Feimi,
Of course, I will make a pull request after the preconditioner is fully
commented & tested, although it was a really small addition to the code...
Excellent -- we are always glad for any kind of contribution, large or small.
I'm sorry I was not clear in explaining my problem. I had a block matrix to
solve, and the ILU is used for solving the inverse of the schur complement
(by applying ILU to an approximate of it as the preconditioner) in my DIY
preconditioner.
I tried different # of dofs as you suggested and I found it very weird: for
same case with different number of global refinement, I found only when I
have 26k dofs, the time with 2 cores is faster than 1 core, either coarser
or finer mesh causes solver to be slower in parallel (also tried
BlockJacobi and the results are almost the same). I wonder if there could
be any possibility that my communication has problem or the matrix is
already not distributed? Don't understand....
That's such a small problem. The fastest way to solve problems that are so
small is to use a direct solver. I'll point to this again:
It often comes down to how much communication you do. If your problem is
small, then there is not very much work to do for each process, and much
time is spent on communication between processes. A typical rule of thumb
is that your problem needs to have at least 50,000 to 100,000 unknowns per
process for computations to offset communication.
As to the concrete slowdown you see, I would suggest to also look at the
number of GMRES iterations you do. If the number of iterations you need
depends significantly on the number of cores, then you're in trouble. Good and
scalable methods must all result in a number of iterations that does not
depend on the number of cores in a significant way.
Best
W.
--
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Wolfgang Bangerth email: [email protected]
www: http://www.math.colostate.edu/~bangerth/
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