Hi,
I’m using paraview to view outputs from Smoothed Particle Hydrodynamics (SPH) simulations. The data is in h5part format, containing arrays of X, Y, Z and other physical characteristics of our SPH particles. I’m able to visualise some of the files on my machine, either as particles, or interpolating over a volume with SPHVolumeInterpolator, and rendering the volume or isosurfaces.
Now, for simulations with higher resolution, where I’d want more resolution (i.e. more particles), and/or a higher resolution for the volume I interpolate on, the data doesn’t fit in the computer memory.
To get around this, I thought I could run paraview on the cluster on multiple nodes, to spread the memory usage. But I’m not sure if I do it correctly and if it’s actually possible.
To test it, here is what I tried (simply on my machine to validate the setup first, the goal being to run a pvbatch script on the cluster later on with the big dataset):
- run
mpiexec -np 4 pvserverto run multiple parallel processes. - run
paraviewand connect to the pverver. - open the “h5part”
- display the processID: it looks like the data is already spread over the various processes
- (optionally?) use a RedistributeDataset to have contiguous particles on the same node
- (optionally?) use a Ghost Cells
- set up a SPHVolumeInterpolator
Trying different configurations of 5/6/7, it seems to me that SPHVolumeInterpolator supports multithreading but not distributed parallelism, is this correct?
In this case, is there a way to achieve what I am trying to do? The other option being to get access to another machine with a lot more memory than the one I have at hand, where I might be able to pull this off locally.
Thanks