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 > >

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