进程是由系统自己管理的。

1：最基本的写法

from multiprocessing import Pool def f(x): return x*x if __name__ == '__main__': p = Pool(5) print(p.map(f, [1, 2, 3])) [1, 4, 9]

2、实际上是通过os.fork的方法产生进程的

unix中，所有进程都是通过fork的方法产生的。

multiprocessing Process os info(title): title , __name__ (os, ): , os.getppid() , os.getpid() f(name): info() , name __name__ == : info() p = Process(=f, =(,)) p.start() p.join()

3、线程共享内存

threading run(info_list,n): info_list.append(n) info_list __name__ == : info=[] i (): p=threading.Thread(=run,=[info,i]) p.start() [0] [0, 1] [0, 1, 2] [0, 1, 2, 3] [0, 1, 2, 3, 4] [0, 1, 2, 3, 4, 5] [0, 1, 2, 3, 4, 5, 6] [0, 1, 2, 3, 4, 5, 6, 7] [0, 1, 2, 3, 4, 5, 6, 7, 8] [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

进程不共享内存：

multiprocessing Process run(info_list,n): info_list.append(n) info_list __name__ == : info=[] i (): p=Process(=run,=[info,i]) p.start() [1] [2] [3] [0] [4] [5] [6] [7] [8] [9]

若想共享内存，需使用multiprocessing模块中的Queue

multiprocessing Process, Queue f(q,n): q.put([n,]) __name__ == : q=Queue() i (): p=Process(=f,=(q,i)) p.start() : q.get()

4、锁:仅是对于屏幕的共享，因为进程是独立的，所以对于多进程没有用

multiprocessing Process, Lock f(l, i): l.acquire() , i l.release() __name__ == : lock = Lock() num (): Process(=f, =(lock, num)).start() hello world 0 hello world 1 hello world 2 hello world 3 hello world 4 hello world 5 hello world 6 hello world 7 hello world 8 hello world 9

5、进程间内存共享：Value，Array

multiprocessing Process, Value, Array f(n, a): n.value = i ((a)): a[i] = -a[i] __name__ == : num = Value(, ) arr = Array(, ()) num.value arr[:] p = Process(=f, =(num, arr)) p.start() p.join() 0.0 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] 3.1415927 [0, -1, -2, -3, -4, -5, -6, -7, -8, -9]

#manager共享方法，但速度慢

multiprocessing Process, Manager f(d, l): d[] = d[] = d[] = l.reverse() __name__ == : manager = Manager() d = manager.dict() l = manager.list(()) p = Process(=f, =(d, l)) p.start() p.join() d l # print '-------------'这里只是另一种写法 # print pool.map(f,range(10)) {0.25: None, 1: '1', '2': 2} [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]

#异步：这种写法用的不多

multiprocessing Pool time f(x): x*x time.sleep() x*x __name__ == : pool=Pool(=) res_list=[] i (): res=pool.apply_async(f,[i]) res_list.append(res) r res_list: r.get(timeout=10) #超时时间

同步的就是apply