numpy.dot() result different with classic computation for large-size arrays

[Gribouillis]

I guess it is because a 64 bits signed integer can store values between -2**63 and +2**63-1, which order of magnitude is 10**19, but here the result has a magnitude of 10**23. It follows that some bits are silently lost by overflow in the computation with 64 bits integers. Here is an example

>>> import array
>>> a = array.array('q')
>>> x = 2**63-1000
>>> x
9223372036854774808
>>> a.append(x)
>>> a
array('q', [9223372036854774808])
>>> import numpy as np
>>> np.dot(a, a)
1000000
>>> x * x
85070591730234597419099578148391436864
>>>