Python:了解线程模块 [英] Python: Understanding Threading Module

问题描述

在学习Python的 threading 模块时，我运行了一个简单的测试.有趣的是，线程是顺序运行的，而不是并行运行的.是否可以修改此测试代码，以便程序以与多处理相同的方式执行线程:并行执行?

While learning Python's threading module I've run a simple test. Interesting that the threads are running sequentially and not parallel. Is it possible to modify this test code so a program executes the threads in same fashion as multiprocessing does: in parallel?

import threading

def mySlowFunc(arg):
    print "\nStarting...", arg
    m=0
    for i in range(arg):
        m+=i
    print '\n...Finishing', arg

myList =[35000000, 45000000, 55000000]

for each in myList:
    thread = threading.Thread(target=mySlowFunc, args=(each,) )
    thread.daemon = True
    thread.start()
    thread.join()

print "\n Happy End \n"

修改后的代码:

此版本的代码将启动以并行"方式运行的6个线程".但是，即使有6个线程，实际上也只使用了两个CPU线程(另外6个物理CPU线程将处于空闲状态并且不执行任何操作).

REVISED CODE:

This version of the code will initiate 6 'threads' running in 'parallel'. But even while there will be 6 threads only two CPU's threads are actually used (6 other Physical CPU threads will be idling and doing nothing).

import threading

def mySlowFunc(arg):
    print "\nStarting " + str(arg) + "..."
    m=0
    for i in range(arg):
        m+=i
    print '\n...Finishing ' + str(arg)

myList =[35000000, 45000000, 55000000, 25000000, 75000000, 65000000]


for each in myList:
    thread = threading.Thread(target=mySlowFunc, args=(each,) )
    thread.daemon = False
    thread.start()

print "\n Bottom of script reached \n"

推荐答案

来自

等待直到线程终止.这将阻塞调用线程，直到被调用join()方法的线程终止(正常或通过未处理的异常终止)，或者直到发生可选的超时为止.

Wait until the thread terminates. This blocks the calling thread until the thread whose join() method is called terminates – either normally or through an unhandled exception – or until the optional timeout occurs.

只需创建一个线程列表，然后在启动其中的每一个线程之后将其加入即可.

Just create a list of threads and join them after launching every single one of them.

线程是并行执行的，您可以将python的线程想像成具有单核的计算机，事实是，python的线程最适合IO操作(读取/写入大文件，通过套接字发送数据，那类的东西).如果需要CPU功能，则需要使用多处理模块

The threads are executing in parallel, you can think of python's threads like a computer with a single core, the thing is, python's threads are best for IO operations (reading/writing a big file, sending data through a socket, that sort of thing). If you want CPU power you need to use the multiprocessing module

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