Re: [Numpy-discussion] Introductory mail and GSoc Project "Vector math library integration"

[Sturla Molden]

On 11/03/15 23:20, Dp Docs wrote:

> ​​​So we are supposed to integrate just one of these libraries?

As a Mac user I would be annoyed if we only supported MKL and not 
Accelerate Framework. AMD LibM should be supported too.

MKL is non-free, but we use it for BLAS and LAPACK. AMD LibM is non-free 
in a similar manner. Acelerate Framework (vecLib) is a part of Apple's 
operating systems. You can abstract out the differences.

Eigen is C++. We do not use C++ in NumPy, only C, Python and some 
Cython. C++ and Fortran can be used in SciPy, but not in NumPy.


Apple's reference is here:

https://developer.apple.com/library/mac/documentation/Performance/Conceptual/vecLib/index.html


AMD's information is here:

http://developer.amd.com/tools-and-sdks/cpu-development/libm/

(Vector math functions seem to be moved from ACML to LibM)


Intel's reference is here:

https://software.intel.com/sites/products/documentation/doclib/iss/2013/mkl/mklman/GUID-59EC4B87-29C8-4FB4-B57C-D269E6364954.htm



Sturla

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Re: [Numpy-discussion] Introductory mail and GSoc Project "Vector math library integration"

[Dp Docs]

On Thu, Mar 12, 2015 at 2:01 AM, Daπid  wrote:
>
> On 11 March 2015 at 16:51, Dp Docs  wrote:
>> On Wed, Mar 11, 2015 at 7:52 PM, Sturla Molden 
wrote:
>> >
>> > There are at least two ways to proceed here. One is to only use vector
>> > math when strides and alignment allow it.
>> I didn't got it. can you explain in detail?
>
>
> One example, you can create a numpy 2D array using only the odd columns
of a matrix.
>
> odd_matrix = full_matrix[::2, ::2]
>
> This is just a view of the original data, so you save the time and the
memory of making a copy. The drawback is that you trash
​>​
memory locality, as the elements are not contiguous in memory. If the
memory is guaranteed to be contiguous, a compiler can apply
​>​
extra optimisations, and this is what vector libraries usually assume. What
I think Sturla is suggesting with "when strides and aligment
​>​
allow it" is to use the fast version if the array is contiguous, and fall
back to the present implementation otherwise. Another would be to
​>​
make an optimally aligned copy, but that could eat up whatever time we save
from using the faster library, and other problems.
>
> The difficulty with Numpy's strides is that they allow so many ways of
manipulating the data... (alternating elements, transpositions, different
precisions...).
>
>>
>> I think the actual problem is not "to choose which library to
integrate", it is how to integrate these libraries? as I have seen the code
​>>​
base and been told the current implementation uses the c math library, Can
we just use the current  implementation and whenever it
​>>​
is calling C Maths functions, we will replace by these above fast library
functions?Then we have to modify the Numpy library (which
​>>​
usually get imported for maths operation) by using some if else conditions
like first work with the faster one  and if it is not available
​>>​
the look for the Default one.
>
>
> At the moment, we are linking to whichever LAPACK is avaliable at compile
time, so no need for a runtime check. I guess it could
​>​
(should?) be the same.
​I didn't understand this. I was asking about let say I have chosen one
faster library, now I need to integrate this​ in *some way *without
changing the default functionality so that when Numpy will import "from
numpy import *",it should be able to access the integrated libraries
functions as well as default libraries functions, What should we be that* some
way*?​ Even at the Compile, it need to decide that which Function it is
going to use, right? It have been discussed above about integration of MKL
libraries but when MKL is not available on the hardware Architecture, will
the above library support as default library? if yes, then the Above
discussed integration method may be the required one for integration in
this project, right?
Can you please tell me a bit more or provide some link related to that?​
Availability of these faster Libraries depends on the Hardware
Architectures etc. or availability of hardware Resources in a System?
because if it is later one, this newly integrated library will support
operations some time while sometimes not? I believe it's the first one but
it is better to clear any type of confusion. For example, assuming
availability of Hardware means later one,  let say if library A needed the
A1 for it's support and A1 is busy then it will not be able to support the
operation. Meanwhile, library B, needs Support of hardware type B1 , and
it's not Busy then it will support these operations. What I want to say is
Assuming the Availability of faster lib. means availability of hardware
Resources in a System at a particular time when we want to do operation,
it's totally unpredictable and Availability of these resources will be
Random and even worse, if it take a bit extra time between compile and
running, and that h/d resource have been allocated to other process in the
meantime then it would be very problematic to use these operations. So this
leads to think that Availability of lib. means type of h/d architecture
whether it supports or not that lib. Since there are many kind of h/d
architecture and it is not the case that one library support all these
architectures (though it may be), So we need to integrate more than one
lib. for providing support to all kind of architecture (in ideal case which
will make it to be a very big project).
>
>>
>> Moreover, I have Another Doubt also. are we suppose to integrate just
one fast library or more than one so that if one is not available, look for
the second one and if second is not available then either go to default are
look for the third one if available?
>> Are we suppose to think like this: Let say "exp" is faster in sleef
library so integrate sleef library for this operation and let say "sin" is
faster in any other library, so integrate that library for sin operation? I
mean, it may be possible that different operations are faster in different
libraries So the implementation should be operation oriented or just
int

