It is not surprising. BCGS uses less memory for the Krylov vectors, but that might be a small fraction of the total memory used (considering your matrix and GAMG). FGMRES(30) needs 60 work vectors (2 per iteration). If you're using a linear (non-iterative) preconditioner, then you don't need a flexible method -- plain GMRES should be fine. FGMRES uses the unpreconditioned norm, which you can also get via -ksp_type gmres -ksp_norm_type unpreconditioned.
This classic paper shows that for any class of nonsymmetric Krylov method, there are matrices in which that method outperforms every other method by at least sqrt(N). https://epubs.siam.org/doi/10.1137/0613049 Marco Cisternino <marco.cistern...@optimad.it> writes: > Good Morning, > I usually solve a non-symmetric discretization of the Poisson equation using > GAMG+FGMRES. > In the last days I tried to use BCGS in place of FGMRES, still using GAMG as > preconditioner. > No problem in finding the solution but I'm experiencing something I didn't > expect. > The test case is a 25 millions cells domain with Dirichlet and Neumann > boundary conditions. > Both the solvers are able to solve the problem with an increasing number of > MPI processes, but: > > * FGMRES is about 25% faster than BCGS for all the processes number > * Both solvers have the same scalability from 48 to 384 processes > * Both solvers almost use the same amount of memory (FGMRES use a > restart=30) > Am I wrong expecting less memory consumption and more performance from BCGS > with respect to FGMRES? > Thank you in advance for any help. > > Best regards, > Marco Cisternino
