[petsc-users] [EXTERNAL] Re: Unexpected performance losses switching to COO interface

[Zhang, Hong]

I noticed that you are using ARKIMEX in the code. A temporary workaround you can try is to disable adaptive time stepping, e.g. by using the option -ts_adapt_type none. Then MatShift() will not be called when the Jacobians are computed.

Hong (Mr.)

On Oct 5, 2023, at 4:52 PM, Fackler, Philip via petsc-users <petsc-users at mcs.anl.gov> wrote:

Aha! That makes sense. Thank you.

Philip Fackler
Research Software Engineer, Application Engineering Group
Advanced Computing Systems Research Section
Computer Science and Mathematics Division
Oak Ridge National Laboratory
________________________________
From: Junchao Zhang <junchao.zhang at gmail.com>
Sent: Thursday, October 5, 2023 17:29
To: Fackler, Philip <facklerpw at ornl.gov>
Cc: petsc-users at mcs.anl.gov <petsc-users at mcs.anl.gov>; xolotl-psi-development at lists.sourceforge.net <xolotl-psi-development at lists.sourceforge.net>; Blondel, Sophie <sblondel at utk.edu>
Subject: [EXTERNAL] Re: [petsc-users] Unexpected performance losses switching to COO interface

Wait a moment, it seems it was because we do not have a GPU implementation of MatShift...
Let me see how to add it.
--Junchao Zhang


On Thu, Oct 5, 2023 at 10:58 AM Junchao Zhang <junchao.zhang at gmail.com<mailto:junchao.zhang at gmail.com>> wrote:
Hi, Philip,
  I looked at the hpcdb-NE_3-cuda file. It seems you used MatSetValues() instead of the COO interface?  MatSetValues() needs to copy the data from device to host and thus is expensive.
  Do you have profiling results with COO enabled?

<Screenshot 2023-10-05 at 10.55.29 AM.png>


--Junchao Zhang


On Mon, Oct 2, 2023 at 9:52 AM Junchao Zhang <junchao.zhang at gmail.com<mailto:junchao.zhang at gmail.com>> wrote:
Hi, Philip,
  I will look into the tarballs and get back to you.
   Thanks.
--Junchao Zhang


On Mon, Oct 2, 2023 at 9:41 AM Fackler, Philip via petsc-users <petsc-users at mcs.anl.gov<mailto:petsc-users at mcs.anl.gov>> wrote:
We finally have xolotl ported to use the new COO interface and the aijkokkos implementation for Mat (and kokkos for Vec). Comparing this port to our previous version (using MatSetValuesStencil and the default Mat and Vec implementations), we expected to see an improvement in performance for both the "serial" and "cuda" builds (here I'm referring to the kokkos configuration).

Attached are two plots that show timings for three different cases. All of these were run on Ascent (the Summit-like training system) with 6 MPI tasks (on a single node). The CUDA cases were given one GPU per task (and used CUDA-aware MPI). The labels on the blue bars indicate speedup. In all cases we used "-fieldsplit_0_pc_type jacobi" to keep the comparison as consistent as possible.

The performance of RHSJacobian (where the bulk of computation happens in xolotl) behaved basically as expected (better than expected in the serial build). NE_3 case in CUDA was the only one that performed worse, but not surprisingly, since its workload for the GPUs is much smaller. We've still got more optimization to do on this.

The real surprise was how much worse the overall solve times were. This seems to be due simply to switching to the kokkos-based implementation. I'm wondering if there are any changes we can make in configuration or runtime arguments to help with PETSc's performance here. Any help looking into this would be appreciated.

The tarballs linked here<https://urldefense.us/v2/url?u=https-3A__drive.google.com_file_d_19X-5FL3SVkGBM9YUzXnRR-5FkVWFG0JFwqZ3_view-3Fusp-3Ddrive-5Flink&d=DwMFaQ&c=v4IIwRuZAmwupIjowmMWUmLasxPEgYsgNI-O7C4ViYc&r=DAkLCjn8leYU-uJ-kfNEQMhPZWx9lzc4d5KgIR-RZWQ&m=GTpC2k9hIdMhUg_aJkeAqd-1CP5M8bwJMJjTriVE1k-j36ZnEHerQkZOzszxWoG2&s=GW0ImGWhWr4rR5AoSULCnaP1CN1QWxTSeMDhdOuhTEA&e=> and here<https://urldefense.us/v2/url?u=https-3A__drive.google.com_file_d_15yDBN7-2DYlO1g6RJNPYNImzr611i1Ffhv_view-3Fusp-3Ddrive-5Flink&d=DwMFaQ&c=v4IIwRuZAmwupIjowmMWUmLasxPEgYsgNI-O7C4ViYc&r=DAkLCjn8leYU-uJ-kfNEQMhPZWx9lzc4d5KgIR-RZWQ&m=GTpC2k9hIdMhUg_aJkeAqd-1CP5M8bwJMJjTriVE1k-j36ZnEHerQkZOzszxWoG2&s=tO-BnNY2myA-pIsRnBjQNoaOSjn-B3-lWGiQp7XXJwk&e=> are profiling databases which, once extracted, can be viewed with hpcviewer. I don't know how helpful that will be, but hopefully it can give you some direction.

Thanks for your help,

Philip Fackler
Research Software Engineer, Application Engineering Group
Advanced Computing Systems Research Section
Computer Science and Mathematics Division
Oak Ridge National Laboratory

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