# Presentation: 349353

**Science-Driven Visualization**
**Research Challenges**
**9 Nov 2004**
**SC2004 – Pittsburgh PA**
**Wes Bethel**
**with help from Friends at**
***Lawrence Berkeley National Laboratory***
- vis.lbl.gov

**Notes:**
Slide 1 notes.

## Outline

**Science-driven Visualization Challenges.**
**LBNL Visualization Research**
- Remote, Distributed and High Performance Visualization.
- Domain-specific solutions for scientific research.
- Computer Science research.
**Conclusion and Future Directions**

**Notes:**
Typical collaboration of discipline researcher and vis/VR person – they ask the impossible.

## Outline

**Outline**

**Science-driven Visualization Challenges.**
**LBNL Visualization Research**
- Remote, Distributed and High Performance Visualization – Introduction and Approach.
- Domain-specific solutions for scientific research.
- Computer Science research.
**Conclusion and Future Directions**

**Notes:**
Typical collaboration of discipline researcher and vis/VR person – they ask the impossible.

## Science-Drive Visualization Challenges – Outline 

**Role of visualization in science, and what users really want?**
**Challenges of user needs.**
**What efforts targeted at meeting those needs?**
**Is the current approach meeting user needs?**

## Role of Visualization in Science

**An instrument to “see data” that is otherwise unseeable.**
**A vehicle to communicate findings and results.**
**Plays an integral part of the scientific process and scientific workflows.**

- Something doesn’t “look right” in this picture – what happened?

## Introduction – The Scientific Process and Workflows

**Hypothesize – experiment/test – refine.**
**Workflows are the sequence of tasks in the scientific process.**
**Visualization serves as the “instrument” to aid in “seeing results” at each stage in the workflow.**

## What Do (Science) Users Need?

**Easy to use software.**
**That is free (and works).**
**That is supported.**
**Help learning/using/applying the software to their problem. **
**New visualization capabilities for their problem.**
**Support for remote and distributed operations, capacity to analyze large and complex data.**

## Challenges of User Needs

- For many modern computational science projects, there exists no “canned” visualization solution. Tools and technology 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.

## Science-Drive Visualization Research Problem Statement

**Trend is towards remote and distributed data analysis and visualization.**
**Domain-specific solutions required.**
**Such solutions are inherently multidisciplinary and extremely complex.**

## Efforts Targeted at Meeting Science Needs

**Individual P.I. Funded to perform some visualization research.**
- A fraction of a P.I. and a graduate student.
- Publish a research paper, might release a research prototype of their software (or might not).
- Their reward is the technical publication.
**Institutional visualization support.**
- NERSC, ASCI/Views, etc.
**Missing: large, program-wide coordination of activities.**

## Does the Current Approach Work?

## Does the Current Approach Work?

**Sloan Digital Sky Survey Portal**
**Interface and operations tailored to astronomy community.**

## Does the Current Approach Work?

**Generally, no:**
- Duplication of effort across disparate programs.
- Little impetus to share work, to leverage others work.
**What’s Missing?**
- Critical visualization infrastructure: community-centric data models, fungible visualization technology that can be shared and reused across program areas.
- Program-wide emphasis upon coordinated visualization activities.
- Requires conscious engineering – coordinated activities will not “emerge” from many small visualization projects.

## Outline

**Outline**

**Science-driven Visualization Challenges.**
**LBNL Visualization Research**
- Remote, Distributed and High Performance Visualization – Introduction and Approach.
- Domain-specific solutions for scientific research.
- Computer Science research.
**Conclusion and Future Directions**

**Notes:**
Typical collaboration of discipline researcher and vis/VR person – they ask the impossible.

## LBNL Visualization Research – Outline 

**The LBNL Visualization Research Vision.**
**The Research Strategy and Tactics.**
**Near-term and long-term goals.**
**Results:**
- Domain-specific solutions.
- Remote and Distributed visualization research results.
- Computer Science Research.

