[petsc-users] reusing LU factorization?

[Matthew Knepley]

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.

  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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