Windows(64位)上的Python 2.7.8(64位)是Ubuntu(64位)上2.7.8(64位)的一半

在Linux上需要1.09171080828秒.在Windows上需要2.14042000294秒.基准代码：import timedef mk_array(num):return [x for x in xrange(1,num)]def run():arr = mk_array(10000000)x = 0start = time.time()x = red...

在Linux上需要1.09171080828秒.

在Windows上需要2.14042000294秒.

基准代码：

import time


def mk_array(num):
        return  [x for x in xrange(1,num)]

def run():
        arr = mk_array(10000000)
        x = 0
        start = time.time()
        x = reduce(lambda x,y: x + y, arr)
        done = time.time()
        elapsed = done - start
        return elapsed

if __name__ == '__main__':
        times = [run() for x in xrange(0,100)]
        avg = sum(times)/len(times)
        print (avg)

我知道GIL会创建或多或少的单线程脚本.

Windows box是我的Hyper-V主机,但是应该足够强大以完全运行单个线程的脚本. 12核2.93Ghz Intel X5670s,72GB ram等

Ubuntu VM具有4核和8GB内存.

两者都运行Python 2.7.8 64位.

为什么Windows速度快一半？

编辑：我已经在0.010593495369秒内放了两个零和linux完成,在0.171899962425秒内放了Windows.谢谢大家,好奇心得到满足.

解决方法:

这是因为在Windows中long的大小,在Windows中,unix中的long是32位,而在Windows中是64位,因此您会更快地遇到Arbitrary-precision_arithmetic问题,这是分配成本更高的问题.

相关问题Why are python’s for loops so non-linear for large inputs

如果仅对xrange进行基准测试,您将看到很大的不同.

Windows使用LLP64的背后原因似乎与32位代码兼容：

Another alternative is the LLP64 model, which maintains compatibility with 32-bit code by leaving both int and long as 32-bit. “LL” refers to the “long long integer” type, which is at least 64 bits on all platforms, including 32-bit environments.

取自wiki/64-bit_computing