[petsc-users] Best practices for solving Dense Linear systems

[Barry Smith]

> On Aug 7, 2020, at 1:25 AM, Nidish <nb25 at rice.edu> wrote:
> 
> Indeed - I was just using the default solver (GMRES with ILU).
> 
> Using just standard LU (direct solve with "-pc_type lu -ksp_type preonly"), I find elemental to be extremely slow even for a 1000x1000 matrix.
> 

What about on one process? 

Elemental generally won't be competitive for such tiny matrices. 
> For MPIaij it's throwing me an error if I tried "-pc_type lu".
> 

   Yes, there is no PETSc code for sparse parallel direct solver, this is expected.

   What about ?

> mpirun -n 1 ./ksps -N 1000 -mat_type mpidense -pc_type jacobi
> 
> mpirun -n 4 ./ksps -N 1000 -mat_type mpidense -pc_type jacobi

Where will your dense matrices be coming from and how big will they be in practice? This will help determine if an iterative solver is appropriate. If they will be 100,000 for example then testing with 1000 will tell you nothing useful, you need to test with the problem size you care about.

Barry

> I'm attaching the code here, in case you'd like to have a look at what I've been trying to do. 
> 
> The two configurations of interest are,
> 
> $> mpirun -n 4 ./ksps -N 1000 -mat_type mpiaij
> $> mpirun -n 4 ./ksps -N 1000 -mat_type elemental
> 
> (for the GMRES with ILU) and,
> 
> $> mpirun -n 4 ./ksps -N 1000 -mat_type mpiaij -pc_type lu -ksp_type preonly
> $> mpirun -n 4 ./ksps -N 1000 -mat_type elemental -pc_type lu -ksp_type preonly
> 
> elemental seems to perform poorly in both cases.
> 
> Nidish
> 
> On 8/7/20 12:50 AM, Barry Smith wrote:
>> 
>>   What is the output of -ksp_view  for the two case?
>> 
>>   It is not only the matrix format but also the matrix solver that matters. For example if you are using an iterative solver the elemental format won't be faster, you should use the PETSc MPIDENSE format. The elemental format is really intended when you use a direct LU solver for the matrix. For tiny matrices like this an iterative solver could easily be faster than the direct solver, it depends on the conditioning (eigenstructure) of the dense matrix. Also the default PETSc solver uses block Jacobi with ILU on each process if using a sparse format, ILU applied to a dense matrix is actually LU so your solver is probably different also between the MPIAIJ and the elemental. 
>> 
>>   Barry
>> 
>> 
>>   
>> 
>>> On Aug 7, 2020, at 12:30 AM, Nidish <nb25 at rice.edu <mailto:nb25 at rice.edu>> wrote:
>>> 
>>> Thank you for the response.
>>> 
>>> I've just been running some tests with matrices up to 2e4 dimensions (dense). When I compared the solution times for "-mat_type elemental" and "-mat_type mpiaij" running with 4 cores, I found the mpidense versions running way faster than elemental. I have not been able to make the elemental version finish up for 2e4 so far (my patience runs out faster). 
>>> 
>>> What's going on here? I thought elemental was supposed to be superior for dense matrices.
>>> 
>>> I can share the code if that's appropriate for this forum (sorry, I'm new here). 
>>> 
>>> Nidish
>>> On Aug 6, 2020, at 23:01, Barry Smith <bsmith at petsc.dev <mailto:bsmith at petsc.dev>> wrote:
>>> 
>>>  On Aug 6, 2020, at 7:32 PM, Nidish <nb25 at rice.edu <mailto:nb25 at rice.edu>> wrote:
>>>  
>>>  I'm relatively new to PETSc, and my applications involve (for the most part) dense matrix solves.
>>>  
>>>  I read in the documentation that this is an area PETSc does not specialize in but instead recommends external libraries such as Elemental. I'm wondering if there are any "best" practices in this regard. Some questions I'd like answered are:
>>>  
>>>  1. Can I just declare my dense matrix as a sparse one and fill the whole matrix up? Do any of the others go this route? What're possible pitfalls/unfavorable outcomes for this? I understand the memory overhead probably shoots up.
>>> 
>>>   No, this isn't practical, the performance will be terrible.
>>> 
>>>  2. Are there any specific guidelines on when I can expect elemental to perform better in parallel than in serial?
>>> 
>>>   Because the computation to communication ratio for dense matrices is higher than for sparse you will see better parallel performance for dense problems of a given size than sparse problems of a similar size. In other words parallelism can help for dense matrices for relatively small problems, of course the specifics of your machine hardware and software also play a role.
>>> 
>>>    Barry
>>> 
>>>  
>>>  Of course, I'm interesting in any other details that may be important in this regard.
>>>  
>>>  Thank you,
>>>  Nidish
>>> 
>> 
> -- 
> Nidish
> <ksps.cpp>

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