Michele : Superlu_dist LU is used for coarse grid PC, which likely produces a zero-pivot. Run your code with '-info |grep pivot' to verify.
Hong Hi Matt, > > the ksp_view output was an attachment to my previous email. > Here it is: > > KSP Object: 1 MPI processes > type: cg > maximum iterations=10000 > tolerances: relative=1e-08, absolute=1e-50, divergence=10000. > left preconditioning > using nonzero initial guess > using UNPRECONDITIONED norm type for convergence test > PC Object: 1 MPI processes > type: mg > MG: type is MULTIPLICATIVE, levels=4 cycles=v > Cycles per PCApply=1 > Using Galerkin computed coarse grid matrices > Coarse grid solver -- level ------------------------------- > KSP Object: (mg_coarse_) 1 MPI processes > type: preonly > maximum iterations=1, initial guess is zero > tolerances: relative=1e-05, absolute=1e-50, divergence=10000. > left preconditioning > using NONE norm type for convergence test > PC Object: (mg_coarse_) 1 MPI processes > type: lu > LU: out-of-place factorization > tolerance for zero pivot 2.22045e-14 > using diagonal shift on blocks to prevent zero pivot [INBLOCKS] > matrix ordering: nd > factor fill ratio given 0., needed 0. > Factored matrix follows: > Mat Object: 1 MPI processes > type: seqaij > rows=16, cols=16 > package used to perform factorization: superlu_dist > total: nonzeros=0, allocated nonzeros=0 > total number of mallocs used during MatSetValues calls =0 > SuperLU_DIST run parameters: > Process grid nprow 1 x npcol 1 > Equilibrate matrix TRUE > Matrix input mode 0 > Replace tiny pivots FALSE > Use iterative refinement FALSE > Processors in row 1 col partition 1 > Row permutation LargeDiag > Column permutation METIS_AT_PLUS_A > Parallel symbolic factorization FALSE > Repeated factorization SamePattern > linear system matrix = precond matrix: > Mat Object: 1 MPI processes > type: seqaij > rows=16, cols=16 > total: nonzeros=72, allocated nonzeros=72 > total number of mallocs used during MatSetValues calls =0 > not using I-node routines > Down solver (pre-smoother) on level 1 ------------------------------- > KSP Object: (mg_levels_1_) 1 MPI processes > type: richardson > Richardson: damping factor=1. > maximum iterations=2 > tolerances: relative=1e-05, absolute=1e-50, divergence=10000. > left preconditioning > using nonzero initial guess > using NONE norm type for convergence test > PC Object: (mg_levels_1_) 1 MPI processes > type: sor > SOR: type = local_symmetric, iterations = 1, local iterations = 1, > omega = 1. > linear system matrix = precond matrix: > Mat Object: 1 MPI processes > type: seqaij > rows=64, cols=64 > total: nonzeros=304, allocated nonzeros=304 > total number of mallocs used during MatSetValues calls =0 > not using I-node routines > Up solver (post-smoother) same as down solver (pre-smoother) > Down solver (pre-smoother) on level 2 ------------------------------- > KSP Object: (mg_levels_2_) 1 MPI processes > type: richardson > Richardson: damping factor=1. > maximum iterations=2 > tolerances: relative=1e-05, absolute=1e-50, divergence=10000. > left preconditioning > using nonzero initial guess > using NONE norm type for convergence test > PC Object: (mg_levels_2_) 1 MPI processes > type: sor > SOR: type = local_symmetric, iterations = 1, local iterations = 1, > omega = 1. > linear system matrix = precond matrix: > Mat Object: 1 MPI processes > type: seqaij > rows=256, cols=256 > total: nonzeros=1248, allocated nonzeros=1248 > total number of mallocs used during MatSetValues calls =0 > not using I-node routines > Up solver (post-smoother) same as down solver (pre-smoother) > Down solver (pre-smoother) on level 3 ------------------------------- > KSP Object: (mg_levels_3_) 1 MPI processes > type: richardson > Richardson: damping factor=1. > maximum iterations=2 > tolerances: relative=1e-05, absolute=1e-50, divergence=10000. > left preconditioning > using nonzero initial guess > using NONE norm type for convergence test > PC Object: (mg_levels_3_) 1 MPI processes > type: sor > SOR: type = local_symmetric, iterations = 1, local iterations = 1, > omega = 1. > linear system matrix = precond matrix: > Mat Object: 1 MPI processes > type: seqaij > rows=1024, cols=1024 > total: nonzeros=5056, allocated nonzeros=5056 > total number of mallocs used during MatSetValues calls =0 > has attached null space > not using I-node routines > Up solver (post-smoother) same as down solver (pre-smoother) > linear system matrix = precond matrix: > Mat Object: 1 MPI processes > type: seqaij > rows=1024, cols=1024 > total: nonzeros=5056, allocated nonzeros=5056 > total number of mallocs used during MatSetValues calls =0 > has attached null space > not using I-node routines > > > Michele > > > > > On Wed, 2016-02-10 at 19:37 -0600, Matthew Knepley wrote: > > On Wed, Feb 10, 2016 at 7:33 PM, Michele Rosso <mro...@uci.edu> wrote: > > Hi, > > I encountered the following error while solving a symmetric positive > defined system: > > Linear solve did not converge due to DIVERGED_PCSETUP_FAILED iterations 0 > PCSETUP_FAILED due to SUBPC_ERROR > > This error appears only if I use the optimized version of both petsc and > my code ( compiler: gfortran, flags: -O3 ). > It is weird since I am solving a time-dependent problem and everything, > i.e. results and convergence rate, are as expected until the above error > shows up. If I run both petsc and my code in debug mode, everything goes > smooth till the end of the simulation. > However, if I reduce the ksp_rtol, even the debug run fails, after running > as expected for a while, because of a KSP_DIVERGED_INDEFINITE_PC . > The options I am using are: > > -ksp_type cg > -ksp_norm_type unpreconditioned > -ksp_rtol 1e-8 > -ksp_lag_norm > -ksp_initial_guess_nonzero yes > -pc_type mg > -pc_mg_galerkin > -pc_mg_levels 4 > -mg_levels_ksp_type richardson > -mg_coarse_ksp_constant_null_space > -mg_coarse_pc_type lu > -mg_coarse_pc_factor_mat_solver_package superlu_dist > -options_left > > I attached a copy of ksp_view. I am currently using petsc-master (last > updated yesterday). > I would appreciate any suggestion on this matter. > > > > I suspect you have a nonlinear PC. Can you send the output of -ksp_view? > > > > Matt > > > > Thanks, > Michele > > > > > > > > > > > > > -- > > What most experimenters take for granted before they begin their > experiments is infinitely more interesting than any results to which their > experiments lead. > -- Norbert Wiener > > >