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

Ralf Gommers ralf.gommers at gmail.com
Thu Mar 12 03:11:23 EDT 2015

On Wed, Mar 11, 2015 at 11:20 PM, Dp Docs <sdpan21 at gmail.com> wrote:

> On Thu, Mar 12, 2015 at 2:01 AM, Daπid <davidmenhur at gmail.com> wrote:
> >
> > On 11 March 2015 at 16:51, Dp Docs <sdpan21 at gmail.com> wrote:
> >> On Wed, Mar 11, 2015 at 7:52 PM, Sturla Molden <sturla.molden at gmail.com>
> 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?

Indeed, it should probably work similar to how BLAS/LAPACK functions are
treated now. So you can support multiple libraries in numpy (pick only one
to start with of course), but at compile time you'd pick the one to use.
Then that library gets always called under the hood, i.e. no new public
functions/objects in numpy but only improved performance of existing ones.

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?

Not HW resources I'd think. Looking at http://www.yeppp.info, it supports
all commonly used cpus/instruction sets.
As long as the accuracy of the library is OK this should not be noticeable
to users except for the difference in performance.

> 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
> 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).
> >
> ​​​So we are supposed to integrate just one of these libraries?(rest will
> use default if they didn't support) ​MKL seems to be good but as it have
> been discussed above that it's non-free and it have been integrated also,
> can you suggest any other library which at least approximate MKL in a
> better way? Though Eigen seems to be good, but it's seems to be worse in
> middle ranges. can you provide any link which provide comparative
> information about all available vector libraries(Free)?​​

The idea on the GSoC page suggests http://www.yeppp.info/ or SLEEF (
http://shibatch.sourceforge.net/). Based on those websites I'm 99.9% sure
that yeppp is a better bet. At least its benchmarks say that it's faster
than MKL. As for the project, Julian (who'd likely be the main mentor) has
already indicated when suggesting the idea that he has no interest in a
non-free library:
http://comments.gmane.org/gmane.comp.python.numeric.general/56933. So Yeppp
+ the build architecture to support multiple libraries later on would
probably be a good target.

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