python学习笔记之多进程练习multiprocessing对CPU密集型任务进行优化_python multiprocessing现状 cpu

主要练习使用多进程ProcessPoolExecutor对CPU密集型任务进行优化

import time
from multiprocessing import Process, Queue, Lock
from concurrent.futures import ProcessPoolExecutor


def func(x):
    '''
    CPU密集型任务
    单个耗时2.94 seconds
    '''
    for i in range(50000000):
        x += i
    return x


if __name__ == '__main__':
    t0 = time.time()
    with ProcessPoolExecutor() as executor:
        results = executor.map(func, [1, 2, 3])
        for result in results:
            print(result)
    t1 = time.time()
    print("ProcessPoolExecutor多进程运行cost:", t1 - t0, "seconds")

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