Python 多处理似乎比常规执行慢 [英] Python multiprocessing seems slower than regular execution

问题描述

在下面的代码中，我正在生成数字为 9999 的立方体并通过线程池和普通方法调用它.

In the code below, I am generating cube of a number 9999 and calling the same via thread pool and normal method.

我正在计算两者之间的差异.似乎正常方法要快得多.我正在 i7 第 8 代英特尔处理器上运行它，在 python 2.7 终端内有 16 gig ram.

I am timing the difference between the same. Seems like the normal method is way faster. I am running this on a i7 8th gen intel processor with 16 gig ram inside a python 2.7 terminal.

我对此感到困惑.可能是我错过了什么.我希望这个问题对未来的人有所帮助.

I am baffled by this. May be I am missing something. I hope this question is helpful for people in the future.

import time
from multiprocessing.pool import ThreadPool

def cube():
    return 9999*9999*9999

print "Start Execution Threading: "
x = int(round(time.time() * 1000))
pool = ThreadPool()

for i in range(0,100):
  result = pool.apply_async(cube, ())
  result = pool.apply_async(cube, ())
  result = pool.apply_async(cube, ())
  # print result.get()

pool.close()
pool.join()

print "Stop Execution Threading: "
y = int(round(time.time() * 1000))

print y-x

print "Start Execution Main: "
x = int(round(time.time() * 1000))

for i in range(0,100):
  cube()
  cube()
  cube()

print "Stop Execution Main: "
y = int(round(time.time() * 1000))
print y-x

推荐答案

多处理意味着您将开始一个新线程.这带来了相当大的开销，因为它必须被初始化.因此，多线程只会带来回报，尤其是在 Python 中，当您并行化所有独立执行需要大量时间(与 Python 启动时间相比)并且可以允许异步运行的任务时.

Multiprocessing means you will start a new thread. That comes with quite an overhead in that it must be initialized. As such, multi-threading only pays off, especially in python, when you parallelize tasks which all on their own take considerable time to execute (in comparison to python start-up time) and which can be allowed to run asyncronously.

在你的例子中，一个简单的乘法执行得如此之快，它不会有回报.

In your case, a simple multiplication, is so quickly executed it will not pay off.

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