Fande : A small one, e.g., the size used by a sequential diagonal block for ilu preconditioner would work.
Thanks, Hong > > > On Tue, Mar 7, 2017 at 10:23 AM, Hong <[email protected]> wrote: > >> I checked >> MatILUFactorSymbolic_SeqBAIJ() and MatILUFactorSymbolic_SeqAIJ(), >> they are virtually same. Why the version for BAIJ is so much slower? >> I'll investigate it. >> > >> Fande, >> How large is your matrix? Is it possible to send us your matrix so I can >> test it? >> > > Thanks, Hong, > > It is a 3020875x3020875 matrix, and it is large. I can make a small one if > you like, but not sure it will reproduce this issue or not. > > Fande, > > > >> >> Hong >> >> >> On Mon, Mar 6, 2017 at 9:08 PM, Barry Smith <[email protected]> wrote: >> >>> >>> 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, >>> > >> >>> > > >>> > >>> > >>> >>> >> >
