什么是 Multiprocessing

和 threading 的比较

多进程 Multiprocessing 和多线程 threading 类似, 他们都是在 python 中用来并行运算的. 不过既然有了 threading, 为什么 Python 还要出一个 multiprocessing 呢? 原因很简单, 就是用来弥补 threading 的一些劣势, 比如在 threading 教程中提到的GIL.

使用 multiprocessing 也非常简单, 如果对 threading 有一定了解的朋友, 你们的享受时间就到了. 因为 python 把 multiprocessing 和 threading 的使用方法做的几乎差不多. 这样我们就更容易上手. 也更容易发挥你电脑多核系统的威力了!

添加进程 Process

导入线程进程标准模块

import multiprocessing as mp
import threading as td

定义一个被线程和进程调用的函数

def job(a,d):
    print('aaaaa')

创建线程和进程

t1 = td.Thread(target=job,args=(1,2))
p1 = mp.Process(target=job,args=(1,2))

注意：Thread和Process的首字母都要大写，被调用的函数没有括号，被调用的函数的参数放在args(…)中

分别启动线程和进程

t1.start()
p1.start()

　　分别连接线程和进程

t1.join()
p1.join()

完整的线程和进程创建使用对比代码

import multiprocessing as mp
import threading as td

def job(a,d):
    print('aaaaa')

t1 = td.Thread(target=job,args=(1,2))
p1 = mp.Process(target=job,args=(1,2))
t1.start()
p1.start()
t1.join()
p1.join()

运用

if __name__=='__main__':

　　完整的应用代码：

import multiprocessing as mp

def job(a,d):
    print('aaaaa')

if __name__=='__main__':
    p1 = mp.Process(target=job,args=(1,2))
    p1.start()
    p1.join()

　　运行环境要在terminal环境下，可能其他的编辑工具会出现运行结束后没有打印结果，在terminal中的运行后打印的结果为：

aaaaa

#3-queue
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# Youtube video tutorial: https://www.youtube.com/channel/UCdyjiB5H8Pu7aDTNVXTTpcg
# Youku video tutorial: http://i.youku.com/pythontutorial

import multiprocessing as mp

def job(q):
    res = 0
    for i in range(1000):
        res += i+i**2+i**3
    q.put(res) # queue

if __name__ == '__main__':
    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(res1+res2)

#4-comparison
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# Youtube video tutorial: https://www.youtube.com/channel/UCdyjiB5H8Pu7aDTNVXTTpcg
# Youku video tutorial: http://i.youku.com/pythontutorial

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

def multithread():
    q = mp.Queue()
    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)

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)

#5-pool
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# Youtube video tutorial: https://www.youtube.com/channel/UCdyjiB5H8Pu7aDTNVXTTpcg
# Youku video tutorial: http://i.youku.com/pythontutorial

import multiprocessing as mp

def job(x):
    return x*x

def multicore():
    pool = mp.Pool(processes=2)
    res = pool.map(job, range(10))
    print(res)
    res = pool.apply_async(job, (2,))
    print(res.get())
    multi_res =[pool.apply_async(job, (i,)) for i in range(10)]
    print([res.get() for res in multi_res])

if __name__ == '__main__':
    multicore()

　6-共享内存

Shared Value

我们可以通过使用Value数据存储在一个共享的内存表中。

import multiprocessing as mp

value1 = mp.Value('i', 0) 
value2 = mp.Value('d', 3.14)

　　其中d和i参数用来设置数据类型的，d表示一个双精浮点类型，i表示一个带符号的整型。更多的形式请查看本页最后的表.

Shared Array

在Python的mutiprocessing中，有还有一个Array类，可以和共享内存交互，来实现在进程之间共享数据。

array = mp.Array('i', [1, 2, 3, 4])

这里的Array和numpy中的不同，它只能是一维的，不能是多维的。同样和Value 一样，需要定义数据形式，否则会报错。我们会在后一节举例说明这两种的使用方法.

错误形式

array = mp.Array('i', [[1, 2], [3, 4]]) # 2维list

"""
TypeError: an integer is required
"""



各参数代表的数据类型

| Type code | C Type             | Python Type       | Minimum size in bytes |
| --------- | ------------------ | ----------------- | --------------------- |
| `'b'`     | signed char        | int               | 1                     |
| `'B'`     | unsigned char      | int               | 1                     |
| `'u'`     | Py_UNICODE         | Unicode character | 2                     |
| `'h'`     | signed short       | int               | 2                     |
| `'H'`     | unsigned short     | int               | 2                     |
| `'i'`     | signed int         | int               | 2                     |
| `'I'`     | unsigned int       | int               | 2                     |
| `'l'`     | signed long        | int               | 4                     |
| `'L'`     | unsigned long      | int               | 4                     |
| `'q'`     | signed long long   | int               | 8                     |
| `'Q'`     | unsigned long long | int               | 8                     |
| `'f'`     | float              | float             | 4                     |
| `'d'`     | double             | float             | 8                     |

#7-lock
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import multiprocessing as mp
import time

def job(v, num, l):
    l.acquire()
    for _ in range(10):
        time.sleep(0.1)
        v.value += num
        print(v.value)
    l.release()

def multicore():
    l = mp.Lock()
    v = mp.Value('i', 0)
    p1 = mp.Process(target=job, args=(v, 1, l))
    p2 = mp.Process(target=job, args=(v, 3, l))
    p1.start()
    p2.start()
    p1.join()
    p2.join()

if __name__ == '__main__':
    multicore()

多进程 multiprocessing