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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