Ben,
These two lines
added_sp.block(0, 1).copy_from(col);
added_sp.block(1, 0).copy_from(row);
account for about 60% of the bottleneck (considering only the runtime
for the piece of code in the original post).
These I can give back to you:
https://github.com/dealii/dealii/pull/6081
:-)
The other 40% of the
bottleneck is from the line
added_Jacobian_matrix.block(1, 0).reinit(
single,
partitioning,
added_sp.block(1, 0),
MPI_COMM_WORLD,
false);
Interestingly, the reinit for the block(0,1) matrix is negligible. I
assume this is related to block(1,0) living on a single processor, and
block(0,1) being distributed. All the other lines have negligible
contribution to the runtime.
That is indeed interesting.
The reinit() function here for the Trilinos matrix looks innocuous. I
suspect that the time is lost in Trilinos, but I've learned to be
cautious about such claims. The function reinit_matrix() in which you
ultimately spend the time is in source/lac/trilinos_sparse_matrix.cc
starting at line 461. If you wanted to time its individual parts to find
out what is happening, that would be the place for it.
The reason I fill the row and col vectors with ones (correct me if I'm
misunderstanding) is so the sparsity pattern will have non-zero entries.
The vectors I use to fill the (0,1) and (1,0) matrices are dense, so
this seemed like the safest choice.
Yes. Any nonzero entry would have been fine. One is a good value absent
any other needs.
Best
W.
--
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Wolfgang Bangerth email: bange...@colostate.edu
www: http://www.math.colostate.edu/~bangerth/
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