python并发编程实战（九）：使用多进程multiprocessing模块加速程序的运行

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：