尝试运行此：import math
import time
from multiprocessing import Queue
import multiprocessing
def factorize_naive(n):
factors = []
for div in range(2, int(n**.5)+1):
while not n % div:
factors.append(div)
n //= div
if n != 1:
factors.append(n)
return factors
nums = range(100000)
nprocs = 4
def worker(nums, out_q):
""" The worker function, invoked in a process. 'nums' is a
list of numbers to factor. The results are placed in
a dictionary that's pushed to a queue.
"""
outdict = {}
for n in nums:
outdict[n] = factorize_naive(n)
out_q.put(outdict)
# Each process will get 'chunksize' nums and a queue to put his out
# dict into
out_q = Queue()
chunksize = int(math.ceil(len(nums) / float(nprocs)))
procs = []
for i in range(nprocs):
p = multiprocessing.Process(
target=worker,
args=(nums[chunksize * i:chunksize * (i + 1)],
out_q))
procs.append(p)
p.start()
# Collect all results into a single result dict. We know how many dicts
# with results to expect.
resultdict = {}
for i in range(nprocs):
resultdict.update(out_q.get())
time.sleep(5)
# Wait for all worker processes to finish
for p in procs:
p.join()
print resultdict
time.sleep(15)
打开任务管理器。您应该能够看到4个子进程在被操作系统终止(由于连接调用)之前处于僵尸状态几秒钟：
在更复杂的情况下，子进程可能永远处于僵尸状态(就像您在另一个question中询问的情况)，如果创建了足够多的子进程，则可以填充进程表，从而给操作系统带来麻烦(这可能会杀死主进程以避免失败)。
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