High Performance Visualization

High Performance Visualization: Enabling Extreme-Scale Scientific Insight

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ISBN 9781439875728
Cat# K13513
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ISBN 9781439875735
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Features

  • Covers the framework of distributed memory parallel systems
  • Presents advanced approaches for achieving high performance visualization
  • Explores emerging platforms and architectures, including GPU-based visualization techniques, hybrid parallelism, and exascale class computational platforms
  • Surveys several open source packages, such as VisIt, ParaView, and VAPOR, for implementing high performance visualization
  • Includes a 32-page color insert

Summary

Visualization and analysis tools, techniques, and algorithms have undergone a rapid evolution in recent decades to accommodate explosive growth in data size and complexity and to exploit emerging multi- and many-core computational platforms. High Performance Visualization: Enabling Extreme-Scale Scientific Insight focuses on the subset of scientific visualization concerned with algorithm design, implementation, and optimization for use on today’s largest computational platforms.

The book collects some of the most seminal work in the field, including algorithms and implementations running at the highest levels of concurrency and used by scientific researchers worldwide. After introducing the fundamental concepts of parallel visualization, the book explores approaches to accelerate visualization and analysis operations on high performance computing platforms. Looking to the future and anticipating changes to computational platforms in the transition from the petascale to exascale regime, it presents the main research challenges and describes several contemporary, high performance visualization implementations.

Reflecting major concepts in high performance visualization, this book unifies a large and diverse body of computer science research, development, and practical applications. It describes the state of the art at the intersection of scientific visualization, large data, and high performance computing trends, giving readers the foundation to apply the concepts and carry out future research in this area.

Table of Contents

Introduction, E. Wes Bethel
Historical Perspective
Moore's Law and the Data Tsunami
Focus of this Book
Book Organization and Themes
Conclusion

I Distributed Memory Parallel Concepts and Systems
Parallel Visualization Frameworks
, Hank Childs
Introduction
Background
Parallelization Strategy
Usage
Advanced Processing Techniques
Conclusion

Remote and Distributed Visualization Architectures, E. Wes Bethel and Mark Miller
Introduction
Visualization Performance Fundamentals and Networks
The Send-Images Partitioning
The Send-Data Partitioning
The Send-Geometry Partitioning
Hybrid and Adaptive Approaches
Which Pipeline Partitioning Works the Best?
Case Study: Visapult
Case Study: Chromium Renderserver
Case Study: VisIt and Dynamic Pipeline Reconfiguration
Conclusion

Rendering, Charles Hansen, E. Wes Bethel, Thiago Ize, and Carson Brownlee
Introduction
Rendering Taxonomy
Rendering Geometry
Volume Rendering
Real-Time Ray Tracer for Visualization on a Cluster
Conclusion

Parallel Image Compositing Methods, Tom Peterka and Kwan-Liu Ma
Introduction
Basic Concepts and Early Work in Compositing
Recent Advances
Results
Discussion and Conclusion

Parallel Integral Curves, David Pugmire, Tom Peterka, and Christoph Garth
Introduction
Challenges to Parallelization
Approaches to Parallelization
Conclusion

II Advanced Processing Techniques
Query-Driven Visualization and Analysis,
Oliver Rübel, E. Wes Bethel, Prabhat, and Kesheng Wu
Introduction
Data Subsetting and Performance
Formulating Multivariate Queries
Applications of Query-Driven Visualization
Conclusion

Progressive Data Access for Regular Grids, John Clyne
Introduction
Preliminaries
Z-Order Curves
Wavelets
Further Reading

In Situ Processing, Hank Childs, Kwan-Liu Ma and Hongfeng Yu, Brad Whitlock, Jeremy Meredith, and Jean Favre, Scott Klasky and Norbert Podhorszki, Karsten Schwan and Matthew Wolf, Manish Parashar, and Fan Zhang
Introduction
Tailored Co-Processing at High Concurrency
Co-Processing with General Visualization Tools via Adaptors
Concurrent Processing
Service Oriented Architecture for data management in HPC
In Situ Analytics using Hybrid Staging
Conclusion

Streaming and Out-of-Core Methods, David E. DeMarle, Berk Geveci, Jon Woodring, and Jim Ahrens
External Memory Algorithms
Taxonomy of Streamed Visualization
Streamed Visualization Concepts
Survey of Current State-of-the-Art
Conclusion

