Before writing matrix-free stuff, however, I would strongly recommend
profiling
(e.g., -log_summary). Krylov vectors might be eating up quite a bit of
memory,
as might the preconditioner (e.g., (I)LU will keep the factors around,
Hypre will copy
the matrix, etc.). Convergence of a coupled matrix-free solve might be
rather slow,
and, if you are using a Krylov method, that will likely blow up the size of
the Krylov
space (unless you use an aggressive restart value, but that will further
impact
convergence). That might undo any memory savings from ditching the Jacobian
and the preconditioner data structures.

Cheers,
Dmitry.


On Sun, Nov 17, 2013 at 8:47 PM, Roy Stogner <[email protected]>wrote:

>
> On Sun, 17 Nov 2013, Ataollah Mesgarnejad wrote:
>
> > I'm just trying to cut out unnecessary parts of my Code. My main problem
> is the memory with my 3-D elasticity + fracture
> > problem. ParallelMesh helps quite a bit but I would like to make the
> memory footprint even smaller and hopefully make things
> > run a bit faster in the process. I would appreciate any suggestions.
>
> If you're already using ParallelMesh then your next big memory hog is
> probably the sparse matrix, which suggests one drastic option: switch
> to a Jacobian-free solve.  IIRC we've got support for PETSc "shell
> matrix" interfaces, so you can implement a Jacobian-vector-product
> function instead of allocating a matrix.
> ---
> Roy
>
>
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