## LBNL Visualization Research Vision

## Problem Statement – Repeated 

**Trend is towards remote and distributed data analysis and visualization.**
**Domain-specific solutions required.**
**Such solutions are inherently multidisciplinary and extremely complex.**

## Research Challenges for Remote and Distributed Visualization

**Community-centric data models, component interfaces, execution frameworks.**
**Visualization algorithms, delivery mechanisms.**
**Effective and simplified use of parallel and distributed resources.**

## LBNL Visualization Research Strategy

**Map the canonical visualization pipeline into remote & distributed use model.**

## LBNL Visualization Research Tactics

**Close relationships with DOE science projects to deliver domain-specific (useful) technologies.**
**Research advances on the visualization pipeline to realize the dream of “vis anywhere, anytime, by anybody.”**
**Fundamental CS research to complement visualization research.**

## LBNL Visualization Research Tactics 

**Components encapsulate algorithms, frameworks marshal data and mediate execution (see HECRTF).**
**Bottom-up: focus on specific application-driven projects. E.g., Accelerator SciDAC.**

## LBNL Visualization Research Tactics

**Distributed and parallel architectures offer new algorithmic opportunities (Visapult).**
**Interaction methodology important for large data exploration, cuts across data management, visualization, applications.**
**Delivery mechanisms are “the handles” provided to the user to guide data exploration and analysis.**

## Outline

**Outline**

**Science-driven Visualization Challenges.**
**LBNL Visualization Research**
- Remote, Distributed and High Performance Visualization – Introduction and Approach.
- Domain-specific solutions for scientific research.
- Computer Science research.
**Conclusion and Future Directions**

**Notes:**
Typical collaboration of discipline researcher and vis/VR person – they ask the impossible.

## Domain-Specific Solutions

**21****st**** Century Accelerator Modeling (SciDAC)**
**Center for Extended MHD (SciDAC)**
**Protein Structure Prediction**

## Accelerator Simulation Visualization

**Data: time-varying, 6D, multi-species.**
**Typical visualization: scatter plots of one dimension against another. E.g., x-position vs. x-phase.**
**Need: ability to explore, to subset, to visually comprehend science.**

## Accelerator Simulation Visualization, ctd.

**Interactive data subsetting and selection.**
- Paint metaphor
- Using domain knowledge.
**Novel visualization technique well-suited for 6D data (next slide).**

## Accelerator Simulation Visualization, ctd.

## Accelerator Simulation Visualization, ctd.

**Proton beam (particles) passing through a cloud of electrons (volume rendering).**

## Accelerator Simulation Visualization, ctd.

**Electron trajectories**

## Accelerator Modeling: Remote and High Performance Visual Analysis

**User-requested domain-specific tool for browsing data.**
**Distributed, pipelined architecture to scale with increasing data sizes.**

- workstation

- Remote data
- storage

## Accelerator Modeling: Remote and High Performance Visual Analysis

**Our group engineered a HDF5 file format for the computational scientists.**
- They were using ASCII files.
**Our group also engineered parallel I/O capabilities using HDF5.**
**A common data model/format is the basis for a family of high performance analysis software technology.**

## Accelerator Modeling Visualization: Conclusion

- Close interaction with scientists resulted in domain-specific technologies as well as new visualization research.
- The “unglamorous work” of data models/formats and I/O is the underpinning for the much of the project.
- We are in a good position to move forward with additional tools based upon a community-centric data model.

## Remote Visualization of Fusion Simulation Results

**Problems: **
- Simulations run at centralized supercomputing facilities generate large, complex data.
- Analysis to be performed by remotely located scientists.
- Science teams are themselves geographically distributed, and have requested some form of collaborative investigation/visualization.

## Remote Visualization of Fusion Simulation Results

**Approach:**
- Use high performance, parallel resources located “close to” the data.
- Where plausible, retain the high performance rendering capabilities of desktop workstations.
- Partition the visualization pipeline (more later) across sites in multiple ways. Which works best?

## Fusion Visualization: Pipelined, Distributed and Parallel Architecture

- Mass
- Storage

- Parallel
- Visualization
- (Compute, I/O)

- POW
- (Plain old workstation)

- Princeton

- Berkeley

- Data

- Vis

- Render

- 10GB/timestep     10sMB

## Fusion Visualization: Pipelined, Distributed and Parallel Architecture

- High capacity I/O and compute located “near” large data source.

## Collaborative Visualization

**Rapid inspection of data too large to move:**
**Saves having to transfer 100s of GB across country.**
**Multiple simultaneous participants (roundtable model).**

- Data

- Vis

- Render

- 10GB/timestep     10sMB

## Remote Fusion Simulation Visualization – Sending Images

**~50fps 800x600, 24-bpp**
- Over 100BaseT, low latency connection (LAN)
- Freely running image generator – only framebuffer contents sent; no mouse events, etc.
- Frame rate relatively insensitive to compression algorithm, as long as some compression is used.
**4-5fps “full screen interactive application”**
- 100BaseT Ethernet, 50ms latency (WAN between LBNL – PPPL)
- Interactive application.

