进程,是系统进行资源分配最小单位(拥有独立的内存单元)。(python中多进程是真的)
线程,是操作系统最小的执行单位(共享内存资源),比进程还小。(python中多线程是假的,因为cpython解释器中的一个模块GIL(全局解释器锁),GIl功能和互斥锁相似。)
 
 

证明过程:
(一)多进程
import multiprocessing
import os
import time def add2():
start_time = time.time()
for i in range(100000000):
pass
end_time = time.time()
use_time = end_time - start_time
print("进程id: %s use_time: %s" % (os.getpid(), use_time)) if __name__ == '__main__':
print("【进程测试】")
p1 = multiprocessing.Process(target=add2, args=(), name="p1-进程")
print("p1.name :%s" % p1.name)
p2 = multiprocessing.Process(target=add2, args=(), name="p2-进程")
start_time = time.time()
p1.start()
p2.start()
p1.join()
p2.join()
end_time = time.time()
use_time = end_time - start_time
print("主进程id:%s use_time: %s" % (os.getpid(),use_time)) print("====主进程单独运行一次循环耗时:=====")
add2()

  

多进程运行结果:
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" alt="" /> 
 
 
(二)多线程
import threading
import time def add2():
start_time = time.time()
for i in range(100000000):
pass
end_time = time.time()
use_time = end_time - start_time
print("线程id:%s 耗时:%s" % (threading.current_thread().ident, use_time)) if __name__ == '__main__':
print("【线程测试】")
print("主线程:%s 主线程id:%s" % (threading.current_thread(), threading.current_thread().ident))
t1 = threading.Thread(target=add2, args=(), name="t1-线程")
t2 = threading.Thread(target=add2, args=(), name="t2-线程")
start_time = time.time()
t1.start()
t2.start()
t1.join()
t2.join()
end_time = time.time()
use_time = end_time - start_time
print("线程id:%s 耗时:%s (主线程)" % (threading.current_thread().ident, use_time)) print("====主线程单独运行一次循环耗时:=====")
add2()

  

多线程运行结果:
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" alt="" />
 
 
(三)结论:
 
不论是线程还是进程,循环单独运行的时间都是差不多的4秒内。
而多线程的总耗时基本上是单独循环一次耗时的2倍左右,所以多线程是假的,是串行的。

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