In Situ Visualization¶
In situ visualization is a possibility how to visualize your data while your computation is progressing on multiple nodes of a cluster. It is a visualization pipeline that can be used on our Salomon supercomputer. The pipeline is based on ParaView Catalyst library.
To leverage the possibilities of the in situ visualization by Catalyst library, you have to write an adaptor code that will use the actual data from your simulation and process them in the way they can be passed to ParaView for visualization. We provide a simple example of such simulator/adaptor code that bind together to provide the in situ visualization.
Detailed description of the Catalyst API can be found here. We restrict ourselves to provide more of an overall description of the code together with specifications for building, and explanation about how to run the code on the cluster.
The Catalyst library is part of the ParaView module. More about ParaView can be found here. We use version 5.6.0. It has been compiled with
intel/2017a and installed on the Salomon cluster.
All code concerning the simulator/adaptor is available to download from here. It is a package with the following files: CMakeLists.txt, FEAdaptor.h, FEAdaptor.cxx, FEDataStructures.h, FEDataStructures.cxx, FEDriver.cxx and feslicescript.py.
After the download unpack the code by
$ tar xvf package_name
Then use CMake to manage the build process, but before that load the appropriate modules (CMake, ParaView) by
$ ml CMake ParaView/5.6.0-intel-2017a-mpi
This module set also loads necessary intel compiler within the dependencies
$ mkdir build $ cd build $ cmake ../
Now you can build the simulator/adaptor code by using make
It will generate the CxxFullExampleAdaptor executable file. This will be later run together with ParaView and it will provide the in situ visualization example.
Provided example is a simple MPI program. Main executing part is written in FEDriver.cxx. It is a simulator code that creates computational grid and performs simulator/adaptor interaction (see below).
Dimensions of the computational grid in terms of number of points in x, y, z direction are supplied as input parameters to the main function (see lines 22-24 in code below). Based on the number of MPI ranks each MPI process creates different part of the overall grid. This is done by grid initialization, see line 30. The respective code for this is in FEDataStructures.cxx. The parametr nr. 4 in main is for the name of a Python script (we use feslicescript.py). It sets up the ParaView-Catalyst pipeline, see line 36. The simulation starts by linearly progressing timeStep value in the for loop. Each iteration of the loop upates the grid attributes (Velocity and Pressure) by calling the iUpdateFields method from Attributes class. Next in the loop, the adaptor's CoProcess function is called by using actual parameters (grid, time). To provide some information about the state of the simulation the actual time step is print together with the name of the processor that handles the computation inside the allocated MPI rank. Before the loop ends appropriate sleep time is used to give sime time visualization to update. After the simulation loop ends, clean up is done by calling Finalize function on adaptor and also MPI_Finalize on MPI processes.
Adaptor's initialization performs several necessary steps, see the code below. It creates vtkCPProcessor using Catalyst library and adds pipeline to it. The pipeline is initialized by the reffered Python script
As a Python script to initialize the Catalyst pipeline we use the feslicescript.py. You enable the live visualization in here and set the proper connection port. You can also another commands and functions to configure it for saving the data during the visualization or another tasks that are available from the ParaView environment. For more details see the [Catalyst guide][catalyst_guide].
The UpdateFields method from the Attributes class, updates the velocity value in respect to the value of time and the value of setting which depends on the actual MPI rank, see the code below. In this way, different processes can be visually distinguished during the simulation.
As mentioned before, further in the simulation loop, the adaptor's CoProcess function is called by using actual parameters of the grid, time, and timeStep. In the function proper representation and eescription of the data is created. Such data are then passed to the Processor that has been created during the adaptor's initialization. The code of the CoProcess function is shown below.
Launching the Simulator/Adaptor With ParaView¶
To launch ParaView you have two standard options on Salomon. You can run ParaView from your local machine in client-server mode by following the information here or you can connect to the cluster using VNC and graphical environment by following the information on VNC. In both cases we will use ParaView version 5.6.0 and its respective module.
Either you use client-server mode or VNC for ParaView you have to allocate some computing resources. This is done by the console commands below (supply valid Project ID).
For client-server mode of ParaView we allocate the resources by
$ qsub -I -q qprod -A PROJECT-ID -l select=2
In case of VNC connection we use X11 forwarding by -X option to allow graphical environment on interactive session.
$ qsub -IX -q qprod -A PROJECT-ID -l select=2
The issued console commands launche an interactive session on 2 nodes. This is the minimal setup to test that the simulator/adaptor code runs on multiple nodes.
After the interactive session is opened, load the ParaView module.
$ ml ParaView/5.6.0-intel-2017a-mpi
When the module is loaded and you run the client-server mode, launch the mpirun command for pvserver as used in description for ParaView client-server but use also the & sign at the end of the command. Then you can use the console later for running the simulator/adaptor code. If you run the VNC session, after loading the ParaView module, setup the environmental parameter for correct keyboard input and then run the ParaView in the background using the & sign.
$ export QT_XKB_CONFIG_ROOT=/usr/share/X11/xkb $ paraview &
If you have proceeded to the end in the description for ParaView client-server mode and run ParaView localy or you have launched ParaView remotely using VNC the further steps are the same for both options. In the ParaView go to the Catalyst -> Connect and hit OK in the pop up window questioning for the connection port. After that ParaView starts listening for incomming data to the in situ visualization.
Go to your build directory and run the built simulator/adaptor code from console by
mpirun -n 2 ./CxxFullExample 30 30 30 ../feslicescript.py
Programs starts to compute on the allocated nodes and prints out the response.
In ParaView you should see a new pipeline called input in the Pipeline Browser.
By clicking on it another item called Extract: input is added.
If you click on the eye icon left to the Extract: input item the data will appear.
To visualize the velocity property on the geometry, go to the Properties tab and choose velocity instead of Solid Color in Coloring menu.
The final result will look like in the image below, where different domains dependent on the number of allocated resources can be seen and they will progress through the time.
After you finish the in situ visualization you should a provide proper cleanup.
Terminate the simulation if it is still running.
In VNC session close the ParaView and the interactive job. Close the VNC client. Kill the Vncserver as described here.
In client-server mode of ParaView disconnect from the server in ParaView and close the interactive job.