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

*Post by Benjamin Root*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 <

*Post by Sebastian K*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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