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<p>Hei,</p>
<p>the usual size of those matrices is (cumulative, not distributed)
at least [8192x8192] x [8192x32768] complex entries as lower
boundary. Does it still make sense to test CUDA for speedup?</p>
<p>Thank you,</p>
<p>regards,</p>
<p>Roland<br>
</p>
<div class="moz-cite-prefix">Am 16.02.21 um 14:14 schrieb Stefano
Zampini:<br>
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<div dir="ltr" class="gmail_attr">Il giorno mar 16 feb 2021
alle ore 11:43 Roland Richter <<a
href="mailto:roland.richter@ntnu.no"
moz-do-not-send="true">roland.richter@ntnu.no</a>> ha
scritto:<br>
</div>
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0.8ex;border-left:1px solid
rgb(204,204,204);padding-left:1ex">
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<p>Hei,</p>
<p>after profiling my program using -log_view, I got the
following output (all matrices are dense):</p>
<p><i>Using 8 OpenMP threads</i><i><br>
</i><i>Using Petsc Development GIT revision:
v3.14.3-583-g5464005aea GIT Date: 2021-01-25 16:01:41
-0600</i><i><br>
</i><i><br>
</i><i> Max Max/Min
Avg Total</i><i><br>
</i><i>Time (sec): 5.074e+03 1.000
5.074e+03</i><i><br>
</i><i>Objects: 2.158e+03 1.000
2.158e+03</i><i><br>
</i><i>Flop: 5.236e+13 1.000
5.236e+13 5.236e+13</i><i><br>
</i><i>Flop/sec: 1.032e+10 1.000
1.032e+10 1.032e+10</i><i><br>
</i><i>MPI Messages: 0.000e+00 0.000
0.000e+00 0.000e+00</i><i><br>
</i><i>MPI Message Lengths: 0.000e+00 0.000
0.000e+00 0.000e+00</i><i><br>
</i><i>MPI Reductions: 0.000e+00 0.000</i><i><br>
</i><i><br>
</i><i>Flop counting convention: 1 flop = 1 real number
operation of type (multiply/divide/add/subtract)</i><i><br>
</i><i> e.g., VecAXPY() for
real vectors of length N --> 2N flop</i><i><br>
</i><i> and VecAXPY() for
complex vectors of length N --> 8N flop</i><i><br>
</i><i><br>
</i><i>Summary of Stages: ----- Time ------ -----
Flop ------ --- Messages --- -- Message Lengths --
-- Reductions --</i><i><br>
</i><i> Avg %Total
Avg %Total Count %Total Avg
%Total Count %Total</i><i><br>
</i><i> 0: Main Stage: 5.0744e+03 100.0%
5.2359e+13 100.0% 0.000e+00 0.0% 0.000e+00
0.0% 0.000e+00 0.0%</i><i><br>
</i><i><br>
</i><i>------------------------------------------------------------------------------------------------------------------------</i><i><br>
</i><i>See the 'Profiling' chapter of the users' manual
for details on interpreting output.</i><i><br>
</i><i>Phase summary info:</i><i><br>
</i><i> Count: number of times phase was executed</i><i><br>
</i><i> Time and Flop: Max - maximum over all
processors</i><i><br>
</i><i> Ratio - ratio of maximum to
minimum over all processors</i><i><br>
</i><i> Mess: number of messages sent</i><i><br>
</i><i> AvgLen: average message length (bytes)</i><i><br>
</i><i> Reduct: number of global reductions</i><i><br>
</i><i> Global: entire computation</i><i><br>
</i><i> Stage: stages of a computation. Set stages
with PetscLogStagePush() and PetscLogStagePop().</i><i><br>
</i><i> %T - percent time in this phase %F
- percent flop in this phase</i><i><br>
</i><i> %M - percent messages in this phase %L
- percent message lengths in this phase</i><i><br>
</i><i> %R - percent reductions in this phase</i><i><br>
</i><i> Total Mflop/s: 10e-6 * (sum of flop over all
processors)/(max time over all processors)</i><i><br>
</i><i> GPU Mflop/s: 10e-6 * (sum of flop on GPU over
all processors)/(max GPU time over all processors)</i><i><br>
</i><i> CpuToGpu Count: total number of CPU to GPU
copies per processor</i><i><br>
</i><i> CpuToGpu Size (Mbytes): 10e-6 * (total size of
CPU to GPU copies per processor)</i><i><br>
</i><i> GpuToCpu Count: total number of GPU to CPU
copies per processor</i><i><br>
</i><i> GpuToCpu Size (Mbytes): 10e-6 * (total size of
GPU to CPU copies per processor)</i><i><br>
</i><i> GPU %F: percent flops on GPU in this event</i><i><br>
</i><i>------------------------------------------------------------------------------------------------------------------------</i><i><br>
</i><i>Event Count Time (sec)
Flop --- Global --- ---
Stage ---- Total GPU - CpuToGpu - - GpuToCpu -
GPU</i><i><br>
</i><i> Max Ratio Max Ratio
