[Paraview] In-situ file/image output on Titan with 18k cores

[Hong Yi]

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.


1.       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?

2.       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.

Thanks for any input, suggestions, and comments!

Regards,
Hong
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