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@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@gmail.com>:

Hi,

On Tue, Feb 28, 2017 at 2:12 PM, Sebastian K
<sebastiankaster@googlemail.com> wrote:
> Thank you for your answer.
> 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

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