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e-Viz

e-Viz. Towards an Adaptive Framework for Visualization on the Grid. e-Vizzers. e-Viz is a three year joint research project funded by UK EPSRC Four partner universities: University of Leeds (Ken Brodlie, Jason Wood) University of Manchester (John Brooke, Mark Riding)

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e-Viz

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  1. e-Viz Towards an Adaptive Framework for Visualization on the Grid

  2. e-Vizzers • e-Viz is a three year joint research project funded by UK EPSRC • Four partner universities: • University of Leeds • (Ken Brodlie, Jason Wood) • University of Manchester • (John Brooke, Mark Riding) • University of Wales, Bangor (Nigel John, Chris Hughes) • University of Wales, Swansea • (Min Chen, David Chisnall, Mark Jones, Nicolas Roard) UW Bangor U Manchester U Leeds UW Swansea

  3. What problem are we trying to solve? Which are applicable to the data? Which is the most suitable? What’s fastest? Availability? Data Choose Visualization Algorithm Choose Software to Implement Algorithm Choose Hardware to run on Visualization Specialist e-Scientist Domain Scientist

  4. Why are we trying to solve this? • Because not all potential Grid visualization users are experts in visualization and Grid technologies.

  5. Why are we trying to solve this? Grid & Visualization Developers e-Scientists Grid Experience Application Scientists Visualization Specialists Visualization Experience

  6. Putting Visualization on the Grid • Can the Grid help? – it’s supposed to be: • heterogeneous in architecture • seamless and transparent in use • fault tolerant in operation • capable of adapting to the changing environment in order to provide the best service.

  7. Computation Rendering SMP Desktop NUMA Cluster Specialised What can the Grid offer? • Heterogeneity: architectures

  8. Linux Irix AIX Solaris BSD HP/UX Windows MacOS What can the Grid offer? • Heterogeneity: operating systems

  9. AVS OpenDX IRISExplorer VTK rtrt Simian Volumizer Matlab What can the Grid offer? • Heterogeneity: visualization software

  10. What can the Grid offer? • Heterogeneity: Implications • There are a wide variety of architectures, operating systems and software applications used in visualization. • Each has it’s own particular rewards and benefits, but we can’t expect every Grid user to be knowledgeable and proficient with each, or even to know which is the most appropriate for a given task. • Want to be able to offer the power afforded by each application to all potential users of visualization.

  11. What can the Grid offer? • Seamlessness and transparency: Implications • End users should only have to learn one interface, but still be able to benefit from the features offered by a wide range of applications. • But each visualization application has it’s own user interface… • … and every operating system looks and behaves differently

  12. What can the Grid offer? • Fault tolerance: Implications • “A distributed system is one in which the failure of a computer you didn’t even know existed can render your own computer unusable” – Leslie Lamport • “A Grid system is one in which the failure of a computer you didn’t even know existed goes by unnoticed” • Users should be able to rely on the Grid

  13. What can the Grid offer? • Fault Tolerance - User Scenario … keyboard, please

  14. What can the Grid offer? • Adaptation: Implications • The world is dynamic and ever changing; so is the Grid. • Network loads and status • Queues on HPC machines • Runtime limits on HPC machines • CPU loading, both remotely and locally • A Grid system should adapt to cope with changes

  15. How can we solve this problem?

  16. Data Visualization Image What is Visualization?

  17. Data Filter Map Render Image What is Visualization?

  18. Data Filter Map Render Image Controls What is Visualization?

  19. Data Visualization Image Controls What is Visualization?

  20. Data Visualization Image Controls Common Abstract Interface

  21. Format Conversion Frame Transport Data Visualization Image Computational Steering Controls Common Abstract Interface

  22. Format Conversion VTK AVS/Express Frame Transport Data Image IRIS Explorer Computational Steering Controls Common Abstract Interface

  23. Format Conversion Frame Transport Data Image AVS Simian VTK rtrt Computational Steering Controls Common Abstract Interface

  24. Common Abstract Interface • Feasibility • can we create equivalent visualizations using different software packages?

  25. Equivalent Visualizations?

  26. Equivalent Visualizations?

  27. Format Conversion Frame Transport Visualization Computational Steering Abstraction • Need to describe the pipeline itself – need an abstract visualization description language • gViz project gives us skML

  28. Format Conversion Frame Transport Visualization Computational Steering Abstraction • Format Conversion • Most visualization software applications can already read a wide range of data formats

  29. Format Conversion Frame Transport Visualization Computational Steering Abstraction • Computational Steering

  30. Abstraction • Computational Steering • RealityGrid or gViz APIs can be used to control a running pipeline • Visualization applications must be instrumented to expose their steerable pipeline parameters • Currently instrumented VTK and RTRT (real time ray tracer) • GUI created, which is really a specialised Computational Steering client • Reads in a pipeline description and dynamically configures itself to show appropriate widgets

  31. Format Conversion Frame Transport Visualization Computational Steering Abstraction • Frame Transport

  32. Abstraction • Frame Transport • Images can be rendered locally or remotely – in either case, they need to be displayed on the user’s own machine, as well as any collaborators • Have created a library to compress remote images and transport them back to the client for display. Visualization applications can be modified to use the library • Range of image compression codecs • Local rendering supported via a rendering overlay • Linux and Windows clients

  33. What do we still need? • Decision making • What visualizations are applicable for a given input data type • Which of the available hardware and software is most suitable to implement such a pipeline • User may not know the answer to these questions – provide assistance

  34. Client Broker Data Store Software Store HPV Machine HPV Machine HPV Machine HPV Machine HPV Machine Architecture WSRF Globus gViz e-Viz

  35. Client Broker Data Store Software Store HPV Machine HPV Machine HPV Machine HPV Machine HPV Machine Architecture (Current) WSRF Globus gViz e-Viz

  36. Demonstration 1 • gViz Pollution Demonstrator - shows • Active simulations as a data source • Computational steering of an active visualization • Common interface to servers

  37. Demonstration 1

  38. Demonstration 2 • Volume Render Demonstrator – shows • Heterogeneous access to multiple servers • (Almost) seamless switching between servers • Fault tolerance through redundancy

  39. Demonstration 2

  40. The Knowledge Gap • The Broker has a knowledgebase which it will use to make informed decisions on pipeline choices. • It’s empty!

  41. Data Filter Map Render Image How to Distribute?

  42. Data Filter Map Render Image How to Distribute?

  43. Filter Map Render Data Image How to Distribute?

  44. Filter Map Render Data Image How to Distribute?

  45. Filter Map Render Data Image How to Distribute?

  46. When to Distribute? • Depends on • Where the dataset resides • How big the dataset is • Network bandwidth • Available machines • Chosen algorithm • Queue status • CPU capabilities • Graphical capabilities • …..

  47. Other problems • Single point of failure in the broker

  48. Conclusions • e-Viz has created a framework for visualization on the Grid that is: • Heterogeneous in architecture • Seamless and transparent in use • Fault tolerant • Adaptive (potentially) • But much more to be done! (project runs another 16 months)

  49. For more information • See a live demo: • Tuesday 22nd, 14:00-16:30, White Rose Grid stand • Project Web Site: • http://www.eviz.org • email: • mark.riding@manchester.ac.uk

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