- Data

- Vis

- Render

- 10GB/timestep     10sMB

## 4-5fps “not unexpected”

**50 ms one-way latency is 100ms RTT**
**Maximum possible frame rate: 10fps**
**Add in more latency due to fb reads, detect and package mouse events, etc. **
**Conclusion: latency is a killer.**

## Frame Rate Limit Due to Latency:1000/2*latencyMS.

- Time

- 50ms

- 50ms

- A

- B

- C/A

- B

- A – user drags the mouse, mouse event sent to server.
- B – “instantaneous” frame render, grab, compress, send and
- 	receipt by client.
- C – client decompresses, displays image, grabs next mouse
- 	event, etc.

## Fusion Visualization: Conclusions

**Using high capacity visualization resources located “close to” the source data for remote use appears promising.**
**Different approaches, each with advantages and disadvantages.**
**Functional results: good.**
**Performance results: mixed. **

## Protein Structure Prediction Outline

**Problem Description.**
**Approaches to help solve an NP-hard problem: **
- Better initial configurations.
- Visualization and intervention to guide optimizations.

## Protein Structure Prediction

**Challenges**
- Protein structure prediction is difficult (NP-hard) – it is one of the grand challenges in computational biology.
- Visualization and interactive techniques can accelerate the process.
- No “off-the-shelf” technologies exist – they must be created.

## Protein Structure Prediction, ctd.

**Given: an amino acid sequence,**
**Find: an optimal protein conformation.**

## Protein Structure Prediction, ctd.

**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

## Protein Structure Prediction, ctd.

**Protein Structure Prediction, ctd.**

## Protein Structure Prediction, ctd.

**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.**

## Protein Structure Prediction – Energy Visualization

**Energy gradient**
_**[(Movie)](file:///Bethel_1.mpg)**_

## Protein Structure Prediction – Energy Visualization

- _[Movie](file:///Bethel_2.mpeg)_

## Protein Structure Prediction – Conclusion

**Increased scientific capacity and capability.**
- CASP4 2000 – days; CASP6 2004 – hours.
**New scientific opportunities:**
- Multiple molecule interactions – drug design.
**Visualization impact:**
- Best Application Paper award, IEEE Visualization 2003.

## Outline

**Outline**

**Science-driven Visualization Challenges.**
**LBNL Visualization Research**
- Remote, Distributed and High Performance Visualization – Introduction and Approach.
- Domain-specific solutions for scientific research.
- Computer Science research.
**Conclusion and Future Directions**

**Notes:**
Typical collaboration of discipline researcher and vis/VR person – they ask the impossible.

## Computer Science/Visualization Research - Outline

**Research Challenges.**
**Query-based visualization.**
**Desktop delivery R&D.**
**Remote and distributed visualization pipeline optimization.**

## Fundamental Remote and Distributed Visualization Research Challenges

**Fungible technologies for creating visualization applications.**
- Components, data/application adapters, vis-centric network transport, resource discovery/allocation, dynamic application construction, decoupling UI from vis/analysis “engine,” decoupling execution control from component architecture.
**Community-centric data models.**
**Multi-resolution and progressive analysis/vis.**

## Fundamental Remote and Distributed Visualization Research Challenges, ctd.

**More interactions with other communities: science applications, data management and data analysis.**
**Long-term deployment and maintenance strategy.**
**Community and programmatic focus on technology interoperability.**

## Query-Driven Visualization (Dex)

**Combine Visualization with SDM technology to accelerate visualization and analysis.**
**Select data based upon boolean queries.**
**Only visualize/analyze data that meets query criteria.**

## Remote Desktop Delivery – Thin Client

**QuickTime VR**
- _[Panorama Movies](file:///Bethel_3.mov)_
- Object Movies
- _[Two axis](file:///Bethel_4.mov)__, __[time-varying.](file:///Bethel_5.mov)_
**QTVR:**
- Industry standard
- Freely available players (except Linux!).
**LBNL Contribution**
- Object-movie encoder.
- Current research – multi-resolution-capable.

