I'm solving the schrodinger equation in a basis state method and looking at adding the azimuthal quantum numbers (the m's) to my basis so I can look at circularly polarized light.
However, when I do this, I'll need to do some sort of convergence study to make sure I add enough of them. The way my code is structured, it will probably be easier to just remove rows and columns from a bigger matrix, instead of adding them to a smaller matrix. However, depending on the way I structure the matrix, I could end up removing all the values (or a significant portion of them) from a processor when I do that. Speaking more on that, is the "PETSC_DECIDE" way of finding the local distribution smart in any way? or does it just assume an even distribution of values? (I assume that it assumes a even distribution of values before the assembly, but does it redistribute during assembly?) Thanks, -Andrew On May 15, 2012, at 6:40 AM, Jed Brown wrote: > On Mon, May 14, 2012 at 10:39 PM, Andrew Spott <andrew.spott at gmail.com> > wrote: > That is what I figure. > > I'm curious though if you need to manually determine the local row > distribution after you do that. (for example, say you completely remove all > the values from the local range of one processor? that processor wouldn't be > utilized unless you redistribute the matrix) > > What sizes and method are we talking about? Usually additional (compact) > basis functions only make sense to add to one of a small number of processes. -------------- next part -------------- An HTML attachment was scrubbed... URL: <http://lists.mcs.anl.gov/pipermail/petsc-users/attachments/20120515/aeab303a/attachment.htm>
