Subject: Re: [boost] [gsoc18][ublas] Proposal to add advanced matrix operations
From: Rajaditya Mukherjee (rajaditya.mukherjee_at_[hidden])
Date: 2018-01-20 23:05:54
I did the Eigenvalue codes in GSOC 2015 but it had some stability issues
with one specific test matrix and hence it wasn't merged. I can commit to
merging them well before the start date of this years GSOC since I have
some time between now and march to work on this if someone wants to take
the work forward.
*Rajaditya Mukherjee *
*6th Year Graduate Student*
Dept. of Computer Science and Engineering
The Ohio State University
Tel :- +1-(614)-271-4439
email :- rajaditya.mukherjee_at_[hidden],mukherjee.62_at_[hidden]
On Sat, Jan 20, 2018 at 4:30 PM, SHIKHAR SRIVASTAVA via Boost <
> Hi Artyom,
> Thank you for the insight. Given the metrics, it surely looks like even
> implementing those operations in ublas won't do any good.
> I will look into the blas/LAPACK backend for ublas. I will look for a
> possible proposal which can be completed in the given GSOC time frame.
> Then again there is this question, is there any mentor available for this
> project who can refine some of the requirements ?
> Shikhar Srivastava
> On 21-Jan-2018 12:36 AM, "Artyom Beilis via Boost" <boost_at_[hidden]>
> > On Fri, Jan 19, 2018 at 8:37 AM, SHIKHAR SRIVASTAVA via Boost
> > <boost_at_[hidden]> wrote:
> > > Hi everyone,
> > >
> > > I am a 4th year undergraduate student pursuing a degree in Computer
> > Science
> > > and Engineering. I have strong programming experience in C++ through
> > > internships, self projects and programming events. I wish to be a part
> > > gsoc18 under boost and am particularly interested in the linear algebra
> > > library Boost.ublas.
> > >
> > > The ublas library can be made more useful for Machine Learning
> > applications
> > > like recommendation systems, clustering and classification, pattern
> > > recognition by adding some operations required in those.
> > > I propose to add advanced matrix operations to ublas including -
> > >
> > > 1. Triangular Factorisation (LU and Cholesky)
> > > 2. Orthogonal Factorisation (QR and QL)
> > > 3. Operations to find Singular Value lists
> > > 4. Eigenvalue algorithms
> > > 5. Singular Value Decomposition (SVD)
> > > 6. Jordan Decomposition
> > > 7. Schur Decomposition
> > > 8. Hessenberg Decomposition
> > >
> > >
> > Hello,
> > I'm sorry to disappoint you but uBlas is not nearby useful library for
> > real world
> > machine learning applications because it exceptionally slow in comparison
> > to
> > "real" BLAS libraries being used for such applications like
> > OpenBLAS, Atlas or proprietary MKL.
> > They all give you what you are talking about, they are tested
> > very well and exceptionally fast.
> > I mean uBlas is by 2-3 orders of magnitude slower than OpenBLAS or
> > Atlas even for small matrices
> > 8x8 GEMM - uBlas slower by 50 times than OpenBlas and 30 times slower
> > Atlas
> > 128x128 GEMM - uBlas slower by 600 times thatn OpenBlas and 50 times
> > slower than Atlas.
> > So I don't think investing in implementation of algorithm that are
> > already implemented in LAPACK
> > libraries and have way better performance would actually will be
> > helpful for real world applications.
> > What you CAN do is to provide *Blas/LAPACK based backend for uBlas...
> > Regards,
> > Artyom
> > _______________________________________________
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