## Visualization Pipeline Optimization

**Context: many heterogeneous, distributed resources.**
**Goal: user wants to take advantage of distributed resources to solve a problem.**
**Problem(s): need to select a set of resources to meet the task at hand.**

## Visualization Pipeline Optimization

**Problem: component placement on distributed resources changes as a function of both performance target and specific data.**
**Problem: distributed applications launched “by hand,” resource placement performed “by hand.”**

## Performance Modeling and Pipeline Optimization

**Approach: model performance of individual components, optimize placement as a function of performance target.**
**Goal: automate the process of placing components on distribute resources.**
**Results: quadratic order algorithm, high degree of accuracy.**

## Performance Modeling and Pipeline Optimization

**Render Remote**
- Move images:
- setenv DISPLAY
- SGI’s Vizserver
- Data too big to move.
**Render Local**
- Move data
- ftp, scp
- Logistical networking
**Hybrid approaches**
- Move “vis results” for local rendering
- CEI’s Ensight, Visapult

- Render

- Render

- Render

## Pipeline Optimization – User View

**Goal: simplify use of distributed visualization resources.**

## Visualization Pipeline Optimization – Overview

**Obtain/derive performance measurements for pipeline components.**
**Automatically select placement of tasks on distributed resources to meet performance objectives.**

## Performance Modeling and Pipeline Optimization

**Single workflow:**
- Reader -> Isosurface -> Render
**Reader performance: **
- Function of:
- Data Size
- Machine constant
*T**reader** (n**v**) = n**v** * C**reader*

## Performance Modeling and Pipeline Optimization

**Render Performance:**
- Function of:
- Number of triangles,
- Machine constant.
*T**render** = n**t** * C**render** + T**readback*

## Performance Modeling and Pipeline Optimization

**Isosurface Performance:**
- Function of:
- Data set size,
- Number of triangles generated (determined by combination of dataset and isocontour level).
- Dominated number of triangles generated!
*T**iso**(n**t**,n**v**) = n**v** * C**base** + n**t** * C**iso*

## Performance Modeling and Pipeline Optimization

**Precompute histogram of data values.**
**Use histogram to estimate number of triangles as a function of iso level.**

## Performance Modeling and Pipeline Optimization

**Performance targets:**
- Optimize for interactive transformation.
- Optimize for changing isocontour level.
- Optimize for data throughput.

## Performance Modeling and Pipeline Optimization

**Pipeline Configurations:**
- Render local – send data to workstation.
- Render remote – send images to workstation.
- Hybrid – send triangles to workstation.

## Performance Modeling and Pipeline Optimization

**Optimize placement using Djikstra’s shortest path algorithm.**
**Edge weights assigned based upon performance target.**
**Low-cost algorithm:**
- O(E + VlogV)

## Performance Modeling and Pipeline Optimization - Conclusions

**“****Microbenchmarks” to estimate individual component performance.**
- Per-dataset statistics can be precomputed and saved with the dataset.
**Quadratic-order workflow-to-resource placement algorithm.**
**Optimizes pipeline performance for an specific interaction target – relieves users from task of manual resource selection.**

## Outline

**Outline**

**Science-driven Visualization Challenges.**
**LBNL Visualization Research**
- Remote, Distributed and High Performance Visualization – Introduction and Approach.
- Domain-specific solutions for scientific research.
- Computer Science research.
**Conclusion and Future Directions**

**Notes:**
Typical collaboration of discipline researcher and vis/VR person – they ask the impossible.

## Conclusions

**Close collaboration with applications produces usable, focused visualization technologies.**
**Such collaborations are long-term relationships.**
- How to formalize and sustain such relationships?

## Conclusions

**Component-based development holds much promise (see HECRTF).**
**Underpinnings:**
- Community-centric data models.
- Interactive, parallel, distributed execution framework.

## Conclusions

**Opportunity to move towards technology sharing and reuse, especially for visualization community.**
**Produce usable, long-lived visualization technology for applications.**
**Need for cross-program bridges – one form is  stable infrastructure underpinnings based upon  common component interfaces and community centric data models. **

## Summary

**LBNL has a world-class Visualization R&D program that has a balanced and effective having an emphasis upon remote, distributed and high performance visualization, and meeting the needs of science.**
**Visit us on the web at http://vis.lbl.gov/**
**This work was supported by the Director, Office of Science, of the U.S. Department of Energy under Contract No. DE-AC03-76SF00098.**** **

## The End