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<div style="direction: ltr;font-family: Arial;color: #000000;font-size: 14pt;">Hi Mark,<br>
<br>
Thanks for the suggestions. Sounds like you would say that there is<br>
something wrong with the performance of the cpu only calculation.<br>
Is that a fair conclusion? I have been looking at smaller problem<br>
sizes since the original run. Limiting the iteration count also seems<br>
like a good way to look at the performance of larger problem sizes.<br>
<br>
Thanks again for your suggestions.<br>
<br>
Dave<br>
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<div style="direction: ltr;" id="divRpF491886"><font color="#000000" face="Tahoma" size="2"><b>From:</b> petsc-dev-bounces@mcs.anl.gov [petsc-dev-bounces@mcs.anl.gov] on behalf of Mark F. Adams [mark.adams@columbia.edu]<br>
<b>Sent:</b> Thursday, February 23, 2012 2:20 PM<br>
<b>To:</b> For users of the development version of PETSc<br>
<b>Subject:</b> Re: [petsc-dev] Understanding Some Parallel Results with PETSc<br>
</font><br>
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<div></div>
<div>
<div>The difference in performance for VecAXPY and VecAYPX is dramatic (~35x) and these are dead simple methods that are almost identical and are not parallel so they may be a good place to start looking. You might look a simpler example like src/vec/vec/example/tutorial/ex1.c.
You could add a loop around the calls to VecAXPY and VecAYPX to get some meaningful timings.</div>
<div><br>
</div>
<div>Also, you might limit the number of iterations to say 100, so it does not take 10 hours to run these tests.</div>
<div><br>
</div>
<div>You could also try scaling the problem up (or down) to see when these problems kick in (eg, when you go off a node ...).</div>
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<div>Mark</div>
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<div>On Feb 23, 2012, at 2:49 PM, Nystrom, William D wrote:</div>
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Hi Matt,<br>
<br>
Attached are the log files for the two runs.<br>
<br>
Thanks,<br>
<br>
Dave<br>
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<div id="divRpF62437" style="direction: ltr;"><font color="#000000" face="Tahoma" size="2"><b>From:</b><span class="Apple-converted-space"> </span><a href="mailto:petsc-dev-bounces@mcs.anl.gov" target="_blank">petsc-dev-bounces@mcs.anl.gov</a><span class="Apple-converted-space"> </span>[petsc-dev-bounces@mcs.anl.gov]
on behalf of Matthew Knepley [knepley@gmail.com]<br>
<b>Sent:</b><span class="Apple-converted-space"> </span>Thursday, February 23, 2012 11:17 AM<br>
<b>To:</b><span class="Apple-converted-space"> </span>For users of the development version of PETSc<br>
<b>Subject:</b><span class="Apple-converted-space"> </span>Re: [petsc-dev] Understanding Some Parallel Results with PETSc<br>
</font><br>
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<div>On Thu, Feb 23, 2012 at 11:06 AM, Nystrom, William D<span class="Apple-converted-space"> </span><span dir="ltr"><<a href="mailto:wdn@lanl.gov" target="_blank">wdn@lanl.gov</a>></span><span class="Apple-converted-space"> </span>wrote:<br>
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<blockquote class="gmail_quote" style="margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid rgb(204, 204, 204); padding-left: 1ex; position: static; z-index: auto;">
I recently ran a couple of test runs with petsc-dev that I do not understand. I'm running on a test bed<br>
machine that has 4 nodes with two Tesla 2090 gpus per node. Each node is dual socket and populated<br>
with Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz processors. These are 8 core processors and so each<br>
node has 16 cores. On the gpu, I'm running with Paul's latest version of the txpetscgpu package. I'm<br>
running the src/ksp/ksp/examples/tutorials/ex2.c petsc example with m=n=10000. My objective was<br>
to compare the performance running on 4 nodes using all 8 gpus to that of running on the same 4 nodes<br>
