Re: [Numpy-discussion] Using library-specific headers

2016-09-29 Thread Robert McLeod
Pavlyk,

NumExpr optionally includes MKL's VML at compile-time.  You may want to
look at its implementation.  From what I recall it relies on a function in
a bootstrapped __config__.py to determine if MKL is present.

Robert

On Thu, Sep 29, 2016 at 7:27 PM, Pavlyk, Oleksandr <
oleksandr.pav...@intel.com> wrote:

> Hi Julian,
>
> Thank you very much for the response. It appears to work.
>
> I work on "Intel Distribution for Python" at Intel Corp. This question was
> motivated by work needed to
> prepare pull requests with our changes/optimizations to numpy source code.
> In particular, the numpy.random_intel package
>
>https://mail.scipy.org/pipermail/numpy-discussion/2016-June/075693.html
>
> relies on MKL, but its potential inclusion in numpy should not break the
> build if MKL is unavailable.
>
> Also our benchmarking was pointing at Numpy's sequential memory copying as
> a bottleneck.
> I am working to open a pull request into the main trunk of numpy to take
> advantage of multithreaded
> MKL's BLAS dcopy function to do memory copying in parallel for
> sufficiently large sizes.
>
> Related to numpy.random_inter, I noticed that the randomstate package,
> which extends numpy.random was
> not being made a part of numpy, but rather published on PyPI as a
> stand-alone module. Does that mean that
> the community decided against  including it in numpy's codebase? If so, I
> would appreciate if someone could
> elaborate on or point me to the reasoning behind that decision.
>
> Thank you,
> Oleksandr
>
>
>
> -Original Message-
> From: NumPy-Discussion [mailto:numpy-discussion-boun...@scipy.org] On
> Behalf Of Julian Taylor
> Sent: Thursday, September 29, 2016 8:10 AM
> To: numpy-discussion@scipy.org
> Subject: Re: [Numpy-discussion] Using library-specific headers
>
> On 09/27/2016 11:09 PM, Pavlyk, Oleksandr wrote:
> > Suppose I would like to take advantage of some functions from MKL in
> > numpy C source code, which would require to use
> >
> >
> >
> > #include "mkl.h"
> >
> >
> >
> > Ideally this include line must not break the build of numpy when MKL
> > is not present, so my initial approach was to use
> >
> >
> >
> > #if defined(SCIPY_MKL_H)
> >
> > #include "mkl.h"
> >
> > #endif
> >
> >
> >
> > Unfortunately, this did not work when building with gcc on a machine
> > where MKL is present on default LD_LIBRARY_PATH, because then the
> > distutils code was setting SCIPY_MKL_H preprocessor variable, even
> > though mkl headers are not on the C_INCLUDE_PATH.
> >
> >
> >
> > What is the preferred solution to include an external library header
> > to ensure that code-base continues to build in most common cases?
> >
> >
> >
> > One approach I can think of is to set a preprocessor variable, say
> > HAVE_MKL_HEADERS in numpy/core/includes/numpy/config.h depending on an
> > outcome of building of a simple _configtest.c using
> > config.try_compile(), like it is done in numpy/core/setup.py //
> >
> > / /
> >
> > Is there a simpler, or a better way?
> >
>
> hi,
> you could put the header into OPTIONAL_HEADERS in
> numpy/core/setup_common.py. This will define HAVE_HEADERFILENAME_H for you
> but this will not check that the corresponding the library actually exists
> and can be linked.
> For that SCIPY_MKL_H is probably the right macro, though its name is
> confusing as it does not check for the header presence ...
>
> Can you tell us more about what from mkl you are attempting to add and for
> what purpos, e.g. is it something that should go into numpy proper or just
> for personal/internal use?
>
> cheers,
> Julian
>
>
>
>
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-- 
Robert McLeod, Ph.D.
Center for Cellular Imaging and Nano Analytics (C-CINA)
Biozentrum der Universität Basel
Mattenstrasse 26, 4058 Basel
Work: +41.061.387.3225
robert.mcl...@unibas.ch
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robbmcl...@gmail.com
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Re: [Numpy-discussion] Using library-specific headers

2016-09-29 Thread Robert Kern
On Thu, Sep 29, 2016 at 6:27 PM, Pavlyk, Oleksandr <
oleksandr.pav...@intel.com> wrote:

> Related to numpy.random_inter, I noticed that the randomstate package,
which extends numpy.random was
> not being made a part of numpy, but rather published on PyPI as a
stand-alone module. Does that mean that
> the community decided against  including it in numpy's codebase? If so, I
would appreciate if someone could
> elaborate on or point me to the reasoning behind that decision.

No, we are just working out the API and the extensibility machinery in a
separate package before committing to backwards compatibility.

--
Robert Kern
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Re: [Numpy-discussion] Using library-specific headers

2016-09-29 Thread Pavlyk, Oleksandr
Hi Julian, 

Thank you very much for the response. It appears to work. 

I work on "Intel Distribution for Python" at Intel Corp. This question was 
motivated by work needed to 
prepare pull requests with our changes/optimizations to numpy source code. 
In particular, the numpy.random_intel package

   https://mail.scipy.org/pipermail/numpy-discussion/2016-June/075693.html

relies on MKL, but its potential inclusion in numpy should not break the build 
if MKL is unavailable. 

