Thanks for sharing this, Barry. I haven't had time to read their paper, but
it looks worth a read.
Hong, since many machine-learning or data-mining problems can be cast as
linear algebra problems (several examples involving eigenproblems come to
mind), I'm guessing that there must be several
Hong writes:
> It was implemented a decade ago for small to medium matrices.
> Your matrix structure reveals the worst part of outer product.
>
> After discussing it with Barry, we decided to replace the algorithm (not
> API) using At=A^T and C=At*B for sequential
Jed:
> Hong writes:
>
> > Jed,
> > I reproduced what you observed.
> > 30x time is spent on numerical C = At*B.
> > We compute A^T*B using outer product C=(A^T)[:,i]*B[i,:].
> > For your large matrix A: rows=574902, cols=184446,
> > outer product takes long long time to
Hong writes:
> Jed,
> I reproduced what you observed.
> 30x time is spent on numerical C = At*B.
> We compute A^T*B using outer product C=(A^T)[:,i]*B[i,:].
> For your large matrix A: rows=574902, cols=184446,
> outer product takes long long time to insert values to C.
>
> We
Great news! According to their papers, MLSVM works only in serial. I am not
sure what is stopping them using PETSc in parallel.
Btw, are there any other cases that use PETSc for machine learning?
Hong (Mr.)
> On Sep 21, 2017, at 1:02 PM, Barry Smith wrote:
>
>
> From:
Mark,
It doesn't look like this a PETSc example (in a PETSc branch). If you can
send me full instructions for building/compiling a code that has this behavior
please send me all info so I can run it and debug it.
Barry
Otherwise it is just speculation city.
> On Sep 18, 2017, at
> On Sep 18, 2017, at 7:59 PM, Mark Adams wrote:
>
> Also, I tested this on my Mac (this output) and on Cori at NERSC and the
> behavior looked identical.
>
> On Mon, Sep 18, 2017 at 8:44 PM, Mark Adams wrote:
> I get this strange error when I use GAMG, in
From: Ilya Safro isa...@g.clemson.edu
Date: September 17, 2017
Subject: MLSVM 1.0, Multilevel Support Vector Machines
We are pleased to announce the release of MLSVM 1.0, a library of fast
multilevel algorithms for training nonlinear support vector machine
models on large-scale datasets. The
Jed,
I reproduced what you observed.
30x time is spent on numerical C = At*B.
We compute A^T*B using outer product C=(A^T)[:,i]*B[i,:].
For your large matrix A: rows=574902, cols=184446,
outer product takes long long time to insert values to C.
We may use At=A^T and C=At*B instead.
Hong
On Thu,
Jed :
> ~jedbrown/ceres_solver_iteration_001_A.petsc on mcs.anl.gov.
>
I got this file. How many matrices in this file? How to load them?
It seems the first matrix is rectangular.
Hong
>
> The issue is that MatTransposeMatMult is implemented using sparse outer
> products which is quite
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