<div dir="ltr"><div dir="ltr"><br></div><br><div class="gmail_quote"><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">roland.richter@ntnu.no</a>> ha scritto:<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>
<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">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>
<p></p></div></blockquote><div><br></div><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><div> </div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div><p>Thanks!</p>
<p>Regards,</p>
<p>Roland<br>
</p>
</div>
</blockquote></div><br clear="all"><div><br></div>-- <br><div dir="ltr" class="gmail_signature">Stefano</div></div>