[petsc-users] reusing LU factorization?

[Hong Zhang]

MUMPS now supports parallel symbolic factorization. With petsc-3.4
interface, you can use runtime option

  -mat_mumps_icntl_28 <1>: ICNTL(28): use 1 for sequential analysis and
ictnl(7) ordering, or 2 for parallel analysis and ictnl(29) ordering
  -mat_mumps_icntl_29 <0>: ICNTL(29): parallel ordering 1 = ptscotch 2 =
parmetis

e.g, '-mat_mumps_icntl_28 2 -mat_mumps_icntl_29 2' activates parallel
symbolic factorization with pametis for matrix ordering.
Give it a try and let us know what you get.

Hong


On Tue, Jan 28, 2014 at 5:48 PM, Smith, Barry F. <bsmith at mcs.anl.gov> wrote:

>
> On Jan 28, 2014, at 5:39 PM, Matthew Knepley <knepley at gmail.com> wrote:
>
> > On Tue, Jan 28, 2014 at 5:25 PM, Tabrez Ali <stali at geology.wisc.edu>
> wrote:
> > Hello
> >
> > This is my observation as well (with MUMPS). The first solve (after
> assembly which is super fast) takes a few mins (for ~1 million unknowns on
> 12/24 cores) but from then on only a few seconds for each subsequent solve
> for each time step.
> >
> > Perhaps symbolic factorization in MUMPS is all serial?
> >
> > Yes, it is.
>
>    I missed this. I was just assuming a PETSc LU. Yes, I have no idea of
> relative time of symbolic and numeric for those other packages.
>
>   Barry
> >
> >   Matt
> >
> > Like the OP I often do multiple runs on the same problem but I dont know
> if MUMPS or any other direct solver can save the symbolic factorization
> info to a file that perhaps can be utilized in subsequent reruns to avoid
> the costly "first solves".
> >
> > Tabrez
> >
> >
> > On 01/28/2014 04:04 PM, Barry Smith wrote:
> > On Jan 28, 2014, at 1:36 PM, David Liu<daveliu at mit.edu>  wrote:
> >
> > Hi, I'm writing an application that solves a sparse matrix many times
> using Pastix. I notice that the first solves takes a very long time,
> >    Is it the first "solve" or the first time you put values into that
> matrix that "takes a long time"? If you are not properly preallocating the
> matrix then the initial setting of values will be slow and waste memory.
>  See
> http://www.mcs.anl.gov/petsc/petsc-current/docs/manualpages/Mat/MatXAIJSetPreallocation.html
> >
> >    The symbolic factorization is usually much faster than a numeric
> factorization so that is not the cause of the slow "first solve".
> >
> >     Barry
> >
> >
> >
> > while the subsequent solves are very fast. I don't fully understand
> what's going on behind the curtains, but I'm guessing it's because the very
> first solve has to read in the non-zero structure for the LU factorization,
> while the subsequent solves are faster because the nonzero structure
> doesn't change.
> >
> > My question is, is there any way to save the information obtained from
> the very first solve, so that the next time I run the application, the very
> first solve can be fast too (provided that I still have the same nonzero
> structure)?
> >
> >
> > --
> > No one trusts a model except the one who wrote it; Everyone trusts an
> observation except the one who made it- Harlow Shapley
> >
> >
> >
> >
> > --
> > 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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