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 <[email protected]
> 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" <[email protected]> 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 <
>> [email protected]> 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
>>>
>>> _______________________________________________
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>>> [email protected]
>>> https://mail.scipy.org/mailman/listinfo/numpy-discussion
>>>
>>>
>>
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