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108 lines (86 loc) · 2.52 KB
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"""
Python中全局解释器锁（GIL）的存在，在任意时刻只允许一个线程在解释器中运行，因此python的多线程不适合处理cpu密集型的任务，想要处理cpu密集型的任务，可以使用多进程模型
使用标准库中multiprocessing.Process，它可以启动进程执行任务操作接口，进程间通信，进程间同步等都与Threading.Thread类似
"""
from multiprocessing import Process
from threading import Thread
import time
x = 1
# 多个子进程之间的内存是独立的
def f(s):
print(s)
global x
x = 5
print(f'Process output x: {x}')
def f2(q):
print('F2 Start')
print("Get the q: ", q.get())
print('F2 End')
def f3(c):
print('子进程通过Pipe收到：', c.recv())
c.send(50)
from decimal import Decimal
def isArmstrong(n):
a, t = [], n
while t > 0:
a.append(t % 10)
t /= 10
k = len(a)
return (sum([Decimal(x) ** k for x in a])) == Decimal(n)
def findArmstrong(a, b):
print(a, b)
result = [k for k in range(a, b) if isArmstrong(k)]
print('%s - %s: %s' % (a, b, result))
def findByThread(*argslist):
workers = []
for args in argslist:
worker = Thread(target=findArmstrong, args=args)
workers.append(worker)
worker.start()
for worker in workers:
worker.join()
def findByProcess(*argslist):
workers = []
for args in argslist:
worker = Process(target=findArmstrong, args=args)
workers.append(worker)
worker.start()
for worker in workers:
worker.join()
if __name__ == '__main__':
# p = Process(target=f, args=('hello',))
#
# p.start()
# p.join()
# print(f'Main output x {x}')
#
# # 可以用过Queue和Pipe在进程间通信
#
# # Queue
# q = Queue()
# q.put(1)
# print(q.get())
# p2 = Process(target=f2, args=(q,))
# p2.start()
# # 延时体现子进程被挂起的
# time.sleep(2)
# # 在队列中插入数据，子进程中得到数据，并打印出来
# q.put(1)
#
# # Pipe
# c1, c2 = Pipe()
# c1.send('abc')
# print(c2.recv())
# c2.send('efg')
# print(c1.recv())
#
# p3 = Process(target=f3, args=(c2,))
# p3.start()
#
# c1.send(55)
# print("主进程通过Pipe收到：", c1.recv())
# 多线程 vs 多进程的运行时间
start = time.time()
# findByProcess((2000000, 2500000), (2500000, 3000000))
findByThread((2000000, 2500000), (2500000, 3000000))
print(time.time() - start)