with all 64 cores. This problem uses about a third of the memory available on the gpus. I was using cg<br>
with jacobi preconditioning on both the gpu run and the cpu run. What is puzzling to me is that the cpu<br>
case ran 44x times slower than the gpu case and the big difference was in the time spend in functions<br>
like VecTDot, VecNorm and VecAXPY.<br>
<br>
Below is a table that summarizes the performance of the main functions that were using time in the<br>
two runs. Times are in seconds.<br>
<br>
| GPU | CPU | Ratio<br>
-------------------------------------------------------------------------<br>
MatMult | 450.64 | 5484.7 | 12.17<br>
-------------------------------------------------------------------------<br>
VecTDot | 285.35 | 16688.0 | 58.48<br>
-------------------------------------------------------------------------<br>
VecNorm | 19.03 | 9058.8 | 476.03<br>
-------------------------------------------------------------------------<br>
VecAXPY | 106.88 | 5636.3 | 52.73<br>
-------------------------------------------------------------------------<br>
VecAYPX | 53.69 | 85.1 | 1.58<br>
-------------------------------------------------------------------------<br>
KSPSolve | 811.95 | 35930.0 | 44.25<br>
-------------------------------------------------------------------------<br>
<br>
The ratio of MatMult for CPU versus GPU is what I typically see when I am comparing a CPU run on<br>
a single core versus a run on a single GPU. Since both runs are communicating across node via mpi,<br>
I'm puzzled about why the CPU case is so much slower than the GPU case especially since there is<br>
communication for the MatMult as well. Both runs compute the same final error norm using the exact<br>
same number of iterations. Do these results make sense to people who understand the performance<br>
issues of parallel sparse linear solvers much better than I? Or do these results look abnormal. I had<br>
wondered if part of the performance issue was related to my running 8 times as many mpi processes<br>
for the CPU case. However, I ran a smaller problem with m=n=1000 and using 8 mpi processes and<br>
2 cores per node and I see the same extreme differences in the times spent in VecTDot, VecNorm<br>
and VecAXPY.<br>
<br>
Here are the command lines I used for the two runs:<br>
<br>
CPU:<br>
<br>
mpirun -np 64 -mca btl self,sm,openib ex2 -m 10000 -n 10000 -ksp_type cg -ksp_max_it 100000 -pc_type jacobi -log_summary -options_left<br>
<br>
GPU:<br>
<br>
mpirun -np 8 -npernode 2 -mca btl self,sm,openib ex2 -m 10000 -n 10000 -ksp_type cg -ksp_max_it 100000 -pc_type jacobi -log_summary -options_left -mat_type aijcusp -vec_type cusp -cusp_storage_format dia<br>
</blockquote>
<div><br>
</div>
<div>1) Always send -log_summary with performance questions</div>
<div><br>
</div>
<div>2) Comparing two things will not make any sense beyond "one ran faster" without a model for execution time</div>
<div><br>
</div>
<div>3) In order to make sense of my model, I need flop rates for those events</div>
<div><br>
</div>
<div> Matt</div>
<div> </div>
<blockquote class="gmail_quote" style="margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid rgb(204, 204, 204); padding-left: 1ex;">
Thanks,<br>
<br>
Dave<br>
<br>
--<br>
Dave Nystrom<br>
LANL HPC-5<br>
Phone:<span class="Apple-converted-space"> </span><a href="tel:505-667-7913" value="+15056677913" target="_blank">505-667-7913</a><br>
Email:<span class="Apple-converted-space"> </span><a href="mailto:wdn@lanl.gov" target="_blank">wdn@lanl.gov</a><br>
Smail: Mail Stop B272<br>
Group HPC-5<br>
Los Alamos National Laboratory<br>
Los Alamos, NM 87545<br>
<br>
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--<span class="Apple-converted-space"> </span><br>
What most experimenters take for granted before they begin their experiments is infinitely more interesting than any results to which their experiments lead.<br>
-- Norbert Wiener<br>
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<span><ex2_10000_10000_cg_jacobi_mpi_64.log></span><span><ex2_10000_10000_cg_jacobi_cusp_dia_mpi_8.log></span></div>
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