Re: [Numpy-discussion] Introductory mail and GSoc Project "Vector math library integration"

[Dp Docs]

On Wed, Mar 11, 2015 at 10:34 PM, Gregor Thalhammer <
gregor.thalham...@gmail.com> wrote:
>
>
> On the scipy mailing list I also answered to Amine, who is also
interested in this proposal.
​​ Can you provide the link of that discussion? I am getting trouble in
searching that.

​>​
Long time ago I wrote a package that
​
>​
provides fast math functions (ufuncs) for numpy, using Intel’s MKL/VML
library, see  https://github.com/geggo/uvml and my comments
​>​
there. This code could be easily ported to use other vector math libraries.

​When MKL is not available for a System, will this integration work with
default numpy maths functions?
​
​>​
 Would be interesting to evaluate other possibilities. Due to
​>​
the fact that MKL is non-free, there are concerns to use it with numpy,
​>​
although e.g. numpy and scipy using the MKL LAPACK
​>​
routines are used frequently (Anaconda or Christoph Gohlkes  binaries).
>
> You can easily inject the fast math ufuncs into numpy, e.g. with
set_numeric_ops() or np.sin = vml.sin.

​Can you explain in a bit detail or provide a link where i can see it?​

​Thanks for your valuable suggestion.​

--

Durgesh Pandey,
IIIT-Hyderabad,India.
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Re: [Numpy-discussion] Introductory mail and GSoc Project "Vector math library integration"

[Daπid]

On 11 March 2015 at 16:51, Dp Docs  wrote:

>
>
> On Wed, Mar 11, 2015 at 7:52 PM, Sturla Molden 
> wrote:
> >
> ​Hi sturla,
> Thanks for suggestion.​
>
> > There are several vector math libraries NumPy could use, e.g. MKL/VML,
> > Apple Accelerate (vecLib), ACML, and probably others.
>
> ​Are these libraries fast enough in comparison to C maths libraries?​
>

These are the fastest beast out there, written in C, Assembly, and arcane
incantations.


> > There are at least two ways to proceed here. One is to only use vector
> > math when strides and alignment allow it.
> ​I didn't got it. can you explain in detail?​
>

One example, you can create a numpy 2D array using only the odd columns of
a matrix.

odd_matrix = full_matrix[::2, ::2]

This is just a view of the original data, so you save the time and the
memory of making a copy. The drawback is that you trash memory locality, as
the elements are not contiguous in memory. If the memory is guaranteed to
be contiguous, a compiler can apply extra optimisations, and this is what
vector libraries usually assume. What I think Sturla is suggesting with
"when strides and aligment allow it" is to use the fast version if the
array is contiguous, and fall back to the present implementation otherwise.
Another would be to make an optimally aligned copy, but that could eat up
whatever time we save from using the faster library, and other problems.

The difficulty with Numpy's strides is that they allow so many ways of
manipulating the data... (alternating elements, transpositions, different
precisions...).


