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今天我跑了一些代碼，我想在我的多核cpu上運行它，所以即使我寫了map，我也將其更改爲pool.map。令人驚訝的是，即使它使用瞭如此之多的處理能力或內存（據我所知），我的代碼運行速度較慢。 所以我寫了這個測試，它使用了pathos和multiprocessing。python multiprocessing，pathos slow
from pathos.pools import ProcessPool
from pathos.pools import ThreadPool
#from pathos.pools import ParallelPool
from pathos.pools import SerialPool
from multiprocessing import Pool
import time
def timeit(method):
def timed(*args, **kw):
ts = time.time()
result = method(*args, **kw)
te = time.time()
print ('%r (%r, %r) %2.2f sec' % \
(method.__name__, args, kw, te-ts))
return result
return timed
def times2(x):
return 2*x
@timeit
def test(max,p):
(p.map(times2, range(max)))
def main():
ppool = ProcessPool(4)
tpool = ThreadPool(4)
#parapool = ParallelPool(4)
spool = SerialPool(4)
pool = Pool(4)
for i in range(8):
max = 10**i
print(max)
print('ThreadPool')
test(max,tpool)
#print('ParallelPool')
#test(max,parapool)
print('SerialPool')
test(max,spool)
print('Pool')
test(max,pool)
print('ProcessPool')
test(max,ppool)
print('===============')
if __name__ == '__main__':
main()
這些結果
1
ThreadPool
'test' ((1, <pool ThreadPool(nthreads=4)>), {}) 0.00 sec
SerialPool
'test' ((1, <pool SerialPool()>), {}) 0.00 sec
Pool
'test' ((1, <multiprocessing.pool.Pool object at 0x0000011E63D276A0>), {}) 0.17 sec
ProcessPool
'test' ((1, <pool ProcessPool(ncpus=4)>), {}) 0.00 sec
===============
10
ThreadPool
'test' ((10, <pool ThreadPool(nthreads=4)>), {}) 0.00 sec
SerialPool
'test' ((10, <pool SerialPool()>), {}) 0.00 sec
Pool
'test' ((10, <multiprocessing.pool.Pool object at 0x0000011E63D276A0>), {}) 0.00 sec
ProcessPool
'test' ((10, <pool ProcessPool(ncpus=4)>), {}) 0.01 sec
===============
100
ThreadPool
'test' ((100, <pool ThreadPool(nthreads=4)>), {}) 0.00 sec
SerialPool
'test' ((100, <pool SerialPool()>), {}) 0.00 sec
Pool
'test' ((100, <multiprocessing.pool.Pool object at 0x0000011E63D276A0>), {}) 0.00 sec
ProcessPool
'test' ((100, <pool ProcessPool(ncpus=4)>), {}) 0.01 sec
===============
1000
ThreadPool
'test' ((1000, <pool ThreadPool(nthreads=4)>), {}) 0.00 sec
SerialPool
'test' ((1000, <pool SerialPool()>), {}) 0.00 sec
Pool
'test' ((1000, <multiprocessing.pool.Pool object at 0x0000011E63D276A0>), {}) 0.00 sec
ProcessPool
'test' ((1000, <pool ProcessPool(ncpus=4)>), {}) 0.02 sec
===============
10000
ThreadPool
'test' ((10000, <pool ThreadPool(nthreads=4)>), {}) 0.00 sec
SerialPool
'test' ((10000, <pool SerialPool()>), {}) 0.00 sec
Pool
'test' ((10000, <multiprocessing.pool.Pool object at 0x0000011E63D276A0>), {}) 0.00 sec
ProcessPool
'test' ((10000, <pool ProcessPool(ncpus=4)>), {}) 0.09 sec
===============
100000
ThreadPool
'test' ((100000, <pool ThreadPool(nthreads=4)>), {}) 0.04 sec
SerialPool
'test' ((100000, <pool SerialPool()>), {}) 0.00 sec
Pool
'test' ((100000, <multiprocessing.pool.Pool object at 0x0000011E63D276A0>), {}) 0.01 sec
ProcessPool
'test' ((100000, <pool ProcessPool(ncpus=4)>), {}) 0.74 sec
===============
1000000
ThreadPool
'test' ((1000000, <pool ThreadPool(nthreads=4)>), {}) 0.42 sec
SerialPool
'test' ((1000000, <pool SerialPool()>), {}) 0.00 sec
Pool
'test' ((1000000, <multiprocessing.pool.Pool object at 0x0000011E63D276A0>), {}) 0.17 sec
ProcessPool
'test' ((1000000, <pool ProcessPool(ncpus=4)>), {}) 7.54 sec
===============
10000000
ThreadPool
'test' ((10000000, <pool ThreadPool(nthreads=4)>), {}) 4.57 sec
SerialPool
'test' ((10000000, <pool SerialPool()>), {}) 0.00 sec
Pool
'test' ((10000000, <multiprocessing.pool.Pool object at 0x0000011E63D276A0>), {}) 2.25 sec
ProcessPool
'test' ((10000000, <pool ProcessPool(ncpus=4)>), {}) 81.51 sec
===============
，你可以看到多處理經常勾引ProcessPool，比SerialPool更慢。 我運行i5-2500和我今天通過PIP
>pip freeze
colorama==0.3.9
decorator==4.1.2
dill==0.2.7.1
helper-htmlparse==0.1
htmldom==2.0
lxml==4.0.0
multiprocess==0.70.5
pathos==0.2.1
pox==0.2.3
ppft==1.6.4.7.1
py==1.4.34
pyfs==0.0.8
pyreadline==2.1
pytest==3.2.2
six==1.11.0
安裝悲愴，爲什麼會發生這種情況？
我確信的一件事是，你使用的線程越多，Python代碼所花費的時間就越多。最新的Python有一個更好的GIL版本，所以...在最新的python 3.x版本中可能會有一些性能增益，相比於舊版本， –
也是如此，python並沒有真正使用多線程。它使用單個線程，並在進程之間交換鎖 –