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Tue, 28 Feb 2017 14:17:53 -0800

It would really help to see the code you are using in both cases as well as
some heap usage numbers...

    -Joe

On Tue, Feb 28, 2017 at 5:12 PM, Sebastian K <sebastiankas...@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) 
> <http://man7.org/linux/man-pages/man3/malloc.3.html>, all products
>                   of *nmemb***size* of calloc(3) 
> <http://man7.org/linux/man-pages/man3/calloc.3.html>, all *size* arguments of
>                   realloc(3) 
> <http://man7.org/linux/man-pages/man3/realloc.3.html>, *length* arguments of 
> mmap(2) <http://man7.org/linux/man-pages/man2/mmap.2.html>, and *new_size*
>                   arguments of mremap(2) 
> <http://man7.org/linux/man-pages/man2/mremap.2.html>.
>
> Regards
>
> Sebastian
>
>
> On 28 Feb 2017 11:03 p.m., "Benjamin Root" <ben.v.r...@gmail.com> wrote:
>
>> You are going to need to provide much more context than that. Overhead
>> compared to what? And where (io, cpu, etc.)? What are the size of your
>> arrays, and what sort of operations are you doing? Finally, how much
>> overhead are you seeing?
>>
>> There can be all sorts of reasons for overhead, and some can easily be
>> mitigated, and others not so much.
>>
>> Cheers!
>> Ben Root
>>
>>
>> On Tue, Feb 28, 2017 at 4:47 PM, Sebastian K <
>> sebastiankas...@googlemail.com> wrote:
>>
>>> Hello everyone,
>>>
>>> I'm interested in the numpy project and tried a lot with the numpy
>>> array. I'm wondering what is actually done that there is so much overhead
>>> when I call a function in Numpy. What is the reason?
>>> Thanks in advance.
>>>
>>> Regards
>>>
>>> Sebastian Kaster
>>>
>>> _______________________________________________
>>> NumPy-Discussion mailing list
>>> NumPy-Discussion@scipy.org
>>> https://mail.scipy.org/mailman/listinfo/numpy-discussion
>>>
>>>
>>
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>>
>>
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