[petsc-users] Sparse triangular solver

[Hoang-Vu Dang]

Thank you for the prompt reply. That is very helpful.

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 ?

Vu
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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