推荐回答(1个)
想要充分利用多核CPU资源,Python中大部分情况下都需要使用多进程,Python中提供了multiprocessing这个包实现多进程。multiprocessing支持子进程、进程间的同步与通信,提供了Process、Queue、Pipe、Lock等组件。
开辟子进程
multiprocessing中提供了Process类来生成进程实例
Process([group [, target [, name [, args [, kwargs]]]]])1
group分组,实际上不使用
target表示调用对象,你可以传入方法的名字
args表示给调用对象以元组的形式提供参数,比如target是函数a,他有两个参数m,n,那么该参数为args=(m, n)即可
kwargs表示调用对象的字典
name是别名,相当于给这个进程取一个名字
先来个小例子:
- # -*- coding:utf-8 -*-from multiprocessing import Process, Poolimport osimport timedef run_proc(wTime):
- n = 0
- while n < 3: print "subProcess %s run," % os.getpid(), "{0}".format(time.ctime()) #获取当前进程号和正在运行是的时间
- time.sleep(wTime) #等待(休眠)
- n += 1if __name__ == "__main__":
- p = Process(target=run_proc, args=(2,)) #申请子进程
- p.start() #运行进程
- print "Parent process run. subProcess is ", p.pid print "Parent process end,{0}".format(time.ctime())12345678910111213141516171819
运行结果:
Parent process run. subProcess is 30196
Parent process end,Mon Mar 27 11:20:21 2017
subProcess 30196 run, Mon Mar 27 11:20:21 2017
subProcess 30196 run, Mon Mar 27 11:20:23 2017
subProcess 30196 run, Mon Mar 27 11:20:25 2017
根据运行结果可知,父进程运行结束后子进程仍然还在运行,这可能造成僵尸( zombie)进程。
通常情况下,当子进程终结时,它会通知父进程,清空自己所占据的内存,并在内核里留下自己的退出信息。父进程在得知子进程终结时,会从内核中取出子进程的退出信息。但是,如果父进程早于子进程终结,这可能造成子进程的退出信息滞留在内核中,子进程成为僵尸(zombie)进程。当大量僵尸进程积累时,内存空间会被挤占。
有什么办法可以避免僵尸进程呢?
这里介绍进程的一个属性 deamon,当其值为TRUE时,其父进程结束,该进程也直接终止运行(即使还没运行完)。
所以给上面的程序加上p.deamon = true,看看效果。
- # -*- coding:utf-8 -*-from multiprocessing import Process, Poolimport osimport timedef run_proc(wTime):
- n = 0
- while n < 3: print "subProcess %s run," % os.getpid(), "{0}".format(time.ctime())
- time.sleep(wTime)
- n += 1if __name__ == "__main__":
- p = Process(target=run_proc, args=(2,))
- p.daemon = True #加入daemon
- p.start() print "Parent process run. subProcess is ", p.pid print "Parent process end,{0}".format(time.ctime())1234567891011121314151617181920
执行结果:
Parent process run. subProcess is 31856
Parent process end,Mon Mar 27 11:40:10 2017
这是问题又来了,子进程并没有执行完,这不是所期望的结果。有没办法将子进程执行完后才让父进程结束呢?
这里引入p.join()方法,它使子进程执行结束后,父进程才执行之后的代码
- # -*- coding:utf-8 -*-from multiprocessing import Process, Poolimport osimport timedef run_proc(wTime):
- n = 0
- while n < 3: print "subProcess %s run," % os.getpid(), "{0}".format(time.ctime())
- time.sleep(wTime)
- n += 1if __name__ == "__main__":
- p = Process(target=run_proc, args=(2,))
- p.daemon = True
- p.start()
- p.join() #加入join方法
- print "Parent process run. subProcess is ", p.pid print "Parent process end,{0}".format(time.ctime())123456789101112131415161718192021
执行结果:
subProcess 32076 run, Mon Mar 27 11:46:07 2017
subProcess 32076 run, Mon Mar 27 11:46:09 2017
subProcess 32076 run, Mon Mar 27 11:46:11 2017
Parent process run. subProcess is 32076
Parent process end,Mon Mar 27 11:46:13 2017
这样所有的进程就能顺利的执行了。
将进程定义成类
通过继承Process类,来自定义进程类,实现run方法。实例p通过调用p.start()时自动调用run方法。
如下:
- # -*- coding:utf-8 -*-from multiprocessing import Process, Poolimport osimport timeclass Myprocess(Process):
- def __init__(self, wTime):
- Process.__init__(self)
- self.wTime = wTime def run(self):
- n = 0
- while n < 3: print "subProcess %s run," % os.getpid(), "{0}".format(time.ctime())
- time.sleep(self.wTime)
- n += 1if __name__ == "__main__":
- p = Myprocess(2)
- p.daemon = True
- p.start() #自动调用run方法
- p.join() print "Parent process run. subProcess is ", p.pid print "Parent process end,{0}".format(time.ctime())12345678910111213141516171819202122232425262728
执行结果和上一个例子相同。
创建多个进程
很多时候系统都需要创建多个进程以提高CPU的利用率,当数量较少时,可以手动生成一个个Process实例。当进程数量很多时,或许可以利用循环,但是这需要程序员手动管理系统中并发进程的数量,有时会很麻烦。这时进程池Pool就可以发挥其功效了。可以通过传递参数限制并发进程的数量,默认值为CPU的核数。
直接上例子:
- # -*- coding:utf-8 -*-from multiprocessing import Process,Poolimport os,timedef run_proc(name): ##定义一个函数用于进程调用
- for i in range(5):
- time.sleep(0.2) #休眠0.2秒
- print 'Run child process %s (%s)' % (name, os.getpid())#执行一次该函数共需1秒的时间if __name__ =='__main__': #执行主进程
- print 'Run the main process (%s).' % (os.getpid())
- mainStart = time.time() #记录主进程开始的时间
- p = Pool(8) #开辟进程池
- for i in range(16): #开辟14个进程
- p.apply_async(run_proc,args=('Process'+str(i),))#每个进程都调用run_proc函数,
- #args表示给该函数传递的参数。
- print 'Waiting for all subprocesses done ...'
- p.close() #关闭进程池
- p.join() #等待开辟的所有进程执行完后,主进程才继续往下执行
- print 'All subprocesses done'
- mainEnd = time.time() #记录主进程结束时间
- print 'All process ran %0.2f seconds.' % (mainEnd-mainStart) #主进程执行时间123456789101112131415161718192021222324
执行结果:
开头部分
Run the main process (30920).
Waiting for all subprocesses done …
Run child process Process0 (32396)
Run child process Process3 (25392)
Run child process Process1 (28732)
Run child process Process2 (32436)
末尾部分:
Run child process Process15 (25880)
All subprocesses done
All process last 2.49 seconds.
相关说明:
这里进程池对并发进程的限制数量为8个,而程序运行时会产生16个进程,进程池将自动管理系统内进程的并发数量,其余进程将会在队列中等待。限制并发数量是因为,系统中并发的进程不是越多越好,并发进程太多,可能使CPU大部分的时间用于进程调度,而不是执行有效的计算。
采用多进程并发技术时,就单个处理机而言,其对进程的执行是串行的。但具体某个时刻哪个进程获得CPU资源而执行是不可预知的(如执行结果的开头部分,各进程的执行顺序不定),这就体现了进程的异步性。
如果单个程序执行14次run_proc函数,那么它会需要至少16秒,通过进程的并发,这里只需要2.49秒,可见并发的优势。
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