1. 12.1 Vis_04 Data Visualization Lecture 12 Visualization Software Environments: - Overview of Major Systems - Distributed and Collaborative Visualization

2. 12.2 Vis_04 Visualization Software Environments n IRIS Explorer is one of a family of similar visualization systems n AVS, 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

3. 12.3 Vis_04 Convergence of Technologies Demand for interactive exploration of large datasets from simulations & measurement Systems made possible late 1980s by: n Visual programming – technology waiting for application - dataflow ideal for visualization because of pipeline n Visualization algorithm development – 3D scalar and vector visualization n Network based window systems – easy to use distributed computing n Faster graphics workstations

4. 12.4 Vis_04 AVS n Released 1989 by Stellar as software to help sell graphics workstations n Now software company with range of products: – AVS5, AVS/Express, Gsharp – www.avs.com

5. 12.5 Vis_04 IRIS Explorer n Released 1991 by Silicon Graphics, bundled free with every Indigo workstation n Now developed and distributed by NAG Ltd, Oxford – runs on UNIX and Windows – IRIS Explorer Centre of Excellence at Univ of Leeds – www.nag.co.uk – www.comp.leeds.ac.uk/iecoe

6. 12.6 Vis_04 IBM Open Visualization Data Explorer – now OpenDX n Released around 1991 by IBM n Made open source in 1999 – www.opendx.org

7. 12.7 Vis_04 Amira n More recent product n Increasing use for medical applications.. n.. But also engineering including CFD n Marketed by TGS n www.tgs.com

8. 12.8 Vis_04 vtk - Visualization Toolkit n vtk is a programming - based toolkit n Open source C++ library n www.kitware.com

9. 12.9 Vis_04.. And there are many others http://gd.tuwien.ac.at:8050/D/1/

10. 12.10 Vis_04 Distributed and Cooperative Visualization Extending existing systems to new computing developments

11. 12.11 Vis_04 Scenario: Release of Toxic Chemical The fugitive pollutant … where is it headed for? Model the dispersion and solve the PDEs… Visualize cells where concentration exceeds safety limit

12. 12.12 Vis_04 The Starting Point n Traditional data visualization approach is to decompose into: – Read in data – Construct a visualization as geometry – Render the geometry as an image n Facilitated by modular visualization environments – such as IRIS Explorer – using concept of dataflow pipeline data visualize render

13. 12.13 Vis_04 Simulation and Visualization n Traditional approach is: – Perform the simulation – Perform the visualization data visualize render Step two Simulation Step one

14. 12.14 Vis_04 Creating Your Own Modules n It is possible to create your own modules in IRIS Explorer n The mbuilder tool creates a wrapper around your own code n See: www.nag.co.uk/visual/IE/iecbb/DOC/Unix/ Doc/MWG/CONTENTS.htm

15. 12.15 Vis_04 Computational Steering n Greater flexibility is achieved if we integrate the simulation and visualization in the same pipeline simulatevisualize rendercontrol Online control of simulation and visualization - becomes possible in IRIS Explorer through ability to create own modules

16. 12.16 Vis_04 Tracking the Pollution Control: wind direction widget Simulate: finite volume code running as an IRIS Explorer module Visualize: select cells where concentration exceeds threshold Render: draw the cells

17. 12.17 Vis_04 Improvements needed n Need to harness external compute resources n Need to bring in wider expertise through collaboration

18. 12.18 Vis_04 Harnessing Remote Compute Resources Explorer on single host Explorer on multiple hosts Select remote host Automatic authentication using: Globus certificate SSH Key pair

19. 12.19 Vis_04 Simulation Runs Remotely Control: desktop Simulation: remote Cell extraction: remote (so as to minimize network load) Cell filling: local Render: local

20. 12.20 Vis_04 internet data visualize render Collaborative Data Visualization n Extends the dataflow model to interlink pipelines across the Internet n Collaborative server provides the link n So one user – for example - can send geometry to another person for viewing collaborative server share render

21. 12.21 Vis_04 Programming the Collaboration n It is useful to be able to program the collaboration – To adapt to how people want to collaborate – To adapt to network bandwidths n Here raw data is exchanged so a different visualization can be created internet collaborative server data visualize render share visualise render

22. 12.22 Vis_04 IRIS Explorer COVISA Toolkit

23. 12.23 Vis_04 Bring in the Meteorologist Remotely Scientist in lab Initiate collaborative session Link in meteorologist remotely

24. 12.24 Vis_04 Data Visualization End of Part 1