Tue Feb 28 18:04:19 EST 2017

```Yes you are right. There is no need to add that line. I deleted it. But the
measured heap peak is still the same.

2017-03-01 0:00 GMT+01:00 Joseph Fox-Rabinovitz <jfoxrabinovitz at gmail.com>:

> For one thing, `C = np.empty(shape=(n,n), dtype='float64')` allocates 10^4
> extra elements before being immediately discarded.
>
>     -Joe
>
> On Tue, Feb 28, 2017 at 5:57 PM, Sebastian K <sebastiankaster at googlemail.
> com> wrote:
>
>> Yes it is true the execution time is much faster with the numpy function.
>>
>>  The Code for numpy version:
>>
>> def createMatrix(n):
>> Matrix = np.empty(shape=(n,n), dtype='float64')
>> for x in range(n):
>> for y in range(n):
>> Matrix[x, y] = 0.1 + ((x*y)%1000)/1000.0
>> return Matrix
>>
>>
>>
>> if __name__ == '__main__':
>> n = getDimension()
>> if n > 0:
>> A = createMatrix(n)
>> B = createMatrix(n)
>> C = np.empty(shape=(n,n), dtype='float64')
>> C = np.dot(A,B)
>>
>> #print(C)
>>
>> In the pure python version I am just implementing the multiplication with
>> three for-loops.
>>
>> Measured data with libmemusage:
>> dimension of matrix: 100x100
>> heap peak pure python3: 1060565
>> heap peakt numpy function: 4917180
>>
>>
>> 2017-02-28 23:17 GMT+01:00 Matthew Brett <matthew.brett at gmail.com>:
>>
>>> Hi,
>>>
>>> On Tue, Feb 28, 2017 at 2:12 PM, Sebastian K
>>> > For example a very simple algorithm is a matrix multiplication. I can
>>> see
>>> > that the heap peak is much higher for the numpy version in comparison
>>> to a
>>> > pure python 3 implementation.
>>> > The heap is measured with the libmemusage from libc:
>>> >
>>> >           heap peak
>>> >                   Maximum of all size arguments of malloc(3), all
>>> products
>>> >                   of nmemb*size of calloc(3), all size arguments of
>>> >                   realloc(3), length arguments of mmap(2), and new_size
>>> >                   arguments of mremap(2).
>>>
>>> Could you post the exact code you're comparing?
>>>
>>> I think you'll find that a naive Python 3 matrix multiplication method
>>> is much, much slower than the same thing with Numpy, with arrays of
>>> any reasonable size.
>>>
>>> Cheers,
>>>
>>> Matthew
>>> _______________________________________________
>>> NumPy-Discussion mailing list
>>> NumPy-Discussion at scipy.org
>>> https://mail.scipy.org/mailman/listinfo/numpy-discussion
>>>
>>
>>
>> _______________________________________________
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>>
>>
>
> _______________________________________________
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>
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