III Advanced Architectural Challenges and Solutions
GPU-Accelerated Visualization,
Marco Ament, Steffen Frey, Christoph Muller, Sebastian Grottel, Thomas Ertl, and Daniel Weiskopf
Introduction
Programmable Graphics Hardware
GPU-Accelerated Volume Rendering
Particle-Based Rendering
GPGPU High Performance Environments
Large Display Visualization

Hybrid Parallelism, E. Wes Bethel, David Camp, Hank Childs, Chistoph Garth, Mark Howison, Kenneth I. Joy, and Dave Pugmire
Introduction
Hybrid Parallelism and Volume Rendering
Hybrid Parallelism and Integral Curve Calculation
Conclusion and Future Work

Visualization at Extreme-Scale Concurrency, Hank Childs, David Pugmire, Sean Ahern, Brad Whitlock, Mark Howison, Prabhat, Gunther Weber, and E. Wes Bethel
Overview|Pure Parallelism
Massive Data Experiments
Scaling Experiments
Pitfalls at Scale
Conclusion

Performance Optimization and Autotuning, E. Wes Bethel and Mark Howison
Introduction
Optimizing Performance of a Three-Dimensional Stencil Operator on the GPU
Optimizing Raycasting Volume Rendering on Multi-Core GPUs and Many-Core GPUs
Conclusion

The Path to Exascale, Sean Ahern
Introduction
Future System Architectures
Science Understanding Needs at the Exascale
Research Directions
Conclusion and the Path Forward

IV High Performance Visualization Implementations
VisIt: An End-User Tool for Visualizing and Analyzing Very Large Data,
Hank Childs, Eric Brugger, Brad Whitlock, Jeremy Meredith, Sean Ahern, David Pugmire, Kathleen Bonnell, Mark Miller, Cyrus Harrison, Gunther HWeber, Hari Krishnan, Thomas Fogal, Allen Sanderson, Christoph Garth, EWes Bethel, David Camp, Oliver Rubel, Marc Durant, Jean MFavre, and Paul Navrátil
Introduction
Focal Points
Design
Successes
Future Challenges
Conclusion

IceT, Kenneth Moreland
Introduction
Motivation
Implementation
Application Programming Interface
Conclusion

The ParaView Visualization Application, Utkarsh Ayachit, Berk Geveci, Kenneth Moreland, John Patchett, and Jim Ahrens
Introduction
Understanding the Need
The ParaView Framework
Parallel Data Processing
The ParaView Application
Customizing with Plug-ins and Custom Applications
Co-processing: In Situ Visualization and Data Analysis
ParaViewWeb: Interactive Visualization for the Web
ParaView In Use
.Conclusion

The ViSUS Visualization Framework, Valerio Pascucci, Giorgio Scorzelli, Brian Summa, Peer-Timo Bremer, Attila Gyulassy, Cameron Christensen, Sujin Philip, and Sidharth Kumar
Introduction
ViSUS Software Architecture
Applications

The VAPOR Visualization Application, Alan Norton and John Clyne
Introduction
Progressive Data Access
Visualization-Guided Analysis
Progressive Access Examination
Discussion
Conclusion

The EnSight Visualization Application, Randall Frank and Michael F. Krogh
Introduction
EnSight Architectural Overview
Cluster Abstraction: CEIShell
Advanced Rendering
Conclusion
Acknowledgments
Index

Author Bio(s)

Editorial Reviews

E. Wes Bethel, Hank Childs, and Chuck Hansen have developed an eminently readable and comprehensive book. It provides the very first in-depth introduction to the interaction of two highly important and relevant topics in computational science: high performance computing and scientific visualization. The book provides a broad background on both topics, but more importantly, for the first time in book form, they describe some of the most recent developments in scientific visualization as we move from the Petascale era to Exaflops computing. … It will provide a solid foundation for anyone who considers using the most recent tools for visualization in order to understand complex simulation data or to understand the ever increasing amount of experimental data. I highly recommend this timely book for scientists and engineers.
—From the Foreword by Horst Simon, Lawrence Berkeley National Laboratory and University of California, Berkeley

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