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操作来说, 使用多线程编程,进程切换代价要高于线程
#1. 对于耗费cpu的操作,多进程由于多线程
# 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(25,40)]
# 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))
# if __name__ == "__main__":
# with ProcessPoolExecutor(3) as executor: #在windows下多进程要在 主函数中才能执行
# all_task = [executor.submit(fib, (num)) for num in range(25,40)]
# 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操作来说,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:
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))
[python]多进程和多线程对比
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转载自blog.csdn.net/qq_38065133/article/details/82795600
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