Max Ratio Mess AvgLen Reduct %T %F %M %L %R %T
%F %M %L %R Mflop/s Mflop/s Count Size Count
Size %F</i><i><br>
</i><i>---------------------------------------------------------------------------------------------------------------------------------------------------------------</i><i><br>
</i><i><br>
</i><i>--- Event Stage 0: Main Stage</i><i><br>
</i><i><br>
</i><i>VecSet 37 1.0 1.0354e-04 1.0
0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00 0 0 0 0 0
0 0 0 0 0 0 0 0 0.00e+00 0
0.00e+00 0</i><i><br>
</i><i>VecAssemblyBegin 31 1.0 2.9080e-06 1.0
0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00 0 0 0 0 0
0 0 0 0 0 0 0 0 0.00e+00 0
0.00e+00 0</i><i><br>
</i><i>VecAssemblyEnd 31 1.0 2.3270e-06 1.0
0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00 0 0 0 0 0
0 0 0 0 0 0 0 0 0.00e+00 0
0.00e+00 0</i><i><br>
</i><i>MatCopy 49928 1.0 3.7437e+02 1.0
0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00 7 0 0 0 0
7 0 0 0 0 0 0 0 0.00e+00 0
0.00e+00 0</i><i><br>
</i><i>MatConvert 2080 1.0 5.8492e+00 1.0
0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00 0 0 0 0 0
0 0 0 0 0 0 0 0 0.00e+00 0
0.00e+00 0</i><i><br>
</i><i>MatScale 56162 1.0 6.9348e+02 1.0
1.60e+12 1.0 0.0e+00 0.0e+00 0.0e+00 14 3 0 0 0
14 3 0 0 0 2303 0 0 0.00e+00 0
0.00e+00 0</i><i><br>
</i><i>MatAssemblyBegin 56222 1.0 1.7370e-02 1.0
0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00 0 0 0 0 0
0 0 0 0 0 0 0 0 0.00e+00 0
0.00e+00 0</i><i><br>
</i><i>MatAssemblyEnd 56222 1.0 8.8713e-03 1.0
0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00 0 0 0 0 0
0 0 0 0 0 0 0 0 0.00e+00 0
0.00e+00 0</i><i><br>
</i><i>MatZeroEntries 60363 1.0 3.1011e+02 1.0
0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00 6 0 0 0 0
6 0 0 0 0 0 0 0 0.00e+00 0
0.00e+00 0</i><i><br>
</i><i>MatAXPY 8320 1.0 1.2254e+02 1.0
5.58e+11 1.0 0.0e+00 0.0e+00 0.0e+00 2 1 0 0 0
2 1 0 0 0 4557 0 0 0.00e+00 0
0.00e+00 0</i><i><br>
</i><i>MatMatMultSym 4161 1.0 7.1613e-03 1.0
0.00e+00 0.0 0.0e+00 0.0e+00 0.0e+00 0 0 0 0 0
0 0 0 0 0 0 0 0 0.00e+00 0
0.00e+00 0</i><i><br>
</i><i>MatMatMultNum 4161 1.0 4.0706e+02 1.0
5.02e+13 1.0 0.0e+00 0.0e+00 0.0e+00 8 96 0 0 0
8 96 0 0 0 123331 0 0 0.00e+00 0
0.00e+00 0</i><i><br>
</i><i>---------------------------------------------------------------------------------------------------------------------------------------------------------------</i><i><br>
</i><i><br>
</i><i>Memory usage is given in bytes:</i><i><br>
</i><i><br>
</i><i>Object Type Creations Destructions
Memory Descendants' Mem.</i><i><br>
</i><i>Reports information only for process 0.</i><i><br>
</i><i><br>
</i><i>--- Event Stage 0: Main Stage</i><i><br>
</i><i><br>
</i><i> Vector 37 34
1634064 0.</i><i><br>
</i><i> Matrix 2120 2120
52734663456 0.</i><i><br>
</i><i> Viewer 1
0 0 0.</i><i><br>
</i><i>========================================================================================================================</i></p>
<p>Apparently, MatMatMultNum and MatScale take the most
time (by far) during execution. Therefore, I was
wondering if it is possible to move those operations/all
matrices and vectors to a GPU or another accelerator.
According to <a
href="https://www.mcs.anl.gov/petsc/features/gpus.html"
target="_blank" moz-do-not-send="true">https://www.mcs.anl.gov/petsc/features/gpus.html</a>
CUDA is only supported for distributed vectors, but not
for dense distributed matrices. Are there any updates
related to that, or other ways to speed up the involved
operations?</p>
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<div>You should compute the timings associated with each call,
and not consider the lump sum. For example, each MatScale
takes 6.9348e+02/56162 = 0.012347851 seconds on average, I
doubt you can get any reasonable speedup with CUDA. What are
the sizes of these matrices? </div>
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0.8ex;border-left:1px solid
rgb(204,204,204);padding-left:1ex">
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<p>Thanks!</p>
<p>Regards,</p>
<p>Roland<br>
</p>
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-- <br>
<div dir="ltr" class="gmail_signature">Stefano</div>
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