愤怒中的小草

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import time
from concurrent.futures import ThreadPoolExecutor,as_completed
from concurrent.futures import ProcessPoolExecutor
#多进程编程
#耗CPU的操作,用多进程编程;对于IO操作,使用多线程编程;进程切换的代价要高于线程

#1. 对于耗CPU的操作,多进程优于多线程,比如计算和图形操作 机器学习
def fib(n):
if n<=2:
return 1;
return fib(n-1) + fib(n-2)
"""
with ThreadPoolExecutor(3) as executor:
all_task = [executor.submit(fib,(num)) for num in range(25,35)]
start_time = time.time()
for future in as_completed(all_task):
data = future.result()
print("exe result:{}".format(data))

print("last time is:{}".format(time.time() - start_time))
"""
#window下编程 ProcessPoolExecutor要在main下面,linux下无此问题
#线程花费的时间 明显比进程要多
"""
if __name__ == "__main__":
with ProcessPoolExecutor(3) as executor:
all_task = [executor.submit(fib,(num)) for num in range(25,35)]
start_time = time.time()
for future in as_completed(all_task):
data = future.result()
print("exe result:{}".format(data))

print("last time is:{}".format(time.time() - start_time))
"""
#2. 对于IO操作,多线程优于多进程
def random_sleep(n):
time.sleep(n)
return n

if __name__ == "__main__":
#with ThreadPoolExecutor(3) as executor:
with ProcessPoolExecutor(3) as executor:
all_task = [executor.submit(random_sleep,(num)) for num in [2]*30]
start_time = time.time()
for future in as_completed(all_task):
data = future.result()
print("exe result:{}".format(data))

print("last time is:{}".format(time.time() - start_time))




posted on 2019-02-22 22:57  愤怒中的小草  阅读(187)  评论(0编辑  收藏  举报