在Numpy中执行零功能 [英] Performance of zeros function in Numpy

问题描述

我刚刚注意到numpy的zeros函数具有奇怪的行为:

I just noticed that the zeros function of numpy has a strange behavior :

%timeit np.zeros((1000, 1000))
1.06 ms ± 29.8 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

%timeit np.zeros((5000, 5000))
4 µs ± 66 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

另一方面，ones似乎具有正常行为. 有人知道为什么用zeros函数初始化一个小的numpy数组比花一个大的数组要花更多的时间吗?

On the other hand, ones seems to have a normal behavior. Is anybody know why initializing a small numpy array with the zeros function takes more time than for a large array ?

(Python 3.5，numpy 1.11)

(Python 3.5, numpy 1.11)

推荐答案

这看起来像calloc达到了一个阈值，在该阈值下，OS要求内存清零，而无需手动对其进行初始化.查看源代码，最终numpy.zeros 委托给calloc 以获得清零的内存块，如果与numpy.empty进行比较，则不执行初始化:

This looks like calloc hitting a threshold where it makes an OS request for zeroed memory and doesn't need to initialize it manually. Looking through the source code, numpy.zeros eventually delegates to calloc to acquire a zeroed memory block, and if you compare to numpy.empty, which doesn't perform initialization:

In [15]: %timeit np.zeros((5000, 5000))
The slowest run took 12.65 times longer than the fastest. This could mean that a
n intermediate result is being cached.
100000 loops, best of 3: 10 µs per loop

In [16]: %timeit np.empty((5000, 5000))
The slowest run took 5.05 times longer than the fastest. This could mean that an
 intermediate result is being cached.
100000 loops, best of 3: 10.3 µs per loop

您会看到np.zeros对于5000x5000阵列没有初始化开销.

you can see that np.zeros has no initialization overhead for the 5000x5000 array.

实际上，在您尝试访问该内存之前，该操作系统甚至没有真正"分配该内存.在无数TB可用空间的机器上，对TB级阵列的请求成功完成:

In fact, the OS isn't even "really" allocating that memory until you try to access it. A request for terabytes of array succeeds on a machine without terabytes to spare:

In [23]: x = np.zeros(2**40)  # No MemoryError!

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