有了多线程threading，为什么还要用多进程multiprocessing

多进程multiprocessing知识梳理（对比多线程threading）

代码实战：单线程、多线程、多进程对比CPU密集计算速度

tmp/06.thread_process_cpu_bound.py

import math
from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
import time


PRIMES = [112272535095293] * 100



def is_prime(n):
    if n < 2:
        return False
    if n == 2:
        return True
    if n % 2 == 0:
        return False
    sqrt_n = int(math.floor(math.sqrt(n)))
    for i in range(3, sqrt_n + 1, 2):
        if n % i == 0:
            return False
    return True


def single_thread():
    for number in PRIMES:
        is_prime(number)


def multi_thread():
    with ThreadPoolExecutor() as pool:
        pool.map(is_prime, PRIMES)


def multi_process():
    with ProcessPoolExecutor() as pool:
        pool.map(is_prime, PRIMES)


if __name__ == '__main__':
    start = time.time()
    single_thread()
    end = time.time()
    print("single_thread, cost: ", end - start, "seconds")

    start = time.time()
    multi_thread()
    end = time.time()
    print("multi_thread, cost: ", end - start, "seconds")

    start = time.time()
    multi_process()
    end = time.time()
    print("multi_process, cost: ", end - start, "seconds")

运行结果：

标签：multi,end,thread,python,编程,start,time,return,multiprocessing
来源： https://www.cnblogs.com/my_captain/p/16444937.html