<div dir="ltr">Hi, Philip,<div> I will look into the tarballs and get back to you.</div><div> Thanks.<br clear="all"><div><div dir="ltr" class="gmail_signature" data-smartmail="gmail_signature"><div dir="ltr">--Junchao Zhang</div></div></div><br></div></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Mon, Oct 2, 2023 at 9:41 AM Fackler, Philip via petsc-users <<a href="mailto:petsc-users@mcs.anl.gov">petsc-users@mcs.anl.gov</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div class="msg3364327590849106712">
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<div><span style="font-family:Consolas,Courier,monospace;font-size:12pt;color:rgb(0,0,0)">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).</span></div>
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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.<br>
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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.<br>
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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.<br>
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The tarballs linked <a href="https://drive.google.com/file/d/19X_L3SVkGBM9YUzXnRR_kVWFG0JFwqZ3/view?usp=drive_link" title="https://drive.google.com/file/d/19X_L3SVkGBM9YUzXnRR_kVWFG0JFwqZ3/view?usp=drive_link" id="m_3364327590849106712LPNoLPOWALinkPreview" target="_blank">
here</a> and <a href="https://drive.google.com/file/d/15yDBN7-YlO1g6RJNPYNImzr611i1Ffhv/view?usp=drive_link" title="https://drive.google.com/file/d/15yDBN7-YlO1g6RJNPYNImzr611i1Ffhv/view?usp=drive_link" id="m_3364327590849106712LPNoLPOWALinkPreview_1" target="_blank">
here</a> 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.</div>
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<span style="font-family:Consolas,Courier,monospace;font-size:12pt;color:rgb(0,0,0)">Thanks for your help,</span>
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<span style="font-size:11pt"><strong>Philip Fackler<br>
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<div><span style="font-size:11pt">Research Software Engineer, Application Engineering Group</span></div>
<div><span style="font-size:11pt">Advanced Computing Systems Research Section</span></div>
<div><span style="font-size:11pt">Computer Science and Mathematics Division<br>
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<div><span style="font-size:11pt"><strong>Oak Ridge National Laboratory</strong></span><span style="font-size:11pt"></span><br>
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