[petsc-users] Very slow SVD with SLEPC

[Rakesh Halder]

Hi Jose,

I'm only interested in part of the singular triplets, so those algorithms
work for me. I tried using ScaLAPACK and it gives similar performance to
Lanczos and Cross, so it's still very slow.... I'm still having memory
issues with LAPACK and Elemental is giving me an error message indicating
that the operation isn't supported for rectangular matrices.

With regards to scaLAPACK or any other solver, I'm wondering if there's
some settings to use with the SVD object to ensure optimal performance.

Thanks,

Rakesh

On Sun, Nov 15, 2020 at 2:59 PM Jose E. Roman <jroman at dsic.upv.es> wrote:

> Rakesh,
>
> The solvers you mention are not intended for computing the full SVD, only
> part of the singular triplets. In the latest version (3.14) there are now
> solvers that wrap external packages for parallel dense computations:
> ScaLAPACK and Elemental.
>
> Jose
>
>
> > El 15 nov 2020, a las 20:48, Matthew Knepley <knepley at gmail.com>
> escribió:
> >
> > On Sun, Nov 15, 2020 at 2:18 PM Rakesh Halder <rhalder at umich.edu> wrote:
> > Hi all,
> >
> > A program I'm writing involves calculating the SVD of a large, dense N
> by n matrix (N ~= 150,000, n ~=10,000). I've used the different SVD solvers
> available through SLEPc, including the cross product, lanczos, and method
> available through the LAPACK library. The cross product and lanczos methods
> take a very long time to compute the SVD (around 7-8 hours on one
> processor) while the solver using the LAPACK library runs out of memory. If
> I write this matrix to a file and solve the SVD using MATLAB or python
> (numPy) it takes around 10 minutes. I'm wondering if there's a much cheaper
> way to solve the SVD.
> >
> > This seems suspicious, since I know numpy just calls LAPACK, and I am
> fairly sure that Matlab does as well. Do the machines that you
> > are running on have different amounts of RAM?
> >
> >   Thanks,
> >
> >      Matt
> >
> > Thanks,
> >
> > Rakesh
> >
> >
> > --
> > What most experimenters take for granted before they begin their
> experiments is infinitely more interesting than any results to which their
> experiments lead.
> > -- Norbert Wiener
> >
> > https://www.cse.buffalo.edu/~knepley/
>
>
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