《Python程序设计》第12章 多线程和多进程

口令：RAPID708第12章

多线程和多进程学习目标了解多线程和多进程的含义和区别。掌握多进程的相关操作。掌握多线程的相关操作。多线程程序只能从上到下，逐行执行代码，多线程让程序拥有分身效果，能同时处理多件事情。多线程是加速程序运算的有效方法，在Python中多线程操作使用“threading”模块。创建线程importthreadingdefthread_job():print('当前线程个数：%s'%threading.active_count())print('当前线程信息：%s'%threading.current_thread())print('所有线程信息：%s'%threading.enumerate())definit():thread=threading.Thread(target=thread_job)#定义线程thread.start() #启动线程if__name__=='__main__':init()多线程多线程操作时可能会遇到线程抢占的情况。importthreadingimporttimedefthread_job():print("T1start\n")time.sleep(0.1) #延时0.1sprint("T1finish\n")added_thread=threading.Thread(target=thread_job,name='T1')added_thread.start()print("alldone\n")多线程线程等待importthreadingimporttimedefthread1_job():print('T1start\n')time.sleep(1)print('T1finish\n')defthread2_job():print('T2start\n')print('T2finish\n')defmain():thread1=threading.Thread(target=thread1_job,name='T1')thread2=threading.Thread(target=thread2_job,name='T2')thread1.start()thread2.start()thread1.join()#等待线程T1执行完成后，才会继续运行

print('alldone\n')if__name__=='__main__':main()多线程线程存储importthreadingimporttimefromqueueimportQueuedefjob(l,q):foriinrange(len(l)):l[i]=l[i]**2q.put(l) #数据存储definit():q=Queue()threads=[]data=[[1,2,3],[4,5,6],[7,7,7],[8,8,8]]foriinrange(4):#创建4个线程t=threading.Thread(target=job,args=(data[i],q))t.start()threads.append(t)forthreadinthreads:thread.join()results=[]for_inrange(4):results.append(q.get()) #获取数据print(results)if__name__=='__main__':init()多线程使用“join()”方法虽然在一定程度上加强了线程管理，但是线程运行效果依旧很混乱。importthreadingdefjob1():globalAforiinrange(10):#全局变量A循环10次，每次循环加1A+=1print('job1',A)defjob2():globalAforiinrange(10):#全局变量A循环10次，每次循环加10A+=10print('job2',A)if__name__=='__main__':lock=threading.Lock()A=0t1=threading.Thread(target=job1)t2=threading.Thread(target=job2)t1.start()t2.start()t1.join()t2.join()多线程线程同步importthreadingdefjob1():globalA,locklock.acquire() #内存上锁foriinrange(10):A+=1print('job1',A)lock.release() #内存锁打开defjob2():globalA,locklock.acquire()foriinrange(10):A+=10print('job2',A)lock.release()if__name__=='__main__':lock=threading.Lock() #定义Lock()对象A=0t1=threading.Thread(target=job1)t2=threading.Thread(target=job2)t1.start()t2.start()t1.join()t2.join()多进程多线程是有劣势的，“GIL”（全局解释器锁）让它没能更有效率的处理一些分摊的任务，而现在的电脑大部分配备了多核处理器，多进程可以让电脑更有效率的分配任务给每一个处理器,这种做法有效解决多线程的弊端。importmultiprocessingasmpdefjob():print('job')if__name__=='__main__':p1=mp.Process(target=job)p1.start()p1.join()多进程importmultiprocessingasmpdefjob(q):res=0foriinrange(1000):res+=i+i**2+i**3q.put(res)#将进程运行结果存放到队列中if__name__=='__main__':q=mp.Queue()p1=mp.Process(target=job,args=(q,))#进程1p2=mp.Process(target=job,args=(q,))#进程2p1.start()p2.start()p1.join()p2.join()res1=q.get() #从队列中取出数据res2=q.get()print(res1+res2)进程存储多进程进程池importmultiprocessingasmpdefjob(x):returnx*xdefinit():pool=mp.Pool()res=pool.map(job,range(10))print(res)if__name__=='__main__':init()设置进程池CPU核数definit():pool=mp.Pool(processes=3)#自定义CPU核数量为3res=pool.map(job,range(10))print(res)进程池传单个参数definit():pool=mp.Pool()res=pool.map(job,range(10))print(res)res=pool.apply_async(job,(2,))print(res.get())#获得结果进程池传多个参数definit():pool=mp.Pool()res=pool.map(job,range(10))print(res)res=pool.apply_async(job,(2,))print(res.get())#获得结果

multi_res=[pool.apply_async(job,(i,))foriinrange(10)]#迭代器输入

print([res.get()forresinmulti_res])#迭代输出多进程进程同步importmultiprocessingasmpimporttimedefjob(num1,num2):for_inrange(5):time.sleep(0.1)#延时0.1Snum1.value+=num2#v.value获取共享变量值print(num1.value,end="")definit():num=mp.Value('i',0)#定义共享变量p1=mp.Process(target=job,args=(num,1)) #累加值1p2=mp.Process(target=job,args=(num,3)) #累加值3p1.start()p2.start()p1.join()p2.join()if__name__=='__main__':init()多进程进程锁importmultiprocessingasmpimporttimedefjob(num1,num2,l):l.acquire()#内存锁住for_inrange(5):time.sleep(0.1)num1.value+=num2#获取共享内存print(num1.value)l.release()#内存释放definit():l=mp.Lock(