I managed to pass the following options to PETSc using a GPU node on Perlmutter.

    -mat_type aijcusparse -vec_type cuda -log_view -options_left

Below is a summary of the test using 4 MPI tasks and 1 GPU per task.

o #PETSc Option Table entries:
   -log_view
   -mat_type aijcusparse
   -options_left
   -vec_type cuda
   #End of PETSc Option Table entries
   WARNING! There are options you set that were not used!
   WARNING! could be spelling mistake, etc!
   There is one unused database option. It is:
   Option left: name:-mat_type value: aijcusparse

The -mat_type option has not been used. In the application code, we use

    ierr = MatCreateAIJ(PETSC_COMM_WORLD,mlocal,mlocal,m,n,
             d_nz,PETSC_NULL,o_nz,PETSC_NULL,&A);;CHKERRQ(ierr);


o The percent flops on the GPU for KSPSolve is 17%.

In comparison with a CPU run using 16 MPI tasks, the GPU run is an order of magnitude slower. How can I improve the GPU performance?

Thanks,
Cho
From: Ng, Cho-Kuen <cho@slac.stanford.edu>
Sent: Friday, June 30, 2023 7:57 AM
To: Barry Smith <bsmith@petsc.dev>; Mark Adams <mfadams@lbl.gov>
Cc: Matthew Knepley <knepley@gmail.com>; petsc-users@mcs.anl.gov <petsc-users@mcs.anl.gov>
Subject: Re: [petsc-users] Using PETSc GPU backend
 
Barry, Mark and Matt,

Thank you all for the suggestions. I will modify the code so we can pass runtime options.

Cho
From: Barry Smith <bsmith@petsc.dev>
Sent: Friday, June 30, 2023 7:01 AM
To: Mark Adams <mfadams@lbl.gov>
Cc: Matthew Knepley <knepley@gmail.com>; Ng, Cho-Kuen <cho@slac.stanford.edu>; petsc-users@mcs.anl.gov <petsc-users@mcs.anl.gov>
Subject: Re: [petsc-users] Using PETSc GPU backend
 

  Note that options like -mat_type aijcusparse  -vec_type cuda only work if the program is set up to allow runtime swapping of matrix and vector types. If you have a call to MatCreateMPIAIJ() or other specific types then then these options do nothing but because Mark had you use -options_left the program will tell you at the end that it did not use the option so you will know.

On Jun 30, 2023, at 9:30 AM, Mark Adams <mfadams@lbl.gov> wrote:

PetscCall(PetscInitialize(&argc, &argv, NULL, help)); gives us the args and you run:

a.out -mat_type aijcusparse -vec_type cuda -log_view -options_left

Mark

On Fri, Jun 30, 2023 at 6:16 AM Matthew Knepley <knepley@gmail.com> wrote:
On Fri, Jun 30, 2023 at 1:13 AM Ng, Cho-Kuen via petsc-users <petsc-users@mcs.anl.gov> wrote:
Mark,

The application code reads in parameters from an input file, where we can put the PETSc runtime options. Then we pass the options to PetscInitialize(...). Does that sounds right?

PETSc will read command line argument automatically in PetscInitialize() unless you shut it off.

  Thanks,

    Matt
 
Cho
From: Ng, Cho-Kuen <cho@slac.stanford.edu>
Sent: Thursday, June 29, 2023 8:32 PM
To: Mark Adams <mfadams@lbl.gov>
Cc: petsc-users@mcs.anl.gov <petsc-users@mcs.anl.gov>
Subject: Re: [petsc-users] Using PETSc GPU backend
 
Mark,

Thanks for the information. How do I put the runtime options for the executable, say, a.out, which does not have the provision to append arguments? Do I need to change the C++ main to read in the options?

Cho
From: Mark Adams <mfadams@lbl.gov>
Sent: Thursday, June 29, 2023 5:55 PM
To: Ng, Cho-Kuen <cho@slac.stanford.edu>
Cc: petsc-users@mcs.anl.gov <petsc-users@mcs.anl.gov>
Subject: Re: [petsc-users] Using PETSc GPU backend
 
Run with options: -mat_type aijcusparse -vec_type cuda -log_view -options_left

The last column of the performance data (from -log_view) will be the percent flops on the GPU. Check that that is > 0.

The end of the output will list the options that were used and options that were _not_ used (if any). Check that there are no options left.

Mark

On Thu, Jun 29, 2023 at 7:50 PM Ng, Cho-Kuen via petsc-users <petsc-users@mcs.anl.gov> wrote:
I installed PETSc on Perlmutter using "spack install petsc+cuda+zoltan" and used it by "spack load petsc/fwge6pf". Then I compiled the application code (purely CPU code) linking to the petsc package, hoping that I can get performance improvement using the petsc GPU backend. However, the timing was the same using the same number of MPI tasks with and without GPU accelerators. Have I missed something in the process, for example, setting up PETSc options at runtime to use the GPU backend?

Thanks,
Cho


--
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