python 多线程与多进程的简单测试比较

想必对接触过python的朋友都知道，python的多线程由于历史原因有一个GIL，并不是真正的多线程。所以查了些许资料亲自跑了下程序比较了下。
测试环境
macbook pro， 双核四线程，i5处理器。
比较对象

1. 单线程
2. 多线程
3. 多进程
   执行的任务都是耗cpu型的任务

单线程

# thread_test.pyimport randomimport threadingdef list_append(count, id, out_list):    """    Creates an empty list and then appends a     random number to the list 'count' number    of times. A CPU-heavy operation!    """    for i in range(count):        out_list.append(random.random())def main():    size = 10000000   # Number of random numbers to add    threads = 2   # Number of threads to create    # Create a list of jobs and then iterate through    # the number of threads appending each thread to    # the job list     # jobs = []    for i in range(0, threads):        out_list = list()        thread = threading.Thread(target=list_append(size, i, out_list))        thread.start()        thread.join()if __name__ == "__main__":    main()

单线程执行两次list_append函数。

多线程

# thread_test.pyimport randomimport threadingdef list_append(count, id, out_list):    """    Creates an empty list and then appends a     random number to the list 'count' number    of times. A CPU-heavy operation!    """    for i in range(count):        out_list.append(random.random())def main():    size = 10000000   # Number of random numbers to add    threads = 2   # Number of threads to create    # Create a list of jobs and then iterate through    # the number of threads appending each thread to    # the job list     jobs = []    for i in range(0, threads):        out_list = list()        thread = threading.Thread(target=list_append(size, i, out_list))        jobs.append(thread)    # Start the threads (i.e. calculate the random number lists)    for j in jobs:        j.start()    # Ensure all of the threads have finished    for j in jobs:        j.join()if __name__ == "__main__":    main()

开双线程执行同一个函数。

多进程

# multiproc_test.pyimport randomimport multiprocessingdef list_append(count, id, out_list):    """    Creates an empty list and then appends a     random number to the list 'count' number    of times. A CPU-heavy operation!    """    for i in range(count):        out_list.append(random.random())def main():    size = 10000000   # Number of random numbers to add    procs = 2   # Number of processes to create    # Create a list of jobs and then iterate through    # the number of processes appending each process to    # the job list     jobs = []    for i in range(0, procs):        out_list = list()        process = multiprocessing.Process(target=list_append,                                           args=(size, i, out_list))        jobs.append(process)    # Start the processes (i.e. calculate the random number lists)          for j in jobs:        j.start()    # Ensure all of the processes have finished    for j in jobs:        j.join()if __name__ == "__main__":    main()

开双进程执行同一个函数。

测试结果

$ time python single_thread_test.py real    0m4.590suser    0m4.082ssys     0m0.448s

$ time python thread_test.py real    0m4.426suser    0m3.900ssys     0m0.456s

$ time python process_test.py real    0m2.436suser    0m4.028ssys     0m0.657s

测试程序用时用的是time指令，传送门在此可以自行学习。

可以从上面的测试结果看到，单线程与双线程在耗时方面并没有太多的区别，但是改为双进程后可以很明显的看到，耗时少了接近一半。这很好的说明了python多进程才是真正的并发。
当然python的多线程也不是一无是处，还是多线程有时相较于单线程还是有优势的，具体可以看这篇博客：传送门

参考文献：
https://www.quantstart.com/articles/parallelising-python-with-threading-and-multiprocessing
http://blog.csdn.net/thinkerabc/article/details/647272
http://www.redicecn.com/html/Python/20111223/355.html
http://python.jobbole.com/84205/