Hi Matt,
Yes. The matrix is 450000x450000 sparse. The hypre takes hundreds of
iterates, not for all but in most of the timesteps. The matrix is not
well conditioned, with nonzero entries range from 1.0e-29 to 1.0e2. I
also made double check if there is anything wrong in the parallel
version, however, the matrix is the same with sequential version except
some round error which is relatively very small. Usually for those not
well conditioned matrix, direct solver should be faster than iterative
solver, right? But when I use the sequential iterative solver with ILU
prec developed almost 20 years go by others, the solver converge fast
with appropriate factorization level. In other words, when I use 24
processor using hypre, the speed is almost the same as as the old
sequential iterative solver using 1 processor.
I use most of the default configuration for the general case with pretty
good speedup. And I am not sure if I miss something for this problem.
Thanks,
Danyang
On 17-05-24 11:12 AM, Matthew Knepley wrote:
On Wed, May 24, 2017 at 12:50 PM, Danyang Su <danyang...@gmail.com
<mailto:danyang...@gmail.com>> wrote:
Hi Matthew and Barry,
Thanks for the quick response.
I also tried superlu and mumps, both work but it is about four
times slower than ILU(dt) prec through hypre, with 24 processors I
have tested.
You mean the total time is 4x? And you are taking hundreds of
iterates? That seems hard to believe, unless you are dropping
a huge number of elements.
When I look into the convergence information, the method using
ILU(dt) still takes 200 to 3000 linear iterations for each newton
iteration. One reason is this equation is hard to solve. As for
the general cases, the same method works awesome and get very good
speedup.
I do not understand what you mean here.
I also doubt if I use hypre correctly for this case. Is there
anyway to check this problem, or is it possible to increase the
factorization level through hypre?
I don't know.
Matt
Thanks,
Danyang
On 17-05-24 04:59 AM, Matthew Knepley wrote:
On Wed, May 24, 2017 at 2:21 AM, Danyang Su <danyang...@gmail.com
<mailto:danyang...@gmail.com>> wrote:
Dear All,
I use PCFactorSetLevels for ILU and PCFactorSetFill for other
preconditioning in my code to help solve the problems that
the default option is hard to solve. However, I found the
latter one, PCFactorSetFill does not take effect for my
problem. The matrices and rhs as well as the solutions are
attached from the link below. I obtain the solution using
hypre preconditioner and it takes 7 and 38 iterations for
matrix 1 and matrix 2. However, if I use other
preconditioner, the solver just failed at the first matrix. I
have tested this matrix using the native sequential solver
(not PETSc) with ILU preconditioning. If I set the incomplete
factorization level to 0, this sequential solver will take
more than 100 iterations. If I increase the factorization
level to 1 or more, it just takes several iterations. This
remind me that the PC factor for this matrices should be
increased. However, when I tried it in PETSc, it just does
not work.
Matrix and rhs can be obtained from the link below.
https://eilinator.eos.ubc.ca:8443/index.php/s/CalUcq9CMeblk4R
<https://eilinator.eos.ubc.ca:8443/index.php/s/CalUcq9CMeblk4R>
Would anyone help to check if you can make this work by
increasing the PC factor level or fill?
We have ILU(k) supported in serial. However ILU(dt) which takes a
tolerance only works through Hypre
http://www.mcs.anl.gov/petsc/documentation/linearsolvertable.html
<http://www.mcs.anl.gov/petsc/documentation/linearsolvertable.html>
I recommend you try SuperLU or MUMPS, which can both be
downloaded automatically by configure, and
do a full sparse LU.
Thanks,
Matt
Thanks and regards,
Danyang
--
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
http://www.caam.rice.edu/~mk51/ <http://www.caam.rice.edu/%7Emk51/>
--
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
http://www.caam.rice.edu/~mk51/ <http://www.caam.rice.edu/%7Emk51/>
