[python]多进程和多线程对比

本文探讨了在Python中使用多线程和多进程进行并行编程的策略,对比了多线程与多进程在不同场景下的优劣,如CPU密集型任务和IO密集型任务,并通过具体示例展示了如何有效利用ThreadPoolExecutor和ProcessPoolExecutor来提高程序执行效率。

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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))


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