Also our benchmarking was pointing at Numpy's sequential memory copying as a 
bottleneck. 
I am working to open a pull request into the main trunk of numpy to take  
advantage of multithreaded 
MKL's BLAS dcopy function to do memory copying in parallel for sufficiently 
large sizes.

Related to numpy.random_inter, I noticed that the randomstate package, which 
extends numpy.random was 
not being made a part of numpy, but rather published on PyPI as a stand-alone 
module. Does that mean that 
the community decided against  including it in numpy's codebase? If so, I would 
appreciate if someone could 
elaborate on or point me to the reasoning behind that decision.

Thank you,
Oleksandr



-Original Message-
From: NumPy-Discussion [mailto:numpy-discussion-boun...@scipy.org] On Behalf Of 
Julian Taylor
Sent: Thursday, September 29, 2016 8:10 AM
To: numpy-discussion@scipy.org
Subject: Re: [Numpy-discussion] Using library-specific headers

On 09/27/2016 11:09 PM, Pavlyk, Oleksandr wrote:
> Suppose I would like to take advantage of some functions from MKL in 
> numpy C source code, which would require to use
>
>
>
> #include "mkl.h"
>
>
>
> Ideally this include line must not break the build of numpy when MKL 
> is not present, so my initial approach was to use
>
>
>
> #if defined(SCIPY_MKL_H)
>
> #include "mkl.h"
>
> #endif
>
>
>
> Unfortunately, this did not work when building with gcc on a machine 
> where MKL is present on default LD_LIBRARY_PATH, because then the 
> distutils code was setting SCIPY_MKL_H preprocessor variable, even 
> though mkl headers are not on the C_INCLUDE_PATH.
>
>
>
> What is the preferred solution to include an external library header 
> to ensure that code-base continues to build in most common cases?
>
>
>
> One approach I can think of is to set a preprocessor variable, say 
> HAVE_MKL_HEADERS in numpy/core/includes/numpy/config.h depending on an 
> outcome of building of a simple _configtest.c using 
> config.try_compile(), like it is done in numpy/core/setup.py //
>
> / /
>
> Is there a simpler, or a better way?
>

hi,
you could put the header into OPTIONAL_HEADERS in numpy/core/setup_common.py. 
This will define HAVE_HEADERFILENAME_H for you but this will not check that the 
corresponding the library actually exists and can be linked.
For that SCIPY_MKL_H is probably the right macro, though its name is confusing 
as it does not check for the header presence ...

Can you tell us more about what from mkl you are attempting to add and for what 
purpos, e.g. is it something that should go into numpy proper or just for 
personal/internal use?

cheers,
Julian




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Re: [Numpy-discussion] Using library-specific headers

2016-09-29 Thread Julian Taylor

On 09/27/2016 11:09 PM, Pavlyk, Oleksandr wrote:

Suppose I would like to take advantage of some functions from MKL in
numpy C source code, which would require to use



#include “mkl.h”



Ideally this include line must not break the build of numpy when MKL is
not present, so my initial approach was to use



#if defined(SCIPY_MKL_H)

#include “mkl.h”

#endif



Unfortunately, this did not work when building with gcc on a machine
where MKL is present on default LD_LIBRARY_PATH, because then the
distutils code was setting SCIPY_MKL_H preprocessor variable, even
though mkl headers are not on the C_INCLUDE_PATH.



What is the preferred solution to include an external library header to
ensure that code-base continues to build in most common cases?



One approach I can think of is to set a preprocessor variable, say
HAVE_MKL_HEADERS in numpy/core/includes/numpy/config.h depending on an
outcome of building of a simple _configtest.c using
config.try_compile(), like it is done in numpy/core/setup.py //

/ /

Is there a simpler, or a better way?



hi,
you could put the header into OPTIONAL_HEADERS in 
numpy/core/setup_common.py. This will define HAVE_HEADERFILENAME_H for 
you but this will not check that the corresponding the library actually 
exists and can be linked.
For that SCIPY_MKL_H is probably the right macro, though its name is 
confusing as it does not check for the header presence ...


Can you tell us more about what from mkl you are attempting to add and 
for what purpos, e.g. is it something that should go into numpy proper 
or just for personal/internal use?


cheers,
Julian
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[Numpy-discussion] Using library-specific headers

2016-09-27 Thread Pavlyk, Oleksandr
Suppose I would like to take advantage of some functions from MKL in numpy C 
source code, which would require to use

#include "mkl.h"

Ideally this include line must not break the build of numpy when MKL is not 
present, so my initial approach was to use

#if defined(SCIPY_MKL_H)
#include "mkl.h"
#endif

Unfortunately, this did not work when building with gcc on a machine where MKL 
is present on default LD_LIBRARY_PATH, because then the distutils code was 
setting SCIPY_MKL_H preprocessor variable, even though mkl headers are not on 
the C_INCLUDE_PATH.

What is the preferred solution to include an external library header to ensure 
that code-base continues to build in most common cases?

One approach I can think of is to set a preprocessor variable, say 
HAVE_MKL_HEADERS in numpy/core/includes/numpy/config.h depending on an outcome 
of building of a simple _configtest.c using config.try_compile(), like it is 
done in numpy/core/setup.py

Is there a simpler, or a better way?

Thank you,
Oleksandr

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