You said “the local number of columns also needs to add up to the global
number of columns” but my desired layout is local_num_columns ==
global_num_columns.
No, that's not what you want. You need to partition the columns in the same
way as you partition the vectors you want to multiply the matrix by. We
generally partition the vectors in the same way as the rows of the matrix, and
so then you also have to choose the same partitioning for the columns of the
matrix.
I only press this because I get the similar dimension error when I tried to
use Slepc to solve the test matrix so maybe my initial sparse matrix dimension
is wrong (or my matrix). Please take a look at the file I am attaching. It’s
the same code you ran before but I added a section using Slepc to solve the
eigenvalue problem. It’s bare bones and produces the dimension error just
like before. This code works for one processor and given the error, this is
not a surprise. For 2 processors, I get “Sum of local length 64 does not
equal global length 128, my local length 64”.
If you look at the backtrace, at which point do you get this error? Is it
really out of the SLEPC functions? I would suspect that it comes out of the
set-up phase for your matrix.
Best
W.
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Wolfgang Bangerth email: [email protected]
www: http://www.math.colostate.edu/~bangerth/
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