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ENV 200616.1 Envisioning Information Lecture 16 – Distributed and Collaborative Visualization Ken Brodlie

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Presentation on theme: "ENV 200616.1 Envisioning Information Lecture 16 – Distributed and Collaborative Visualization Ken Brodlie"— Presentation transcript:

1 ENV Envisioning Information Lecture 16 – Distributed and Collaborative Visualization Ken Brodlie

2 ENV Outline of Lecture From Visualization to Computational Steering Distributed visualization –Extending dataflow across the network –Grid-based visualization and computational steering Collaborative visualization –Sharing the display screen –Sharing the visualization

3 ENV 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 data visualize render

4 ENV IRIS Explorer is one of a family of similar visualization systems First product was AVS –Still major player but no longer visual programming – Amira, IBM Open Visualization Data Explorer (DX), IRIS Explorer –visual programming based : plug, play, throw away –application decomposed as set of modules, configured at run-time (blur between building and running an application) –open : user can write modules –low-cost Visualization Software Environments

5 ENV IBM Open Visualization Data Explorer – now OpenDX Released around 1991 by IBM Made open source in 1999 – A major use of it has been for weather visualization

6 ENV Amira More recent product Increasing use for medical applications.... But also engineering including CFD Marketed by TGS

7 ENV vtk - Visualization Toolkit vtk is a programming - based toolkit Open source C++ library

8 ENV And there are many others

9 ENV IRIS Explorer - Creating Your Own Modules It is possible to create your own modules The mbuilder tool creates a wrapper around your own code See:

10 ENV IRIS Explorer can be driven either by GUI or by command line interface Commands can be grouped as a script that IRIS Explorer runs –explorer -script This allows Explorer to be run in batch mode, or to be driven by another application The scripting language is called Skm (pronounced as scheme) Can be used interactively… … in linux –explorer -script % … in Windows, –use Skm editor (view menu) Scripting - skm

11 ENV To launch a module: (start ReadImg) (start DisplayImg) To connect ports: (connect ReadImg Output DisplayImg Input) To start a map: (start-map cfd) See chapter 6 of User Manual on Web Creating a Simple Script

12 ENV Visualization and Simulation Visualization is a key tool in understanding the results of numerical simulations of complex physical phenomena Different modes of combining simulation and visualization: –Post-processing –Tracking –Steering

13 ENV Linking Visualization and Simulation – Post Processing Post-processing –Do the simulation and store results (step 1) –Look at the results in a separate process (step 2) –Revise the simulation (back to step 1) simulation data visualize render Step 1 Step 2 PRO: study at your own pace CON: must finish simulation first

14 ENV Linking Simulation and Visualization - Tracking Tracking –Exploit extensibility of the dataflow visualization environment by including the simulation in the pipeline –Track the behaviour of simulation as it runs simulate visualize render PRO: can abort fruitless simulations

15 ENV Linking Simulation and Visualization - Steering Computational steering: –By including a control module in the pipeline, we can direct the simulation in response to the visualization simulatevisualize rendercontrol PRO: not only can we track, we can alter the actual course of the simulation Human-in-the-loop

16 ENV Computational Steering Environments Early visualization systems all have this extensibility feature and so can be used for steering –IRIS Explorer for example New systems have emerged specifically to support steering –SCIRun from Utah Pressure profile for EHL contact

17 ENV Imagine this …. An explosion! A dangerous chemical escapes! Where is the fugitive pollutant headed? Who needs to be evacuated?

18 ENV Understanding What Will Happen Model the dispersion by solving system of PDEs Understand solution by visualization What if scenarios … need to be able to steer the simulation For example, what if the wind changes direction?

