Python中的并行编程与Pool.Map（）比较MAC OSX＆amp; Windows 10

Question

在Python 3.X中搜索各种可能性以进行多处理后，非常简单的程序。

在MAC和Windows上运行程序会提供不同的结果。与串行相比，MAC并行处理花费的时间更少。我阅读了有关IPC开销等的各种说明，因为这是一个很容易处理的功能，但是为什么在MAC OSX上结果更好。

基本上知道处理Pool.MAP（）的MAC OSX架构的人可以提供输入。提前谢谢。

**On MAC OSX**
cpu_count() = 4
Creating pool with 4 processes
pool = <multiprocessing.pool.Pool object at 0x1019d96a0>
Time for y_parallel =  2.866562843322754
Time for y_serial =  10.207499027252197


**On Windows**
cpu_count() = 4
Creating pool with 4 processes
pool = <multiprocessing.pool.Pool object at 0x031A1CD0>
Time for y_parallel =  13.155309915542603
Time for y_serial =  10.025831460952759

    import multiprocessing
    import time

    def f(x):
         time.sleep(0.1)
         return x+1

    # Serially adding 100 numbers (X+1) to y_serial
    y_serial = []
    x = range(100)
    startTime_serial = time.time()
    #for i in x:
    #    y_serial = y_serial + [f(i)]
    y_serial += map(f,x)
    endTime_serial = time.time()


    #Letting Pool allocate processes through map.Pool
    if __name__ == '__main__':
        print('cpu_count() = %d' % multiprocessing.cpu_count())
        PROCESSES = multiprocessing.cpu_count()
        print('Creating pool with %d processes' % PROCESSES)
        pool = multiprocessing.Pool()
        print('pool = %s' % pool)
        startTime_parallel = time.time()
        y_parallel = pool.map(f,x)
        pool.close()
        pool.join()
        endTime_parallel = time.time()
        print("Time for y_parallel = ", endTime_parallel - startTime_parallel)
        print ("Time for y_serial = ", endTime_serial - startTime_serial)
        print ("y_parallel", y_parallel)
        print ("y_serial=", y_serial)