On Jul 26, 2010, at 3:53 PM, Barry Smith wrote: > > ./configure an optimized version of PETSc (that is with the ./ > configure flag of --with-debugging=0) and run with -log_summary to > get a summary of where it is spending the time. This will give you a > better idea of why it is taking so long.
Does my log summary means the finite difference Jacobian approximation is not good? Should I write analytic jacobian function(that will be a huge amount of work)? -------------- next part -------------- A non-text attachment was scrubbed... Name: Picture 3.png Type: image/png Size: 23543 bytes Desc: not available URL: <http://lists.mcs.anl.gov/pipermail/petsc-users/attachments/20100726/48f5111a/attachment-0003.png> -------------- next part -------------- -------------- next part -------------- A non-text attachment was scrubbed... Name: Picture 4.png Type: image/png Size: 67304 bytes Desc: not available URL: <http://lists.mcs.anl.gov/pipermail/petsc-users/attachments/20100726/48f5111a/attachment-0004.png> -------------- next part -------------- -------------- next part -------------- A non-text attachment was scrubbed... Name: Picture 5.png Type: image/png Size: 44994 bytes Desc: not available URL: <http://lists.mcs.anl.gov/pipermail/petsc-users/attachments/20100726/48f5111a/attachment-0005.png> -------------- next part -------------- > > Barry > > On Jul 26, 2010, at 2:49 PM, Xuan YU wrote: > >> Hi, >> >> I am using TS solving a nonlinear problem. I created an approximate >> data structure for Jacobian matrix to be used with matcoloring, my >> MatFDColoringView is like this: >> <Picture 1.png> >> >> But the speed of code is too slow than what I expected. Only 10 >> time step costs 11seconds! >> >> What's wrong with my code? How can I speed up? >> >> Thanks! >> >> This is the ts_view result. >> >> TS Object: >> type: beuler >> maximum steps=100 >> maximum time=10 >> total number of nonlinear solver iterations=186 >> total number of linear solver iterations=423 >> SNES Object: >> type: ls >> line search variant: SNESLineSearchCubic >> alpha=0.0001, maxstep=1e+08, minlambda=1e-12 >> maximum iterations=50, maximum function evaluations=10000 >> tolerances: relative=1e-08, absolute=1e-50, solution=1e-08 >> total number of linear solver iterations=1 >> total number of function evaluations=19 >> KSP Object: >> type: gmres >> GMRES: restart=30, using Classical (unmodified) Gram-Schmidt >> Orthogonalization with no iterative refinement >> GMRES: happy breakdown tolerance 1e-30 >> maximum iterations=10000, initial guess is zero >> tolerances: relative=1e-05, absolute=1e-50, divergence=10000 >> left preconditioning >> using PRECONDITIONED norm type for convergence test >> PC Object: >> type: ilu >> ILU: out-of-place factorization >> 0 levels of fill >> tolerance for zero pivot 1e-12 >> using diagonal shift to prevent zero pivot >> matrix ordering: natural >> factor fill ratio given 1, needed 1 >> Factored matrix follows: >> Matrix Object: >> type=seqaij, rows=1838, cols=1838 >> package used to perform factorization: petsc >> total: nonzeros=8464, allocated nonzeros=8464 >> total number of mallocs used during MatSetValues calls =0 >> not using I-node routines >> linear system matrix = precond matrix: >> Matrix Object: >> type=seqaij, rows=1838, cols=1838 >> total: nonzeros=8464, allocated nonzeros=9745 >> total number of mallocs used during MatSetValues calls =37 >> not using I-node routines >> >> >> Xuan YU >> xxy113 at psu.edu >> >> >> >> > > Xuan YU xxy113 at psu.edu