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

Dp Docs sdpan21 at gmail.com
Wed Mar 11 18:20:03 EDT 2015


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? 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
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)?​​

Thanks and regards,

--
Durgesh Pandey,
IIIT-Hyderabad,India.
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/numpy-discussion/attachments/20150312/47c44851/attachment.html>


More information about the NumPy-Discussion mailing list