1. LBNL Visualization Group Research Snapshot Wes Bethel Lawrence Berkeley National Laboratory Berkeley, CA 24 Feb 2004

2. Outline Overview: —Group Profile —What is Scientific Visualization? Why is it significant in the scientific process? Case Studies: —Geosciences —Computational Biochemistry —Remote and Distributed Visualization Challenges and Future Directions

3. Profile Group formed in 1990 to help accelerate science at LBNL (Stellar GS-1000, Ardent Titan). —Earth Sciences focus, Virtual Reality and interactive techniques. Arrival of NERSC in 1996 added production visualization to activity portfolio. —Increased focus on remote and distributed visualization. —Acute challenges of very large scientific data. Personnel —Three to five full-time staff. —Two UC Davis faculty with appointments. —Rotating herd of four to six students.

4. Role of Visualization in Science Visualization provides the means to “see” data in order to create the opportunity for scientific insight. Data is often abstract, dimensionless, and unfamiliar. Rapid exploration of large and complex data sets. Find “interesting things,” serendipitous browsing, “Ah-Ha!” and “Uh, what’s that?” discoveries. Communicate findings to colleagues, funders and the public. Something doesn’t “look right” in this picture – what happened? Data Visualization Display

5. Case Study: Earth Sciences Project: Interactive visualization of simulation results (UTCHEM), coupled with Virtual Reality tools. (Movie) (Movie) Objective: reduce time-to-solution (understanding) through a combination of human-in-the-loop and interactive technologies. Spinoff projects: Advanced Computational Technology Initiative (ACTI), LDRD (Pruess, et. al, visualization of simulation results; geohydrodynmics and reactive chemistry).

6. Case Studies: Computational Biochemistry Protein Folding Problem: what is the minimal-energy structure of a sequence of amino acids? Solution: Nature knows, but computing an answer is NP-hard (not solvable). Approach: Human-guided setup, computer-aided energy optimization and minimization. Conf: 99999999999999999999999999999999999 Pred: HHHHHHHCCCEEEEEEECCCEEEEEEEECCCCCCC AA: FKQYANDNGVDGVWTYDDATKTFTVTEMVTEVPVA

7. Computational Biochemisty, ctd. Given: an amino acid sequence, Find: an optimal protein conformation.

8. Computational Biochemistry, ctd.

9. Optimization and computational steering Initial configurations used as “seed points” for optimization. Intermediate results – the “search tree” – is displayed for inspection. A human may intervene in the optimization.

10. Computational Biochemistry, ctd. Visualization of Energy During Optimization Movie

11. High Performance Remote and Distributed Visualization: Visapult Motivation: remote and interactive visualization of large scientific data over a wide area network. Framework and application for remote direct volume visualization of large structured mesh data. Interactive performance on desktop completely divorced from network bandwidth via Image Based Rendering techniques. Architecture: Source Volume O(n**3) O(n**2) Data Visualization Display

12. Visapult – Wins SC Bandwidth Challenge Three Years in a Row – Undefeated Champion! SC00 – Dallas TX. 1.54Gbps peak rate. 582Mbps sustained. Single OC/48 line (2.48Gbps). 1.5Gbps “bottleneck” over Qwest. 100% peak efficiency. 33% sustained efficiency.

13. Visapult, SC2001 Redesign to use UDP – connectionless 3.3Gbps sustained. OC48 (2.48Gbps) + 2x OC12 (1.48 Mbps) 83% sustained efficiency.

14. Visapult, SC2002 Tuning the connectionless protocol for better efficiency. Improvements in visualization capabilities. Large, multinational team: US, Netherlands, Czech Republic. 2x OC-192 (10Gbps), 1x OC-48 (2.48Gbps).

15. Visapult SC2002, ctd. 16.8 Gbps sustained. 75% efficiency Didn’t have enough remote resources to fill all available network pipes.

16. Visapult Discussion UDP vs. TCP: UDP is “lossy,” while TCP guarantees delivery of packets. How does “lossiness” combined with our progressive update algorithm affect usability?

17. Visapult Discussion, ctd. Technically, Visapult was a success: —High performance remote visualization of large scientific data with a novel latency tolerant algorithm. Practically, there are some issues: —Manual launching of multiple components. —Diagnosing and repairing network problems. —Not a practical end-user tool.

18. Future Directions Visportal: simplifies user access to distributed, component-based tools. Optimizing dynamic placement of components in the visualization pipeline. Integration of disparate visualization resources: —Interface definitions —Data model standards Effective remote and distributed visualization: —Workflow analysis. —Visualization algorithms. —Data management issues.

19. Challenges Given: User needs: (1) easy to use software, (2) that is free, (3) that is supported (and actually works), (4) institutional support for visualization, (5) new visualization capabilities, (6) support for remote and distributed operations, etc. Challenges: For many modern computational science projects, there exists no “canned” visualization solution. Tools must be created. Such efforts require expertise in a wide range of specialties: computer science, software engineering, cognitive science, people skills, etc. Creating such tools requires close and ongoing effort between researchers of many disciplines. Few, if any, “standards” to help provide a stable environment for visualization.

20. The End