Visualisation for WSIRon Fowler

Visualisation GroupSTFC Scientific Computing Department

Outline

• Visualisation and software engineering

• The SoFT project and STFC facilities

• Simple ParaView examples

• Running ParaView remotely and in parallel

• In-situ visualisation

Visualisation in CFD

• Used to verify simulation results

– Are the simulation results correct?

– “Integration testing” level.

• To understand simulation results e.g.

– where the largest forces occur on a structure?

• Monitor long running simulations

– Is the simulation running correctly?

– Should it be aborted?

– “Computational steering”.

Example: wave tank simulation: focused wave

• Hildebrant & Sriram 2014

• Used to compare simulation

and experiment.

• Simulation gives insight to

flow.

Using PyFoam to monitor OF simulation

• Simple dynamic

monitor of run

• Spot errors,

estimate finish

• Job stop/restart if

needed

The SOfT Project

• Project members: MMU, UoP & STFC

• Developing software virtual wave tank environment

• Combining NLFP, incompressible and full NS solvers

• Using parallel HPC and high end visualisation

STFC and Hartree facilities

• Hartree HPC: Blue joule,

dataPlex, phase 2 systems

• Fast parallel file system

• High speed connections to

visualisation rooms

• Large memory visualisation

nodes.

Facilities: 3D stereo and head tracking

• 3D stereo active glasses for room display.

• Head tracking allows immersive VR.

• ParaView supports 3D and immersive displays.

• Oculus rift etc. may be supported in future.

Visualisation tools for CFD

• Commercial tools

– EnSight

– FieldView

– TecPlot

– Matlab

– Avizo Wind

– Etc.

• Open source tools

– ParaView

– VisIT

– VTK

– OpenDX

– SciPython/matplotlib

– Mayavi

ParaView

• Open Source

– Support via KitWare/US

• Readers: 70+:

– OpenFOAM, VTK, EnSight, Fluent, NetCFD, TecPlot…

• Range of filters:

– Isosurfaces, vectors, streamlines, volume vis., derived data, mesh quality

• Animation and video

– To screen and file

• Parallel/remote render

– HPC rendering

• In-situ visualisation

– Catalyst allows visualisation in simulation

BASIC USE OF PARAVIEW

2D Dam break in ParaView & VisIT

Camera animation in ParaView

Wave tank simulation

Using ParaView remotely

• Why remote?

– Focused wave “coarse”: 52 Gbytes

– 2^3 mesh refinement: 416 Gbytes

– 4^3 mesh refinement: 3328 Gbytes

• Too large for local machines

– Need parallel file system, large memory

– Remote rendering possible

– Ideally on same HPC system as simulation

ParaView on HPC

• Simple rendering:

on client

• Heavy rendering:

on server – needs

graphics cards,

X11 or many

nodes (OSMesa)

Remote visualisation: Hartree

• Hartree HPC visualisation nodes:

– 64 Gbytes, 32 cores

– 2 Nvidia Quadro 5000 cards

– Mount parallel file system

• Access to pvserver via ssh:

– Can use parallel readers (MPI)

– User access to remote X11

server for remote rendering

• Examples used client-server

ParaView to desktop

PV client

pvservers (MPI)

ssh

ParaView Catalyst

• Integrate visualisation

into simulation

– Full data dump expensive, slow, large

– Visualisation small, e.g. isosurface << full data

– Add adaptor to code

– Define visualisation as python script

ParaView Catalyst

• Advantages:

– Reduce output data

– Visualisation as

simulation proceeds

– PV scripting for

visualisation pipeline

– Support for

Code_Saturne

• Disadvantages:

– Need adaptor (no OF)

– Define visual pipeline

in advance

– Increase size of HPC

job