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: