gViz: Visualization and Computational Steering on the Grid

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gViz Visualization and Computational Steering on
the Grid Ken Brodlie, Jason Wood University
of Leeds David Duce, Musbah Sagar Oxford
Brookes University
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gViz Visualization Middleware for e-Science

• gViz is an e-Science Core Programme project
  just finished
• has made a start at understanding
• How to evolve existing visualization systems to
  the Grid
• How to link visualization and simulation
  environments

• gViz partners
• Academic Leeds, Oxford, Oxford Brookes, CLRC/RAL
• Industrial NAG, IBM UK and Streamline Computing
• International Caltech, MIT
• Leeds contribution through the White Rose Grid
  e-Science Centre of Excellence

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Starting Point Dataflow Visualization Systems

• Visualization represented as pipeline
• Read in data
• Construct a visualization in terms of geometry
• Render geometry as image
• Realised as modular visualization environment
• IRIS Explorer is one example
• Visual programming paradigm
• Extensible add your own modules
• Others include IBM Open Visualization Data
  Explorer

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Extending the Reference Model to Grid Environments

• Revisit the visualization pipeline
• Start with the traditional reference model
• Progressively bind in software and hardware
  resources
• Three-layer reference model

• Conceptual intent of the visualization
• Show me isosurface of constant temperature
• Logical bind in the software system
• Use IRIS Explorer (or vtk, or whatever)
• Physical bind in the resources to be used
• Run the isosurface extraction on particular Grid
  resource

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Developing an XML Language for Conceptual Layer
skML

• First the conceptual layer
• Dataflow consists fundamentally of
• a map
• containing links
• between ports
• on modules
• which have parameters
• This leads us to a simple XML application for
  visualization called skML
• Here a data reader is linked to an isosurfacer

• lt?xml version"1.0"?gt
• ltskmlgt
• ltmapgt
• ltlinkgt
• ltmodule name"ReadLat
• out-port"Output"gt
• ltparam name"Filename"gt
• testVol.lat
• lt/paramgt
• lt/modulegt
• ltmodule idiso
• name"IsosurfaceLat"
• in-port"Input"gt
• ltparam name"Threshold"
• min"0" max"27"gt
• 1.8lt/paramgt
• lt/modulegt
• lt/linkgt

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Diagrammatic Representation using SVG

• skML gives us an XML application for
  visualization at the conceptual layer
• In addition to language representation, a
  diagrammatic representation has been created in
  SVG so we can do dataflow programming in a web
  browser

lt?xml version"1.0"?gt ltskmlgt ltmapgt ltlinkgt
ltmodule name"ReadLat out-port"Output"gt
ltparam name"Filename"gt testVol.lat
lt/paramgt lt/modulegt ltmodule
idiso name"IsosurfaceLat"
in-port"Input"gt ltparam name"Threshold"
min"0" max"27"gt 1.8lt/paramgt
lt/modulegt lt/linkgt

• Transforming to the logical layer binds in the
  software resource
• A new IRIS Explorer module can read skML and
  generate corresponding map
• skML can also be turned into an IBM Open
  Visualization Data Explorer network

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Physical Layer Secure Distributed IRIS Explorer

• Moving to the physical layer, we need to be able
  to execute modules on remote Grid resources
• IRIS Explorer has been extended to allow a user
  to place modules on specific compute resources
  dataflow pipeline thus spans the Grid
• Compute-intensive modules can be placed remotely
  - design the dataflow for the Grid

IRIS Explorer on multiple hosts

• Automatic authentication using
• Globus certificate
• SSH Key pair

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Next Steps

• Some tangible benefits
• Next release of IRIS Explorer will include the
  distributed execution facility
• but much remains to be done

• Conceptual level
• Visualization ontology needed to define and
  organize set of canonical processes
• Useful to include resource constraints (initial
  steps made with RDF)
• Logical level
• Visualization data exchange between systems needs
  to be studied
• Initial steps made by Julian Gallop (this
  conference)
• Physical level
• User allocation of modules to resources needs to
  be replaced by a brokering service

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Computational Steering
visualization environment

• Computational steering requires a link between a
  visualization environment and a simulation
  environment
• gViz library provides this glue
• Design aims
• Use with different simulation environments and
  different visualization environments
• Allow connect and disconnect
• Lack of intrusion and minimize performance loss
• Robustly handle different producer-consumer rates
• Support multiple simulations
• Support collaboration
• Support historical audit trail

control
visualize
simulation environment
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Environmental Application

• Demonstrator created for an environmental crisis
  scenario
• Dangerous chemical escapes!
• Model dispersion using system of PDEs and solve
  numerically over mesh
• Visualize mesh elements where concentration
  exceeds threshold
• What happens when the wind changes?
• faster-than-real-time
• Simulation environment
• Finite volume code written in C

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Pollution Simulation Using the gViz Library and
IRIS Explorer
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IRIS Explorer as Visualization Environment

• Distributed module execution
• Allows visualization modules to be collocated
  with simulation to minimize data traffic to
  desktop
• Collaborative visualization
• Allows the COVISA multi-user visualization
  facility to be exploited

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Pollution example with other visualization
environments

• Different visualization environments can be
  connected through gViz library to the underlying
  simulation
• Note that multiple users with multiple
  visualization environments can connect
  allowing collaboration amongst a team

SCIRun
vtk
Matlab
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Computational Biology

• In another application the gViz library provides
  monitoring and control of heart modelling
  experiments Arun Holden Richard Clayton
• Multiple simulations of electrical activity of
  the heart

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gViz Anatomy
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Or with Matlab as Visualization Environment
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Or with Grid/Web Services approach

• Grid service interface to gViz library
• Heart Modelling Grid Service uses
• Web interface where user specifies user name and
  passphrase, and location of gViz directory
  service
• Grid service connects to simulations to allow
  steering parameters to be sent, and results to be
  retrieved, via the gViz library
• A second grid service builds images from
  simulation data
• Returned as a Web page

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gViz meets Integrative Biology

• The application to heart modelling continues in
  the Integrative Biology project with David
  Gavaghan
• Here Matlab is the simulation environment
• .. linked by gViz library to IRIS Explorer as the
  visualization environment
• or indeed Matlab can act as the visualization
  environment
• Reality Grid steering also being used in IB
  project, so hope is to gain convergence between
  the two approaches

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Conclusions

• The gViz project has begun to explore the issues
  in evolving visualization systems to Grid
  environments
• Tangible benefits
• Secure distributed IRIS Explorer in next release
  from NAG
• gViz library code will be made available as open
  source (LGPL)
• Raising issues
• Ontology
• Visualization data exchange
• Visualization brokering service
• Continuing development of gViz library within
  Integrative Biology with potential convergence
  with RealityGrid steering library
• Demonstration WRG Stand, Friday 10.30 14.30

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Acknowledgements

• The gViz project team has involved many people
• Leeds University Ken Brodlie, Jason Wood, Chris
  Goodyer, Martin Thompson, Mark Walkley, Haoxiang
  Wang, Ying Li, James Handley, Arun Holden,
  Richard Clayton (now Sheffield)
• Oxford Brookes University David Duce, Musbah
  Sagar
• Oxford University Mike Giles, David Gavaghan
• CLRC/RAL Julian Gallop
• NAG Steve Hague, Jeremy Walton
• Streamline Computing Mike Rudgyard
• IBM UK Brian Collins, Alan Knox, John
  Illingworth
• CACR, Caltech Jim Pool, Santiago de Lombeyda,
  John McCorquadale
• MIT Bob Haimes
• Development environment at Leeds White Rose Grid
  e-Science Centre of Excellence