[Paraview] XDMF reader parallelism?

[Karl-Ulrich Bamberg]

Hi,

Paraview is a great and handy tool. I am now trying to scale it to large data sets and have
a question related to the parallel capabilities of the Paraview-XDMF-Reader:

We use a PIC-Code that devides its grid into blocks distributed over the ranks.
For the output every rank writes exactly one HDF5, collecting all local blocks and all time steps.
One XDMF per rank was written describing the data, as well as a central XDMF including all the individual ones.

The hierarchy is 
	central-xdmf:  spatial_collection->includes_for_rank_xdmf_files
	rank_xdmf_files: temporal_collection->spatial_collectio_within_ranks->grids

The size of the simulation is about 1024 ranks and 256 time steps but should be increased.
For these parameters we see (via top) a memory consumption of 16GB per pvserver instance.
Directly after opening of the file, so even before "apply". 

I guess that this is because all the pvserver instances read and parse the XDMF file?
One time the paths to the HDF5 files were wrong, the memory consumption was the same.
After "apply" there was than an error.

I tried "PV_USE_TRANSMIT=1" and also changed the grid hierarchy to only have:
temporal_collection->spatial_collection->grids

This directly in one file, that was finally 1GB on disk and around 16GB in memory with lxml2 via python.

But it was to no effort, still every instance was consuming around 16GB

Is there any chance that pvserver can parallelize on the top-level so every pvserver instance only reads some of the "include" files.
Or is there a different approach to store the grid-patches (all the same resolution right now) in HDF5?

All suggestions are highly appreciated :-)

Thank you all very much for any support,
Best regards
--
Dipl.-Phys. Karl-Ulrich Bamberg

Ludwig-Maximilians-Universität München
Arnold-Sommerfeld-Center (ASC)
Computational & Plasma Physics
Theresienstr. 37, D-80333 München

phone: +49 (0)89 2180 4577
fax: +49 (0)89 2180 99 4577
e-mail: Karl-Ulrich.Bamberg at physik.uni-muenchen.de

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