[petsc-users] Matrix Construction Question

Adam Byrd adam1.byrd at gmail.com
Tue Jun 28 08:55:12 CDT 2011


Hi,

I'm rather new to PETSc and trying to work out the best way to create and
fill a large sparse matrix distributed over many processors. Currently, my
goal is to create a 3600x3600 matrix in units of 12x12 blocks with several
blocks on any given node. I'd like to create the matrix in such a way that
each node only holds the information in it's handful of blocks and not the
entire matrix. Eventually, this matrix is to be inverted (I know, inversion
should be avoided, but as this is a Hamiltonian matrix from which I need the
Green's function, I'm unaware of a way to forgo carrying out the inversion).
Additionally, the values will be changed slightly and the matrix will be
repeatedly inverted. It's structure will remain the same. In order to learn
how to do this is I am starting with a small 6x6 matrix broken into four 3x3
blocks and distributed one block per node. I've been able to create a local
3x3 matrix on each node, with it's own values, and with the global
row/column IDs correctly set to [0, 1, 2] or [3, 4, 5] depending on where
the block is in the matrix. My problem manifests when I try to create the
larger matrix from the individual smaller ones. When the matrix is
constructed I'm trying to use MatSetValues and having each node pass in it's
3x3 block. I end up with an error that the sum of local lengths 12x12 does
not match the global length 6x6. It appears as though this is from passing
in four 3x3s and the program interpreting that as a 12x12 instead of as a
6x6 with the blocks in a grid.

My question is then: is it possible to fill a matrix as a grid of blocks, or
can I only fill it in groups of rows or columns? Also, am I approaching this
problem the correct way, or are there more efficient ways of building this
matrix with the ultimate goal of inverting it?

I have included my copy of a modified example if it helps. I do apologize if
this is answered somewhere in the documentation, I have been unable to find
a solution.

Respectfully,
Adam
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