[Paraview] Parallel Scalability

Tue Mar 26 11:40:10 EDT 2013

Dear Berk,

I tried your suggestion and coloured the result with the
ProcessIdScalars Filter. I can see the partitioning and it also makes
sense, so there should not be any major load imbalance. I did add some
timing information in the code. I add the changed program. It is evident
that the data input is not the bottleneck. There is also some speedup in
the application of the filters (should be the contour filter mostly),
but this is more than compensated by the much longer time needed by the
WriteImage command which does the rendering. You can see the times
below. As I use a fast raid for the I/O this is not due to disk speed.
Do you have any ideas if I could do something to speed up this last part?

Best regards,
Olaf

1 Process:
Reading data from  conc_4996800.vtr
Setup and Transformation took  1.52222299576
Writing  contour_00380.png
[Array: conc, Array: ProcessId]
(0.0, 0.0)
Filters took  83.8577969074
Rendering took  13.8007481098
Total runtime:  99.1809921265

2 Processes:
Reading data from  conc_4996800.vtr
Setup and Transformation took  1.02662491798
Writing  contour_00380.png
[Array: conc, Array: ProcessId]
(0.0, 1.0)
Filters took  54.6261291504
Rendering took  46.8519799709
Total runtime:  102.504925966

4 Processes:
Reading data from  conc_4996800.vtr
Setup and Transformation took  0.90910410881
Writing  contour_00380.png
[Array: conc, Array: ProcessId]
(0.0, 3.0)
Filters took  56.8009800911
Rendering took  42.3190040588
Total runtime:  100.029356956

Am 26.03.13 13:43, schrieb Berk Geveci:
> Hi Olaf,
> 
> From your previous message, I am assuming that you are using vtr files.
> In this case, the processing should scale. If you can make an example
> files available, I can verify this. Feel free to e-mail them to me
> directly or I can download them somewhere if they are too big. The two
> potential problems are:
> 
> - IO. You still have one disk if you are not running this on a cluster.
> If the processing that ParaView is doing is negligible compared to the
> time it takes to read the data, you will not see good scaling of the
> whole script as you add more processes.
> 
> - Load balancing. ParaView uses static load balancing when running in
> parallel. So if that partitioning is not load balanced wrt iso-surfacing
> (e.g. most of the iso-surface is generated by one process only), you
> will not see good scaling. You can check if this is the case by applying
> Process Id Scalars to the contour output. It will color polygons based
> on which processor generates them.
> 
> Best,
> -berk
> 
> 
> 
> On Mon, Mar 25, 2013 at 10:46 AM, Dr. Olaf Ippisch
> <olaf.ippisch at iwr.uni-heidelberg.de
> <mailto:olaf.ippisch at iwr.uni-heidelberg.de>> wrote:
> 
>     Dear Paraview developers and users,
> 
>     I tried to run paraview in parallel using a python script. I compiled a
>     server including OpenMPI support and support for MESA off-screen
>     rendering and started the server using mpirun. The I connected from a
>     python script (see attachment). I could see that there are two threads
>     both taking 100% CPU time. However, there was absolutely no speed-up.
>     The runtime using two processors was completely the some. The data sets
>     were rather large (about 100 million unknowns in 3D, 512 x 512 x 405).
>     The result looked like the result with one process, but the time needed
>     was also the same. I am sure that I am making some error either in the
>     setup or I am missing something in the python program. Do you have any
>     suggestions?
> 
>     Best regards,
>     Olaf Ippisch
> 
>     --
>     Dr. Olaf Ippisch
>     Universität Heidelberg
>     Interdisziplinäres Zentrum für Wissenschaftliches Rechnen
>     Im Neuenheimer Feld 368, Raum 4.24
>     Tel: 06221/548252   Fax: 06221/548884
>     Mail: Im Neuenheimer Feld 368, 69120 Heidelberg
>     e-mail: <olaf.ippisch at iwr.uni-heidelberg.de
>     <mailto:olaf.ippisch at iwr.uni-heidelberg.de>>
> 
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-- 
Dr. Olaf Ippisch
Universität Heidelberg
Interdisziplinäres Zentrum für Wissenschaftliches Rechnen
Im Neuenheimer Feld 368, Raum 4.24
Tel: 06221/548252   Fax: 06221/548884
Mail: Im Neuenheimer Feld 368, 69120 Heidelberg
e-mail: <olaf.ippisch at iwr.uni-heidelberg.de>
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