今天我运行了一些代码，我想在我的多核CPU上运行它，所以即使我写了map，我也将其更改为pool.map。 令人惊讶的是，尽管我的代码使用了如此多的处理能力或内存(据我所知)，但它的运行速度却变慢了。 所以我写了这个测试，它使用了悲情和多重处理。
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()
这些是结果
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
===============
如您所见，多处理通常胜过 ProcessPool，甚至比 SerialPool 慢。 我正在运行 i5-2500，今天通过 pip 安装了 pathos
>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
为什么会发生这种情况？
最佳答案
您只会从要求较高的任务的并行化中受益。与多处理/多线程代码所需的通信相比，您的任务是相当即时的。尝试使用持续1s的函数，你就会看到效果。另外，请记住，在 python 中，由于 GIL，只有在 IO 受限的情况下，您才会从多线程中受益。对于 CPU 有限的任务，请使用多处理。
请参阅 Raymond 的演讲.
关于python 多处理，pathos 慢，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/46382319/