It may not happen with one rank. Or it may. With more than one rank one may run a code and think it is working correctly but that does not mean the PetscSF is correct. It is generating "extra" degrees of freedom for these vertices each associated with a different edge and not properly connecting them. You can tell by doing a full DMView on the dmplex and PetscSF and seeing more roots than there should be listed. Hence the numerics will be wrong even though it does not crash or complain.

On Dec 1, 2021, at 4:37 PM, Abhyankar, Shrirang G <[email protected]> wrote:

Barry,

“Is there anything we can do to support having multiple edges between the same two vertices”

Some of my power grid datasets have multiple edges between the same two vertices and I’ve not faced an issue with DMNetwork. However, all the data was read on rank 0 only (and then distributed).

Maybe the issue is with the edges being passed on different ranks?

Thanks, Shri From: Barry Smith <[email protected] <mailto:[email protected]>> Date: Wednesday, December 1, 2021 at 3:19 PM To: "Knepley, Matthew G (VISIT)" <[email protected] <mailto:[email protected]>>, PETSc Development <[email protected] <mailto:[email protected]>>, "Abhyankar, Shrirang G" <[email protected] <mailto:[email protected]>>, "Zhang, Hong" <[email protected] <mailto:[email protected]>>, Getnet Betrie <[email protected] <mailto:[email protected]>> Subject: DMPLEX cannot support two different edges for the same two vertices, hence DMPLEX cannot?

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Matt,

If DMPlexBuildFromCellListParallel() is called with two edges that have the same two vertices what will happen? It looks like it ends up with an incorrect PetscSF if the two edges are passed on different ranks. Hence the DMPLEX is not valid and produces garbage.

Neurons can be connected to themselves which seems to be breaking DMPLEX and hence DMNETWORK.

Is there anything we can do to support having multiple edges between the same two vertices? If not is there a way we can have DMPlexBuildFromCellListParallel() generate an error automatically if there are such extra edges in the input data.

Thanks

Barry

In this work, the neurons are represented by vertices in the network and each synapse is a graph edge.