[Numpy-discussion] Linking other libm-Implementation

[Nils Becker]

Hi all,

I wanted to know if there is any sane way to build numpy while linking to a
different implementation of libm?
A drop-in replacement for libm (e.g. openlibm) should in principle work, I
guess, but I did not manage to actually make it work. As far as I
understand the build code, setting MATHLIB=openlibm should suffice, but it
did not. The build works fine, but in the end when running numpy apparently
the functions of the system libm.so are used. I could not verify this
directly (as I do not know how) but noticed that there is no performance
difference between the builds - while there is one with pure C programs
linked against libm and openlibm.
Using amdlibm would require some work as the functions are prefixed with
"_amd", I guess? Using intels libimf should work when using intels
compiler, but I did not try this. With gcc I did not get it to work.

A quite general question: At the moment the performance and the accuracy of
the base mathematical functions depends on the platform and
libm-Implementation of the system. Although there are functions defined in
npy_math, they are only used as fall-backs, if they are not provided by a
library. (correct me if I am wrong here)
Is there some plan to change this in the future and provide defined
behaviour (specified accuracy and/or speed) across platforms? As I
understood it Julia started openlibm for this reason (which is based on
fdlibm/msun, same as npy_math).

Cheers
Nils
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