<div dir="ltr"><div><div>Hi Hong,<br><br></div>Can you describe the type of view you're trying to output? If I remember correctly it was volume rendering of an image data. Also, some details on the grid would be helpful.<br>
<br></div>As for the parallel XML polydata writer, we were talking about changing it so that it would only write out files for polydata with points and cells in them which may help alleviate this issue some. I'm not sure what's going on with the file -- you may need to share more information to help diagnose the problem. Is it a writing problem during your run or a reading problem during post-processing? The XML writers won't create a directory if it doesn't exists yet in order to write a file. <br>
<br>As for reducing the number of processes that are writing, you could use the vtkAllToNRedistributePolyData filter to reduce the number of processes that contain any points and cells and then create another vtkMPIController that is used by the parallel XML writer to make sure only the proper processes write out any data. This would take quite a bit of custom coding though.<br>
<br>Andy<br><br></div><div class="gmail_extra"><br><br><div class="gmail_quote">On Tue, Nov 26, 2013 at 10:31 AM, Hong Yi <span dir="ltr"><<a href="mailto:hongyi@renci.org" target="_blank">hongyi@renci.org</a>></span> wrote:<br>
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<p class="MsoNormal">I have done several simulation runs linked with ParaView Catalyst for in-situ visualization on Titan with 18k cores and have the following observations/questions hoping to seek input from this list.<u></u><u></u></p>
<p class="MsoNormal"><u></u> <u></u></p>
<p><u></u><span>1.<span style="font:7.0pt "Times New Roman"">
</span></span><u></u>It appears IceT-based image compositing for 18k cores takes such a long time that it becomes unpractical to output images in-situ. Specifically, in our case, it takes about 14 minutes for coprocessing for one time point that output a
composited image while simulation alone for one time point only takes about 7 seconds. I have also done a simulation run with in-situ visualization on Titan with 64 cores on a much lower resolution mesh (10 million element mesh as opposed on 167 million element
mesh for 18k core run), in which case coprocessing with image output for 64 cores takes about 25 seconds. Question: is there any way to improve performance of image compositing for 18k cores for in-situ visualization?<u></u><u></u></p>
<p><u></u><span>2.<span style="font:7.0pt "Times New Roman"">
</span></span><u></u>I also tried to avoid image output, but output polydata extracts using XMLPPolyDataWriter instead on 18k cores. In this case, in-situ coprocessing only takes about 20 seconds (compared to 14 minutes with image output). However, too many
files are generated to a point that breaks the hard limit on maximal number of files in a directory since the parallel writer writes a vtp file from each of 18k cores. So the output data files have to be broken up into different directories. However, I got
“cannot find file” error when I put a directory name as a parameter in coprocessor.CreateWriter() function call in my python script. I tried initially to put “data/vorticity_%t.pvtp” as a parameter, but it fails with “cannot find file” error. Not sure whether
this is a bug or I need to put absolute full path in rather than a relative path to the current directory. Another question is whether there are ways to composite these files generated from different cores into one single file while doing coprocessing so only
one composite file is generated rather than a huge number of files when running on large number of cores.
<u></u><u></u></p>
<p class="MsoNormal"><u></u> <u></u></p>
<p class="MsoNormal">Thanks for any input, suggestions, and comments!<u></u><u></u></p>
<p class="MsoNormal"><u></u> <u></u></p>
<p class="MsoNormal">Regards,<u></u><u></u></p>
<p class="MsoNormal">Hong<u></u><u></u></p>
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