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