# -*- coding: utf-8 -*-
import time
from concurrent.futures import ThreadPoolExecutor
import numpy as np

start=time.clock()
list1=np.random.randint(0,100,20)
print list1

def multithreading():

    list2=[]
    with ThreadPoolExecutor(max_workers=8) as executor:
        for result in executor.map(working,list1,chunksize=10):
            print result
            list2.append(result)

    return list2



def working(num):
    time.sleep(0.2)
    return num


x=multithreading()
print x

over= time.clock()

print (over-start)

以下是单进程和多进程的比较

总耗时：

单进程：

差距明显