Please always respond to the list.
Pay attention to the warnings in the log:
##########################################################
# #
# WARNING!!! #
# #
# This code was compiled with a debugging option. #
# To get timing results run ./configure #
# using --with-debugging=no, the performance will #
# be generally two or three times faster. #
# #
##########################################################
With the debugging option the times are not trustworthy, so I suggest repeating
the analysis with an optimized build.
Jose
> El 12 jun 2022, a las 5:41, Runfeng Jin <[email protected]> escribió:
>
> Hello!
> I compare these two matrix solver's log view and find some strange thing.
> Attachment files are the log view.:
> file 1: log of matrix A solver. This is a larger matrix(900,000*900,000)
> but solved quickly(30s);
> file 2: log of matix B solver. This is a smaller matrix(2,547*2,547 , a
> little different from the matrix B that is mentioned in initial email, but
> solved much slower too. I use this for a quicker test) but solved much
> slower(1244s).
>
> By comparing these two files, I find some thing:
> 1) Matrix A has more basis vectors(375) than B(189), but A spent less time on
> BVCreate(0.349s) than B(296s);
> 2) Matrix A spent less time on EPSSetup(0.031s) than B(10.709s)
> 3) Matrix B distribute much more unbalancedly storage among processors(memory
> max/min 4365) than A(memory max/min 1.113), but other metrics seems more
> balanced.
>
> I don't do prealocation in A, and it is distributed across processors by
> PETSc. For B , when preallocation I use PetscSplitOwnership to decide which
> part belongs to local processor, and B is also distributed by PETSc when
> compute matrix values.
>
> - Does this mean, for matrix B, too much nonzero elements are stored in
> single process, and this is why it cost too much more time in solving the
> matrix and find eigenvalues? If so, are there some better ways to distribute
> the matrix among processors?
> - Or are there any else reasons for this difference in cost time?
>
> Hope to recieve your reply, thank you!
>
> Runfeng Jin
>
>
>
> Runfeng Jin <[email protected]> 于2022年6月11日周六 20:33写道：
> Hello!
> I have try ues PETSC_DEFAULT for eps_ncv, but it still cost much time. Is
> there anything else I can do? Attachment is log when use PETSC_DEFAULT for
> eps_ncv.
>
> Thank you !
>
> Runfeng Jin
>
> Jose E. Roman <[email protected]> 于2022年6月10日周五 20:50写道：
> The value -eps_ncv 5000 is huge.
> Better let SLEPc use the default value.
>
> Jose
>
>
> > El 10 jun 2022, a las 14:24, Jin Runfeng <[email protected]> escribió:
> >
> > Hello!
> > I want to acquire the 3 smallest eigenvalue, and attachment is the log
> > view output. I can see epssolve really cost the major time. But I can not
> > see why it cost so much time. Can you see something from it?
> >
> > Thank you !
> >
> > Runfeng Jin
> >
> > On 6月 4 2022, at 1:37 凌晨, Jose E. Roman <[email protected]> wrote:
> > Convergence depends on distribution of eigenvalues you want to compute. On
> > the other hand, the cost also depends on the time it takes to build the
> > preconditioner. Use -log_view to see the cost of the different steps of the
> > computation.
> >
> > Jose
> >
> >
> > > El 3 jun 2022, a las 18:50, jsfaraway <[email protected]> escribió:
> > >
> > > hello!
> > >
> > > I am trying to use epsgd compute matrix's one smallest eigenvalue. And I
> > > find a strang thing. There are two matrix A(900000*900000) and
> > > B(90000*90000). While solve A use 371 iterations and only 30.83s, solve B
> > > use 22 iterations and 38885s! What could be the reason for this? Or what
> > > can I do to find the reason?
> > >
> > > I use" -eps_type gd -eps_ncv 300 -eps_nev 3 -eps_smallest_real ".
> > > And there is one difference I can tell is matrix B has many small value,
> > > whose absolute value is less than 10-6. Could this be the reason?
> > >
> > > Thank you!
> > >
> > > Runfeng Jin
> > <log_view.txt>
>
> <File2_lower-But-Smaller-Matrix.txt><File1_fatesr-But-Larger-MATRIX.txt>
