CPU 密集型任务中的多线程和单线程性能问题 | 密集型任务中的多线程和单线程性能问题

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问题描述

以下两个单线程和多线程脚本在我输入一个大数字(如 555550000

The two following single-threading and multi-threading scripts are taking the same time when I give as input a big number like 555550000

单线程

import threading, time
a=[]
def print_factors(x):
   for i in range(1, x + 1):
       if x % i == 0:
           a.append(i)

n=int(input("Please enter a large number"))
print ("Starting time is %s" % ( time.ctime(time.time()) ))
print("The factors of",n,"are:")
thread = threading.Thread(target=print_factors,args=(n,))
thread.start()
thread.join()
print("Finishing time is %s" % (time.ctime(time.time())))
print(a)

多线程

import threading, time
a=[]
def print_factors1(x):
   for i in range(1, int(x/2)):
       if x % i == 0:
           a.append(i)

def print_factors2(x):
    for i in range(int(x/2), x+1):
        if x % i == 0:
            a.append(i)

n=int(input("Please enter a large number"))
print ("Starting time is %s" % ( time.ctime(time.time()) ))
thread1 = threading.Thread(target=print_factors1,args=(n,))
thread2 = threading.Thread(target=print_factors2,args=(n,))
print("The factors of",n,"are:")
thread1.start()
thread2.start()
thread2.join()
print("Finishing time is %s" % (time.ctime(time.time())))
print(a)

我试图了解单线程和多线程在获得结果所需的时间方面的区别.
我正在测量两种类型的相似时间，但我无法找出原因.

I am trying to understand the difference between single-threading and multi-threading in terms of time taken to got the results.
I'm measuring similar timings for both types and I cannot figuring out the reasons.

推荐答案

你的问题是 GIL，全局解释器锁.

Your problem is GIL, the Global Interpreter Lock.

Python 全局解释器锁或 GIL，简单来说，就是一个互斥锁(或锁)只允许一个线程控制Python 解释器.

您可以在此处找到有关 GIL 的详细信息(只需在 Google 上快速搜索即可找到更多来源):

You can found detailed informations about GIL here (just a fast search on Google and you can find a lot more sources):

https://wiki.python.org/moin/GlobalInterpreterLock
什么是 CPython 中的全局解释器锁 (GIL)?
https://medium.com/python-features/pythons-gil-a-hurdle-to-multithreaded-program-d04ad9c1a63

您需要更改您的实现以使用进程而不是线程.
我按如下方式更改了您的脚本:

You need to change your implementation to use processes instead of threads.
I changed your script as follows:

from multiprocessing import Pool
import time
def print_factors1(x):
    a=[]
    for i in range(1, int(x/2)):
        if x % i == 0:
            a.append(i)
    return a

def print_factors2(x):
    a=[]
    for i in range(int(x/2), x+1):
        if x % i == 0:
            a.append(i)
    return a

if __name__ == '__main__':
    n=int(input("Please enter a large number"))
    pool = Pool(processes=2)
    print ("Starting time is %s" % ( time.ctime(time.time()) ))

    process1 = pool.apply_async(print_factors1,[n])
    process2 = pool.apply_async(print_factors2,[n])

    pool.close()
    pool.join()

    print("Finishing time is %s" % (time.ctime(time.time())))
    print("The factors of",n,"are:")
    print(process1.get())
    print(process2.get())

考虑到线程共享内存，进程不.

Take into account that threads share the memory, processes don't.

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