UMFPACK out of memory

[Matthew Knepley]

On Tue, Oct 27, 2009 at 10:12 AM, francois pacull <fpacull at fluorem.com>wrote:

> Dear PETSc team,
>
> I have a few questions... During the resolution of a linear system in
> parallel, I am trying to apply a local LU solver to each of the SEQaij
> diagonal blocks of a MPIaij matrix partitioned with PARMETIS.
>
> - I started with MUMPS but it seems that it only works with unpartitioned
> aij matrices, is it really the case? Or could we use MUMPS to build an
> additive Schwarz preconditioner for example?
>

You can use MUMPS for the subproblem solver.


> - Then I tried UMFPACK. This works fine when the diagonal blocks (and the
> memory required to store the factors) are small but crashes when they are a
> little bit larger. For example with a "numeric final size" of 4203.7 MBytes,
>  I got the following message "ERROR: out of memory" while there was plenty
> of memory left in the computer. I tried either with the UMFPACK version 5.2,
> downloaded by PETSc, or with a manually installed version 5.4, linked to
> PETSc. Is this a behavior from UMFPACK that you already experienced?
>

Send all the error output. However, in oder to address more than 4G, you
will need 64-bit pointers.

- Since UMFPACK seemed to have a memory limit around 4096 MB,  I tried to
> install a PETSc version with the option "--with-64-bit-indices", however
> none of the partitioning packages could be compiled with this option
> (parmetis,chaco,jostle,party,scotch). Is there a way to compile PETSc with
> 64 bit indices AND a partitioning package?
>

Not that I know of.


> - Finally, I tried to modify the PETSc source code umfpack.c so that it
> would deal with 64 bit indices, but I only ended up so far with a
> segmentation violation message at the execution... Is it the only way I
> could use UMPACK with large sparse matrices?
>

Why not just upgrade to a 64-bit OS if you want to address so much memory on
a single machine?

  Matt


> Thank you,
> Regards,
> francois pacull.
>



-- 
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
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