On 11/5/25 11:37, Nihar Bhardwaj Darbhamulla wrote:

In the parallel setup, I am testing with 214788 dofs, with 1, 2,4,8 and 16 processes, and the timings are attached below. As can be seen, the assembly scales perfectly well with doubling the processes, but it is the solve which ends up taking quite long. The direct solve is based on the following sub- sizes of dofs:
M1 - 66049
M2 - 132098
M3 - 16641

None of these sizes are prohibitively expensive for a Direct Solver in parallel. Given this premise, which directions should I potentially probe into.

Nihar:
without having looked at your code in detail:
* These are very small problems. I would not expect that a parallel solver can provide any kind of speedup on a problem this small -- you will be spending all of your time in communication and none in actual computations. You'll want to test with much larger problems -- a good rule of thumb is that parallel solvers only scale if you have perhaps 50k or 100k unknowns per MPI process; you should apply that to the smallest of the blocks you have, M3. So you'll have to make it at least ten times larger to even be relevant to 2 processes. * I believe that in your code, you compute the sparse direct LU decomposition every time the vmult() function is called for the preconditioner. That's inefficient. You will want to compute the decomposition only once, in the constructor of the DirectInverseMatrix, and then only *apply* the decomposition in each call to vmult. * The results you show indicate that your method leads to a roughly constant number of GMRES iterations. That's a good start. In order to find out which part of your implementation is leading to the growth in run time, put timer sections into the various parts of your preconditioner so that you get finer-grained information about your algorithm's individual components.

Best
 W.

--
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Wolfgang Bangerth          email:                 [email protected]
                           www: http://www.math.colostate.edu/~bangerth/

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