Thanks. Even the symbolic is slower for BAIJ. I don't like that, it definitely should not be since it is (at least should be) doing a symbolic factorization on a symbolic matrix 1/11th the size! Keep us informed.
> On Mar 6, 2017, at 5:44 PM, Kong, Fande <[email protected]> wrote: > > Thanks, Barry, > > Log info: > > AIJ: > > MatSolve 850 1.0 8.6543e+00 4.2 3.04e+09 1.8 0.0e+00 0.0e+00 > 0.0e+00 0 41 0 0 0 0 41 0 0 0 49594 > MatLUFactorNum 25 1.0 1.7622e+00 2.0 2.04e+09 2.1 0.0e+00 0.0e+00 > 0.0e+00 0 26 0 0 0 0 26 0 0 0 153394 > MatILUFactorSym 13 1.0 2.8002e-01 2.9 0.00e+00 0.0 0.0e+00 0.0e+00 > 0.0e+00 0 0 0 0 0 0 0 0 0 0 0 > > BAIJ: > > MatSolve 826 1.0 1.3016e+01 1.7 1.42e+10 1.8 0.0e+00 0.0e+00 > 0.0e+00 1 29 0 0 0 1 29 0 0 0 154617 > MatLUFactorNum 25 1.0 1.5503e+01 2.0 3.55e+10 2.1 0.0e+00 0.0e+00 > 0.0e+00 1 67 0 0 0 1 67 0 0 0 303190 > MatILUFactorSym 13 1.0 5.7561e-01 1.8 0.00e+00 0.0 0.0e+00 0.0e+00 > 0.0e+00 0 0 0 0 0 0 0 0 0 0 0 > > It looks like both MatSolve and MatLUFactorNum are slower. > > I will try your suggestions. > > Fande > > On Mon, Mar 6, 2017 at 4:14 PM, Barry Smith <[email protected]> wrote: > > Note also that if the 11 by 11 blocks are actually sparse (and you don't > store all the zeros in the blocks in the AIJ format) then then AIJ non-block > factorization involves less floating point operations and less memory access > so can be faster than the BAIJ format, depending on "how sparse" the blocks > are. If you actually "fill in" the 11 by 11 blocks with AIJ (with zeros maybe > in certain locations) then the above is not true. > > > > On Mar 6, 2017, at 5:10 PM, Barry Smith <[email protected]> wrote: > > > > > > This is because for block size 11 it is using calls to LAPACK/BLAS for > > the block operations instead of custom routines for that block size. > > > > Here is what you need to do. For a good sized case run both with > > -log_view and check the time spent in > > MatLUFactorNumeric, MatLUFactorSymbolic and in MatSolve for AIJ and BAIJ. > > If they have a different number of function calls then divide by the > > function call count to determine the time per function call. > > > > This will tell you which routine needs to be optimized first either > > MatLUFactorNumeric or MatSolve. My guess is MatSolve. > > > > So edit src/mat/impls/baij/seq/baijsolvnat.c and copy the function > > MatSolve_SeqBAIJ_15_NaturalOrdering_ver1() to a new function > > MatSolve_SeqBAIJ_11_NaturalOrdering_ver1. Edit the new function for the > > block size of 11. > > > > Now edit MatLUFactorNumeric_SeqBAIJ_N() so that if block size is 11 it > > uses the new routine something like. > > > > if (both_identity) { > > if (b->bs == 11) > > C->ops->solve = MatSolve_SeqBAIJ_11_NaturalOrdering_ver1; > > } else { > > C->ops->solve = MatSolve_SeqBAIJ_N_NaturalOrdering; > > } > > > > Rerun and look at the new -log_view. Send all three -log_view to use at > > this point. If this optimization helps and now > > MatLUFactorNumeric is the time sink you can do the process to > > MatLUFactorNumeric_SeqBAIJ_15_NaturalOrdering() to make an 11 size block > > custom version. > > > > Barry > > > >> On Mar 6, 2017, at 4:32 PM, Kong, Fande <[email protected]> wrote: > >> > >> > >> > >> On Mon, Mar 6, 2017 at 3:27 PM, Patrick Sanan <[email protected]> > >> wrote: > >> On Mon, Mar 6, 2017 at 1:48 PM, Kong, Fande <[email protected]> wrote: > >>> Hi All, > >>> > >>> I am solving a nonlinear system whose Jacobian matrix has a block > >>> structure. > >>> More precisely, there is a mesh, and for each vertex there are 11 > >>> variables > >>> associated with it. I am using BAIJ. > >>> > >>> I thought block ILU(k) should be more efficient than the point-wise > >>> ILU(k). > >>> After some numerical experiments, I found that the block ILU(K) is much > >>> slower than the point-wise version. > >> Do you mean that it takes more iterations to converge, or that the > >> time per iteration is greater, or both? > >> > >> The number of iterations is very similar, but the timer per iteration is > >> greater. > >> > >> > >>> > >>> Any thoughts? > >>> > >>> Fande, > >> > > > >
