[petsc-users] Sparse triangular solver

[Hong]

Hoang-Vu :
>
> If I do not need the full solver/factorization but just the backward subs,
> do i need any special treatment ? Is there a way to hint the solver to
> apply only the last step to reduce overhead ?
>
What do you mean " do not need the full solver/factorization"?
Do you need incomplete matrix factorization, e.g., ILU, instead of full
factorization?
The backward subs are steps AFTER matrix factorization.

Hong

On Mar 8, 2015 6:26 PM, "Barry Smith" <bsmith at mcs.anl.gov> wrote:
>
>>
>>   PETSc provides sparse parallel LU (and Cholesky) factorizations and
>> solves via the external packages SuperLU_Dist, MUMPS, and Pastix. You need
>> to first configure PETSc to use one or more of those packages for example
>> ./configure --download-superlu_dist --download-metis --download-parmetis.
>>
>>   It is generally best to use the linear solvers via the PETSc KSP
>> interface (even for direct solvers such as LU). So you create a KSP object,
>> provide the matrix object and call KSPSolve(). You can control the solver
>> used via the options database; to use the installed SuperLU_Dist you would
>> use -pc_type lu -pc_factor_mat_solver_package superlu_dist
>>
>>   The MatrixMarket format is no good for parallel computing so you must
>> first convert the file from MatrixMarket format to the PETSc binary format
>> (see
>> http://www.mcs.anl.gov/petsc/documentation/faq.html#sparse-matrix-ascii-format
>> ) and then  you can use MatLoad() to load the matrix in parallel and then
>> pass it to the KSP solver. For example
>> src/ksp/ksp/examples/tutorials/ex10.c does this.
>>
>>
>>   Barry
>>
>> > On Mar 8, 2015, at 6:08 PM, Hoang-Vu Dang <dang.hvu at gmail.com> wrote:
>> >
>> > Hi,
>> >
>> > I would like to use petcs to perform parallel backward/forward
>> substitution for sparse triangular matrices in a distributed memory cluster
>> (with MPI).
>> >
>> > Could someone provide me some pointers on how to do this or whether
>> petsc is good for this task ?
>> >
>> > I think there is MatSolve method, but unsure whether it supports good
>> algorithm for sparse triangular matrices and how to provide an input in a
>> MartrixMarket format / CSR format.
>> >
>> > Thank you
>> > Vu
>>
>>
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