Re: [boost] [gsoc18][ublas] Proposal to add advanced matrix operations

Subject: Re: [boost] [gsoc18][ublas] Proposal to add advanced matrix operations
From: SHIKHAR SRIVASTAVA (shikharsri1996_at_[hidden])
Date: 2018-01-22 18:03:00

Hi all,

Thanks a lot for your suggestions. It made a lot of things clearer. My
proposal was to add advanced matrix operations that were missing from
ublas.

>From the discussions it seems that it isn't a great idea to reimplement
everything in ublas itself as there can be other options giving much better
performance.

@karen - I am not specifically interested in machine learning. I want to
contribute to ublas for performance and more usability in applications.

@david - The discussions lead me to think if adding Openblas/LAPACK backend
for advanced matrix operation is a really good idea. It would boost
performance and make some hardware acceleration possible (depending on blas
implementation). It would be nice to have some interface with them. Can
this be a potential gsoc anyone would like to mentor?

Regards
Shikhar Srivastava

On 22-Jan-2018 10:59 PM, "Karen Shaeffer via Boost" <boost_at_[hidden]>
wrote:

> On Mon, Jan 22, 2018 at 11:16:55AM +0100, Hans Dembinski via Boost wrote:
> > Hi David, Rajaditya, Artyom, Shikhar,
> >
> > > On 21. Jan 2018, at 13:00, David Bellot via Boost <
> boost_at_[hidden]> wrote:
> > >
> > > - yes uBLAS is slow. Not only needs it a support for hardware
> acceleration
> > > but also it needs to be more modern (C++11/14/17) and its architecture
> > > could be much more improved. This would help integrating hardware
> > > acceleration, IMHO.
> >
> > I am surprised that no one here mentioned Eigen, an IMHO excellent
> high-level header-only C++ library to do matrix-vector calculations.
> >
> > http://eigen.tuxfamily.org/index.php?title=Main_Page <
> http://eigen.tuxfamily.org/index.php?title=Main_Page>
> >
> > It uses expression templates to re-structure the expressions written by
> users to fast optimised code. It supports both matrices with dimensions
> known at compile-time and at run-time, and supports mixed versions, e.g.
> one dimension fixed the other dynamic.
>
> Hello,
> Tensorflow uses Eigen.
>
> If Shikhar is most interested in machine learning libraries, the TensorFlow
> might have some GSOC projects.
>
> Karen.
>
> >
> > According to their own (admittedly, a bit outdated) benchmarks, they are
> extremely competitive, either on par or even beating $$$ closed-source
> libraries.
> >
> > http://eigen.tuxfamily.org/index.php?title=Benchmark <
> http://eigen.tuxfamily.org/index.php?title=Benchmark>
> >
> > Eigen tracks its own performance, so you don't need to worry about it
> getting slower, but perhaps the others have gotten faster in the meantime.
> >
> > http://eigen.tuxfamily.org/index.php?title=Performance_monitoring <
> http://eigen.tuxfamily.org/index.php?title=Performance_monitoring>
> >
> > I used the library in projects and it is very convenient. You just write
> your expressions as you would on paper and let Eigen generate the fastest
> possible code out of that. I only dislike that Eigen does not play well
> with "auto" since C++11, because they use the assignment to a matrix or
> vector to trigger the evaluation of the expression template.
> >
> > I haven't done it myself, but Eigen seems to have a good strategy for
> adding new features, too. People can start to develop their stuff in an
> "unsupported" subdirectory, which is not meant for production, but the code
> is nevertheless distributed with Eigen. This helps to exposes the
> experimental code to more adventurous users. Eventually, a project that
> passed the test of time can move from "unsupported" to the main library.
> >
> > Best regards,
> > Hans
> >
> > _______________________________________________
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