#1、基本创建
import multiprocessing as mp
import threading as td
def job(a, b):
print(a, b)
def main():
p1 = mp.Process(target=job, args=(1, 2)) # 必须在主函数写，调用启动一个子进程
p1.start() #子进程开始运行
p1.join() #等待子进程运行之后，主进程才往下运行
if __name__ == '__main__':
main()
#2、队列基本使用
import multiprocessing as mp
import threading as td
def job(q):
res = 0
for i in range(1000):
res += i + i ** 2 + i ** 3
q.put(res)
def 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('执行完成', res1, res2)
if __name__ == '__main__':
main()
#3、 不用线程进程、用多线程、用多进程对比
import multiprocessing as mp
import threading as td
import time
# 工作函数
def job(q):
res = 0
for i in range(1000000):
res += i + i ** 2 + i ** 3
q.put(res)
# 多进程
def multprocess():
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('multprocess执行完成', res1 + res2)
# 多线程
def multthread():
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('multthread执行完成', res1 + res2)
# 普通
def normal():
res = 0
for i in range(2): # 与两个线程、两个进程做对比
for i in range(1000000):
res += i + i ** 2 + i ** 3
print('normal执行完成', res)
# 主函数
if __name__ == '__main__':
st = time.time()
normal()
st1 = time.time()
print('normal耗时:', st1 - st)
multthread()
st2 = time.time()
print('multthread耗时:', st2 - st1)
multprocess();
print('multprocess耗时:', time.time() - st2)
#4、pool多进程池
import multiprocessing as mp
import threading as td
import time
# 工作函数
def job(x):
return x * x
def multipool(): # 进程池
pool = mp.Pool(processes=3) # 不传参数默认使用所有的cpu,这样只用3个
res = pool.map(job, range(10)) # 可以放入多个参数，多个进程去执行
print(res)
res = pool.apply_async(job, (2,)) # 1次只能使用一个进程算一个东西，第一个是工作对象,第二个是对象(此处只能有1个参数)
print(res.get())
pool_list = [pool.apply_async(job, (i,)) for i in range(10)] # 迭代，可以多个参数了
print([data.get() for data in pool_list])
# 主函数
if __name__ == '__main__':
multipool()
#5、共享内存
import multiprocessing as mp
# 共享内存，多进程多核之间交流，必须用共享内存
value = mp.Value('d', 1) # 第一个参数代表类型
array = mp.Array('i', [1, 2, 3]) # 第一个参数代表类型,第二个参数只能是一维的一个列表,不能是多维
#6、 共享内存应用、锁
import multiprocessing as mp
import time
def job(lock, v, num):
lock.acquire()
for _ in range(10):
time.sleep(0.1)
v.value += num # 多进程之间使用共享内存设置、取值都要用.value才可以
print(v.value)
lock.release()
def main():
lock = mp.Lock()
v = mp.Value('i', 0);
t1 = mp.Process(target=job, args=(lock, v, 1));
t2 = mp.Process(target=job, args=(lock, v, 3));
t1.start()
t2.start()
t1.join()
t2.join()
print(v.value)
if __name__ == '__main__':
st = time.time()
main()
f = time.time() - st
print(f)
