> I do not have an inner inner solve, I just use a single SparseILU<double>
> initialized with system_matrix.block(0,0) to approximate its inverse. It is
> the trick used in step-22.

You might get SparseILU(0) to work with an inner solver like in
step-56. It might not be fast, but it should at least work.

> Looks like the direct solver is the best choice except for multigrid.

Really? For the medium mesh ILU is a lot faster (16s vs 237s) and uses
less memory. If you extrapolate from there, large problems will not
only require a lot of memory but also be slower. This is because a
full LU factorization takes O(n^3) operations in general.

> The
> size of problem will certainly be limited on a single machine because of the
> memory usage. But what if I parallelize my code using distributed-memory?

There are special direct solvers that work okay for medium sized
problems in parallel.

> Does dealii have a parallel version of direct solver? My ultimate problem
> size will have less than 10 million dofs. The cluster I have access to has
> 16G memory on each node, I can probably use 64 nodes or even more. Will I be
> able to solve such a problem using direct solver for
> system_matrix.block(0,0)?

You can try parallel direct solvers like Mumps, SuperLU, or KLU with
interfaces exposed through PETSc or Trilinos. I assume they will work
reasonable well up to that size.

>  I have no experience in multigrid. Forgive me if I am asking the wrong
> question: is algebraic multigrid method a black box comparing to the
> geometric one? By black box I mean it requires no additional information
> other than the matrix itself hence no additional coding.

Correct. See step-55 or step-40 for example. The problem is tuning AMG
parameters to get good performance especially if your problem is not
Laplace-like.

-- 
Timo Heister
http://www.math.clemson.edu/~heister/

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