以下代码亲测可运行，环境py3.5

：使用多进程的pool+map

def f(x):

return x * x

if name == "main":

cores = multiprocessing.cpu_count()

pool = multiprocessing.Pool(processes=cores)

xs = range(5)
# method 1: map
print(pool.map(f,xs))  # prints [0,1,4,9,16]

# method 2: imap
for y in pool.imap(f,xs):
    print(y)            # 0,16,respectively

# method 3: imap_unordered
for y in pool.imap_unordered(f,xs):
    print(y)           # may be in any order

cnt = 0
for _ in pool.imap_unordered(f,xs):
    sys.stdout.write('done %d/%dr' % (cnt,len(xs)))
    cnt += 1</code></pre><p><span style="color:#cc0000;"&gt;<strong>或者</strong></span></p><pre><code class="language-python"&gt;import multiprocessing
import time
def func(msg):

for i in range(3):

print(msg)

time.sleep(1)

return "done " + msg
if name == "main":

pool = multiprocessing.Pool(processes=2)

result = []

for i in range(5):

msg = "hello %d" %(i)

result.append(pool.apply_async(func,(msg,)))

pool.close()

pool.join()

for res in result:

print(res.get())

print("Sub-process(es) done.")

<span style="color:#cc0000;">

<span style="color:#cc0000;">案例2：使用多进程（multiprocessing）

<code class="language-python"># Similarity and difference of multi thread vs. multi process

Written by Vamei
import os

import threading

import multiprocessing
worker function
def worker(sign,lock):

lock.acquire()

print(sign,os.getpid())

lock.release()
if name == "main":
Main
print('Main:',os.getpid())

# Multi-thread
record = []
lock  = threading.Lock()
for i in range(5):
    thread = threading.Thread(target=worker,args=('thread',lock))
    thread.start()
    record.append(thread)

for thread in record:
    thread.join()

# Multi-process
record = []
lock = multiprocessing.Lock()
for i in range(5):
    process = multiprocessing.Process(target=worker,args=('process',lock))
    process.start()
    record.append(process)

for process in record:
    process.join()</code></pre>注意：<p>但在使用这些共享API的时候，我们要注意以下几点:</p><p>在UNIX平台上，当某个进程终结之后，该进程需要被其父进程调用wait，否则进程成为僵尸进程(Zombie)。所以，有必要对每个Process对象调用join()方法 (实际上等同于wait)。对于多线程来说，由于只有一个进程，所以不存在此必要性。</p><p>multiprocessing提供了threading包中没有的IPC(比如Pipe和Queue)，效率上更高。应优先考虑Pipe和Queue，避免使用Lock/Event/Semaphore/Condition等同步方式 (因为它们占据的不是用户进程的资源)。</p><p>多进程应该避免共享资源。在多线程中，我们可以比较容易地共享资源，比如使用全局变量或者传递参数。在多进程情况下，由于每个进程有自己独立的内存空间，以上方法并不合适。此时我们可以通过共享内存和Manager的方法来共享资源。但这样做提高了程序的复杂度，并因为同步的需要而降低了程序的效率。</p><p>Process.PID中保存有PID，如果进程还没有start()，则PID为None。</p><p><span style="color:#cc0000;"&gt;<strong>案例3</strong></span>：使用多进程的quene</p><pre><code class="language-python"&gt;# Written by Vamei
import os

import multiprocessing

import time
==================
input worker
def inputQ(queue):

info = str(os.getpid()) + '(put):' + str(time.time())

queue.put(info)
output worker
def outputQ(queue,lock):

info = queue.get()

lock.acquire()

print (str(os.getpid()) + '(get):' + info)

lock.release()
===================
Main
record1 = []   # store input processes

record2 = []   # store output processes

lock  = multiprocessing.Lock()    # To prevent messy print

queue = multiprocessing.Queue(3)
input processes
for i in range(10):

process = multiprocessing.Process(target=inputQ,args=(queue,))

process.start()

record1.append(process)
output processes
for i in range(10):

process = multiprocessing.Process(target=outputQ,lock))

process.start()

record2.append(process)
for p in record1:

p.join()
queue.close()  # No more object will come,close the queue
for p in record2:

p.join()

参考：http://www.cnblogs.com/vamei/archive/2012/10/12/2721484.html

（编辑：李大同）

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