superlu_dist options

[Fredrik Bengzon]

Hong,
Thank you for the suggestions, but I have looked at the EPS and KSP 
objects and I can not find anything wrong. The problem is that it takes 
longer to solve with 4 cpus than with 2 so the scalability seems to be 
absent when using superlu_dist. I have stored my mass and stiffness 
matrix in the mpiaij format and just passed them on to slepc. When using 
the petsc iterative krylov solvers i see 100% workload on all processors 
but when i switch to superlu_dist only two cpus seem to do the whole 
work of LU factoring. I don't want to use the krylov solver though since 
it might cause slepc not to converge.
Regards,
Fredrik

Hong Zhang wrote:
>
> Run your code with '-eps_view -ksp_view' for checking
> which methods are used
> and '-log_summary' to see which operations dominate
> the computation.
>
> You can turn on parallel symbolic factorization
> with '-mat_superlu_dist_parsymbfact'.
>
> Unless you use large num of processors, symbolic factorization
> takes ignorable execution time. The numeric
> factorization usually dominates.
>
> Hong
>
> On Fri, 8 May 2009, Fredrik Bengzon wrote:
>
>> Hi Petsc team,
>> Sorry for posting questions not really concerning the petsc core, but 
>> when I run superlu_dist from within slepc I notice that the load 
>> balance is poor. It is just fine during assembly (I use Metis to 
>> partition my finite element mesh) but when calling the slepc solver 
>> it dramatically changes. I use superlu_dist as solver for the 
>> eigenvalue iteration. My question is: can this have something to do 
>> with the fact that the option 'Parallel symbolic factorization' is 
>> set to false? If so, can I change the options to superlu_dist using 
>> MatSetOption for instance? Also, does this mean that superlu_dist is 
>> not using parmetis to reorder the matrix?
>> Best Regards,
>> Fredrik Bengzon
>>
>>
>