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 >>> >>> >> >> _______________________________________________ >> NumPy-Discussion mailing list >> NumPy-Discussion@scipy.org >> https://mail.scipy.org/mailman/listinfo/numpy-discussion >> >> > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > https://mail.scipy.org/mailman/listinfo/numpy-discussion > >

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