Python学习之多进程多线程

我们通过对比下多进程，多线程和什么都不做时的消耗时间，看看哪种方式更有效率。

直接贴代码

import multiprocessing as mp
import threading as td
import time

def job(q):
    res = 0
    for i in range(1000000):
        res += i + i**2 + i**3
    q.put(res) # queue

def multicore():
    q = mp.Queue()
    p1 = mp.Process(target=job, args=(q,))
    p2 = mp.Process(target=job, args=(q,))
    p1.start()
    p2.start()
    p1.join()
    p2.join()
    res1 = q.get()
    res2 = q.get()
    print('multicore:',res1 + res2)

def multithread():
    q = mp.Queue() # thread可放入process同样的queue中
    t1 = td.Thread(target=job, args=(q,))
    t2 = td.Thread(target=job, args=(q,))
    t1.start()
    t2.start()
    t1.join()
    t2.join()
    res1 = q.get()
    res2 = q.get()
    print('multithread:', res1 + res2)

def normal():
    res = 0
    for _ in range(2):
        for i in range(1000000):
            res += i + i**2 + i**3
    print('normal:', res)

if __name__ == '__main__':
    st = time.time()
    normal()
    st1 = time.time()
    print('normal time:', st1 - st)
    multithread()
    st2 = time.time()
    print('multithread time:', st2 - st1)
    multicore()
    print('multicore time:', time.time() - st2)

运算结果：

normal: 499999666667166666000000
normal time: 2.0605103969573975
multithread: 499999666667166666000000
multithread time: 1.942901611328125
multicore: 499999666667166666000000
multicore time: 1.177018404006958