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# -*- 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