pool.map() slower about 5 times than map() on 2 cores machine (original) (raw)
Created on 2009-01-19 13:50 by 0x666, last changed 2022-04-11 14:56 by admin. This issue is now closed.
Messages (6)
Author: 0x666 (0x666)
Date: 2009-01-19 13:50
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).
Author: Antoine Pitrou (pitrou) * 
Date: 2009-01-19 15:08
You should try with something less trivial than your "f" function. For such a short function, it seems expectable that the dispatch overhead will dominate the actual computation time.
Author: Amaury Forgeot d'Arc (amaury.forgeotdarc) *
Date: 2009-01-19 15:28
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...
Author: Jesse Noller (jnoller) *
Date: 2009-01-19 15:32
My results don't match yours. (8 cores, Mac OS/X):
-------- testing multiprocessing on 8 cores ----------
100000 elements map() time 0.0444118976593 s 100000 elements pool.map() time 0.0366489887238 s 100000 elements pool.apply_async() time 24.3125801086 s
Now, this could be for a variety of reasons: More cores, different OS (which means different speed at which processes can be forked) and so on. As Antoine/Amaury point out you really need a use case that is large enough to offset the cost of forking the processes in the first place.
I also ran this on an 8 core Ubuntu box with kernel 2.6.22.19 and py2.6.1 and 16gb of ram:
-------- testing multiprocessing on 8 cores ----------
100000 elements map() time 0.0258889198303 s 100000 elements pool.map() time 0.0339770317078 s 100000 elements pool.apply_async() time 11.0373139381 s
OS/X is pretty snappy when it comes for forking.
Now, if you cut the example you provided over to Amaury's example, you see a significant difference:
OS/X, 8 cores:
-------- testing multiprocessing on 8 cores ----------
100000 elements map() time 30.704061985 s 100000 elements pool.map() time 4.95880293846 s 100000 elements pool.apply_async() time 23.6090102196 s
Ubuntu, kernel 2.6.22.19 and py2.6.1:
-------- testing multiprocessing on 8 cores ----------
100000 elements map() time 38.3818569183 s 100000 elements pool.map() time 5.65878105164 s 100000 elements pool.apply_async() time 14.1757941246 s
Author: Jesse Noller (jnoller) *
Date: 2009-01-19 15:33
Closing as not an issue.
Author: 0x666 (0x666)
Date: 2009-01-19 19:17
Thanks for quick response and for informative answers, especially thanks to Antoine Pitrou/Amaury Forgeot.
p.s. By seting bigger range - range(15) and elements = 1000, I was able to get speed-up factor up to 1.8. Wow, I`m amazed :-) Good library, keep it up. BTW, about warp-up,- forking first processes. User can forget to "warpup" pool. It would be cool if pool somehow warm-ups itself in initialization phase (in step mul.Pool(processes=mul.cpu_count())). Maybe you can define another initialization parameter=function ? (if not None, first processes should be spawned, not waiting map function) or something like that. But it is only cosmetics :-)
History
Date
User
Action
Args
2022-04-11 14:56:44
admin
set
github: 49250
2009-01-19 19:17:10
0x666
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messages: +
2009-01-19 15:34:52
benjamin.peterson
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status: open -> closed
2009-01-19 15:33:20
jnoller
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resolution: not a bug
messages: +
2009-01-19 15:32:54
jnoller
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messages: +
2009-01-19 15:28:19
amaury.forgeotdarc
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assignee: jnoller ->
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nosy: + amaury.forgeotdarc
2009-01-19 15:19:04
jnoller
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assignee: jnoller
nosy: + jnoller
2009-01-19 15:08:13
pitrou
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nosy: + pitrou
messages: +
2009-01-19 13:50:51
0x666
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