> I think the actual problem is not "to choose which library to integrate",
> it is how to integrate these libraries? as I have seen the code base and
> been told the current implementation uses the c math library, Can we just
> use the current  implementation and whenever it is calling ​C Maths
> functions, we will replace by these above fast library functions?Then we
> have to modify the Numpy library (which usually get imported for maths
> operation) by using some if else conditions like first work with the faster
> one  and if it is not available the look for the Default one.
>

At the moment, we are linking to whichever LAPACK is avaliable at compile
time, so no need for a runtime check. I guess it could (should?) be the
same.


> Moreover, I have Another Doubt also. are we suppose to integrate just one
> fast library or more than one so that if one is not available, look for the
> second one and if second is not available then either go to default are
> look for the third one if available?
> Are we suppose to think like this: Let say "exp" is faster in sleef
> library so integrate sleef library for this operation and let say "sin" is
> faster in any other library, so integrate that library for sin operation? I
> mean, it may be possible that different operations are faster in different
> libraries So the implementation should be operation oriented or just
> integrate one complete library?​Thanks​
>

Which one is faster depends on the hardware, the version of the library,
and even the size of the problem:

http://s3.postimg.org/wz0eis1o3/single.png

I don't think you can reliably decide ahead of time which one should go for
each operation. But, on the other hand, whichever one you go for will
probably be fast enough for anyone using Python. Most of the work here is
adapting Numpy's machinery to dispatch a call to the vector library, once
that is ready, adding another one will hopefully be easier. At least, at
the moment Numpy can use one of several linear algebra packages (MKL,
ATLAS, CBLAS...) and they are added, I think, without too much pain (but
maybe I am just far away from the screams of whoever did it).


/David.
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[Numpy-discussion] SIMD programming in Cython

[Sturla Molden]

So I just learned a new trick. This is a very nice one which can be nice 
to know about, so I thought I should share this:

https://groups.google.com/forum/#!msg/cython-users/nTnyI7A6sMc/a6_GnOOsLuQJ



Regards,
Sturla

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Re: [Numpy-discussion] Introductory mail and GSoc Project "Vector math library integration"

[Francesc Alted]

2015-03-08 21:47 GMT+01:00 Dp Docs :

> Hi all,
> I am a CS 3rd Undergrad. Student from an Indian Institute (III T). I
> believe I am good in Programming languages like C/C++, Python as I
> have already done Some Projects using these language as a part of my
> academics. I really like Coding (Competitive as well as development).
> I really want to get involved in Numpy Development Project and want to
> take  "Vector math library integration" as a part of my project. I
> want to here any idea from your side for this project.
> Thanks For your time for reading this email and responding back.
>

As Sturla and Gregor suggested, there are quite a few attempts to solve
this shortcoming in NumPy.  In particular Gregor integrated MKL/VML support
in numexpr quite a long time ago, and when combined with my own
implementation of pooled threads (behaving better than Intel's
implementation in VML), then the thing literally flies:

 https://github.com/pydata/numexpr/wiki/NumexprMKL

numba is also another interesting option and it shows much better compiling
times than the integrated compiler in numexpr.  You can see a quick
comparison about expected performances between numexpr and numba:

http://nbviewer.ipython.org/gist/anonymous/4117896

In general, numba wins for small arrays, but numexpr can achieve very good
performance for larger ones.  I think there are interesting things to
discover in both projects, as for example, how they manage memory in order
to avoid temporaries or how they deal with unaligned data efficiently.  I
would advise to look at existing docs and presentations explaining things
in more detail too.

All in all, I would really love to see such a vector math library support
integrated in NumPy because frankly, I don't have bandwidth for maintaining
numexpr anymore (and I am afraid that nobody else would jump in this ship
;).

Good luck!

Francesc


>
> My IRCnickname: dp
>
> Real Name: Durgesh Pandey.
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>



-- 
Francesc Alted
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Re: [Numpy-discussion] Introductory mail and GSoc Project "Vector math library integration"

[Gregor Thalhammer]

> Am 08.03.2015 um 21:47 schrieb Dp Docs :
> 
> Hi all,
> I am a CS 3rd Undergrad. Student from an Indian Institute (III T). I
> believe I am good in Programming languages like C/C++, Python as I
> have already done Some Projects using these language as a part of my
> academics. I really like Coding (Competitive as well as development).
> I really want to get involved in Numpy Development Project and want to
> take  "Vector math library integration" as a part of my project. I
> want to here any idea from your side for this project.
> Thanks For your time for reading this email and responding back.
> 

On the scipy mailing list I also answered to Amine, who is also interested in 
this proposal. Long time ago I wrote a package that provides fast math 
functions (ufuncs) for numpy, using Intel’s MKL/VML library, see  
https://github.com/geggo/uvml  and my comments 
there. This code could be easily ported to use other vector math libraries. 
Would be interesting to evaluate other possibilities. Due to the fact that MKL 
is non-free, there are concerns to use it with numpy, although e.g. numpy and 
scipy using the MKL LAPACK routines are used frequently (Anaconda or Christoph 
Gohlkes  binaries). 

