[Numpy-discussion] Numpy Overhead

[Joseph Fox-Rabinovitz]

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
>> <sebastiankaster at 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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>
>
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