ParaView installations at NSC


Directory list:

VersionDescription
4.0.1 ParaView version 4.0.1.
4.1.0 ParaView version 4.1.0.
4.4.0 ParaView version 4.4.0.
5.0 ParaView version 5.0.0.

Level of support

Tier 3 NSC will not be able to help you much with this program, either because we lack the in-house experience, or because it is a test installation. In general, these types of installations are untested, and will not be updated unless you send a request to NSC.

Please see the page describing our software support categories for more information.
You can also contact support@nsc.liu.se for further information.

ParaView is an open-source, multi-platform data analysis and visualization application. ParaView users can quickly build visualizations to analyze their data using qualitative and quantitative techniques. The data exploration can be done interactively in 3D or programmatically using ParaViews batch processing capabilities.

ParaView was developed to analyze extremely large datasets using distributed memory computing resources. It can be run on supercomputers to analyze datasets of exascale size as well as on laptops for smaller data.

How to run

Load the paraview module corresponding to the version you want to use, e.g

module load paraview/5.0.0

This will add the paraview application to your search path.

To start Paraview in the standard, serial way:

paraview

When running the paraview GUI NSC recommends using ThinLinc to access Triolith. For more information on how to use ThinLinc, please see: www.nsc.liu.se/systems/triolith/#accessing_triolith_thinlinc

How to run Paraview in parallel

Paraview supports parallel rendering. This is recommended if your data sets are very large. It is based on a client-server model. The client is the Paraview graphical interface, where the screen output is rendered. The heavy computation is done on the parallel server. If the size of the data exceeds the memory capacity of a regular compute node, you may want to use a fat node or even a huge node.

The client and server are setup as follows:

  1. Login to a compute node

  2. Load the required Paraview module, e.g.

    module load paraview/5.0.0
    
  3. Start the Paraview client in the background

    paraview &
    
  4. Start the parallel server

    mpirun -np <number of cores> pvserver --use-offscreen-rendering
    

    You should get a text output showing which node and port Paraview is using, e.g. node=n290 and port=11111

    Waiting for client...
    Connection URL: cs://n290:11111
    Accepting connection(s): n290:11111
    

    11111 is the default port

  5. Connect the client with the server
    • Open the server window in Paraview. Menu: File->Connect
    • Click Add Server
    • This opens the window Edit Server Configuration
      • Name: Assign a name to the new configuration
      • Server Type: Client/Server (default)
      • Host: The node, where you are running the parallel server. If you run the client and the server on the same node, then the default setting is fine: localhost
      • Port: 11111 (default)
    • Click Configure
    • Then select Startup Type: Manual and click Save
    • Now choose the server you just edited and click Connect

Your client should now connect to the pvserver we set up and you should be able to do parallel rendering.

To exit paraview, disconnect the client from the server

Disconnect client from server. Menu: File->Disconnect

How to run Paraview on huge nodes

On huge nodes, we currently only support Paraview 5.0.0. It can be accesed via the following module:

module load paraview/5.0.0-huge

How to check if you run a parallel server

The Pipeline Browser (top left windows in the Paraview GUI) shows if you are running in serial or parallel mode:

  • Standard serial mode: builtin
  • Parallel server mode: cs://localhost:11111

Paraview settings

One should change the following setting in Paraview, when using a parallel server:

  • Menu: Edit->Setting
  • Render View: Set Remote Render Threshold = 0

The Remote Render Threshold option tells Paraview to always use parallel rendering. This option is important to obtain parallel scalability.