You can easily inject the fast math ufuncs into numpy, e.g. with 
set_numeric_ops() or np.sin = vml.sin. 

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Re: [Numpy-discussion] Introductory mail and GSoc Project "Vector math library integration"

[Dp Docs]

On Wed, Mar 11, 2015 at 7:52 PM, Sturla Molden 
wrote:
>
​Hi sturla,
Thanks for suggestion.​

> There are several vector math libraries NumPy could use, e.g. MKL/VML,
> Apple Accelerate (vecLib), ACML, and probably others.

​Are these libraries fast enough in comparison to C maths libraries?​


> They all suffer from requiring dense arrays and specific array
> alignments, whereas NumPy arrays have very flexible strides and flexible
> alignment.
​>​
 NumPy also has ufuncs and gufuncs as a complicating factor.
​I don't think the project is supposed to modify the existing functionality
as whenever the Faster libraries ​will be unavailable, it should use the
default libraries.
>
> There are at least two ways to proceed here. One is to only use vector
> math when strides and alignment allow it.
​I didn't got it. can you explain in detail?​
​>​
 The other is to build a vector
> math library specifically for NumPy arrays and (g)ufuncs. The latter you
> will most likely not be able to do in a summer.
​I have also came up with this approach but I am confused a bit with this
approach.​
>
> You should also consider Numba
> and Numexpr. They have some support for
> vector math libraries too.
>
​I will look into this.

I think the actual problem is not "to choose which library to integrate",
it is how to integrate these libraries? as I have seen the code base and
been told the current implementation uses the c math library, Can we just
use the current  implementation and whenever it is calling ​C Maths
functions, we will replace by these above fast library functions?
Then we have to modify the Numpy library (which usually get imported for
maths operation) by using some if else conditions like first work with the
faster one  and if it is not available the look for the Default one.

Moreover, I have Another Doubt also. are we suppose to integrate just one
fast library or more than one so that if one is not available, look for the
second one and if second is not available then either go to default are
look for the third one if available?

Are we suppose to think like this: Let say "exp" is faster in sleef library
so integrate sleef library for this operation and let say "sin" is faster
in any other library, so integrate that library for sin operation? I mean,
it may be possible that different operations are faster in different
libraries So the implementation should be operation oriented or just
integrate one complete library?
​Thanks​



--
Durgesh Pandey,
IIIT-Hyderabad,India.
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Re: [Numpy-discussion] Introductory mail and GSoc Project "Vector math library integration"

[Sturla Molden]

There are several vector math libraries NumPy could use, e.g. MKL/VML, 
Apple Accelerate (vecLib), ACML, and probably others.

They all suffer from requiring dense arrays and specific array 
alignments, whereas NumPy arrays have very flexible strides and flexible 
alignment. NumPy also has ufuncs and gufuncs as a complicating factor.

There are at least two ways to proceed here. One is to only use vector 
math when strides and alignment allow it. The other is to build a vector 
math library specifically for NumPy arrays and (g)ufuncs. The latter you 
will most likely not be able to do in a summer.

You should also consider Numba and Numexpr. They have some support for 
vector math libraries too.


Sturla



On 08/03/15 21:47, Dp Docs wrote:
> Hi all,
> I am a CS 3rd Undergrad. Student from an Indian Institute (III T). I
> believe I am good in Programming languages like C/C++, Python as I
> have already done Some Projects using these language as a part of my
> academics. I really like Coding (Competitive as well as development).
> I really want to get involved in Numpy Development Project and want to
> take  "Vector math library integration" as a part of my project. I
> want to here any idea from your side for this project.
> Thanks For your time for reading this email and responding back.
>
> My IRCnickname: dp
>
> Real Name: Durgesh Pandey.
>


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