19 ENV Tracking the Pollution

20 ENV What can be Steered? Steering requires the writer of the simulation code to expose parameters that can be legitimately modified in the course of a run –frequency of output of results –values of external influences that may vary over a simulation Not all parameters can be changed –time step used by numerical codes to achieve stability and/or accuracy Notion of backtracking is important in some simulations –Often you first observe, then wish to rewind a few timesteps, then replay with different parameter settings

21 ENV Our Scenario We shall use this scenario to illustrate: –Distributed visualization : we need to understand where the pollutant is headed in faster than real-time … therefore we need to run the simulation on a powerful compute resource –Collaborative visualization : there is no time to collocate the scientist, the meteorologist, the politician or whoever needs to be involved … so we need to link people in over the network to allow them to visualize collaboratively … while still using IRIS Explorer!

22 ENV Harnessing Remote Compute Resources – Grid Computing Explorer on single host Explorer on multiple hosts Select remote host Automatic authentication using: Globus certificate SSH Key pair

23 ENV Simulation Runs Remotely

24 ENV A Tale

25 ENV The Monkey Gets the Nuts – Two Heads ARE Better than One Thanks to Accra Academy, Ghana

26 ENV Collaborative Working Radical collocation has proved highly successful in a number of areas –Space missions –Safety critical software development Productivity doubled –Teasley et al, Univ of Michigan But this requires: –Social disruption –Advance planning –… and can end in tears Can we gain at least part of this success using electronic collaboration?

27 ENV Visualizing Collaboratively We need to move away from seeing collaborative visualization as a group around a display screen.... Towards collaboration over a network Collaborative visualization

28 ENV Collaborating in the Pollution Demonstrator Who needs to collaborate and in what way? Scientists and numerical modellers –Discuss amongst each other possible scenarios –Discuss need to pull in further Grid resources perhaps Meteorologist –Will play an active part in controlling the simulation Environmental agency decision makers –Need to analyse what-if scenarios and construct presentations for politicians Politicians, local authorities –Want to see clear presentation of consequences –Probably not interested in steering

29 ENV Sharing the Display Screen A very simple model is to broadcast the display screen of an application to a set of (passive) users –Operating system level –Screen image is broadcast using intelligent compression –Only active user can enter input data visualize render internet User A executes application User B receives copy of user A desktop - does not execute application data visualize render

30 ENV Sharing the Display Screen There are a number of available technologies for screen sharing VNC – Virtual Network Computing –Family of open source products evolved from original VNC development by AT&T –RealVNC : (original development team) –tightVNC : (new compression algorithms) –Heterogeneous Microsoft NetMeeting (and now MSN Messenger)

31 ENV Sharing Display Screen Advantages –Very simple concept – works for any application –Good for training –Good for presentation to a group Disadvantages –No independent working –Performance issues when rapid screen changes Variations –(1) Only one master – only one can control by mouse and keyboard input –(2) Any participant can input

32 ENV Sharing the Visualization This is a more flexible approach – and specific to dataflow visualization Each collaborator is an active participant in the visualization process Multiple, interlinked applications, where each collaborator runs their own application but data and parameter settings are programmed to be shared between the different applications

33 ENV internet data visualize render Sharing the Visualization Extends the dataflow model to interlink pipelines across the Internet Collaborative server provides the link So one user – for example - can send geometry to another person for viewing collaborative server share render

34 ENV Programming the Collaboration It is useful to be able to program the collaboration –To adapt to how people want to collaborate –To adapt to network bandwidths Here raw data is exchanged so a different visualization can be created internet collaborative server data visualize render share visualise render

35 ENV COVISA in action sharing isosurface level sharing data Collaborator A Collaborator B

36 ENV Multiple, Interlinked Applications COVISA part of IRIS Explorer Advantages –Great flexibility –Independent working Disadvantages –Difficult to understand what the other user is doing

37 ENV Bring in the Meteorologist Remotely Scientist in lab Initiate collaborative session Link in meteorologist remotely

38 ENV Conclusions We have studied many aspects of scientific visualization: –Applications and history –Different techniques for scalar and vector data –Distributed and collaborative visualization The practical work is giving experience in –Exploratory visualization (what is going on?) –Presentational visualization (heres what is going on!) Finally, this afternoon, two case studies –Exploration using parallel coordinates –Focus and context for volume visualization

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