High Performance Visualization Enabling Extreme-Scale Scientific Insight

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.

Introduction, E. Wes BethelHistorical Perspective Moore's Law and the Data Tsunami Focus of this Book Book Organization and Themes ConclusionI Distributed Memory Parallel Concepts and SystemsParallel Visualization Frameworks, Hank ChildsIntroduction Background Parallelization Strategy Usage Advanced Processing Techniques Conclusion Remote and Distributed Visualization Architectures, E. Wes Bethel and Mark MillerIntroduction 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 BrownleeIntroduction 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 MaIntroduction Basic Concepts and Early Work in Compositing Recent Advances Results Discussion and Conclusion Parallel Integral Curves, David Pugmire, Tom Peterka, and Christoph GarthIntroduction Challenges to Parallelization Approaches to Parallelization Conclusion II Advanced Processing TechniquesQuery-Driven Visualization and Analysis, Oliver Rübel, E. Wes Bethel, Prabhat, and Kesheng WuIntroduction Data Subsetting and Performance Formulating Multivariate Queries Applications of Query-Driven Visualization Conclusion Progressive Data Access for Regular Grids, John ClyneIntroduction 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 ZhangIntroduction 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 AhrensExternal Memory Algorithms Taxonomy of Streamed Visualization Streamed Visualization Concepts Survey of Current State-of-the-Art Conclusion III Advanced Architectural Challenges and SolutionsGPU-Accelerated Visualization, Marco Ament, Steffen Frey, Christoph Muller, Sebastian Grottel, Thomas Ertl, and Daniel WeiskopfIntroduction 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 PugmireIntroduction 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 BethelOverview|Pure Parallelism Massive Data Experiments Scaling Experiments Pitfalls at Scale Conclusion Performance Optimization and Autotuning, E. Wes Bethel and Mark HowisonIntroduction 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 AhernIntroduction Future System Architectures Science Understanding Needs at the Exascale Research Directions Conclusion and the Path Forward IV High Performance Visualization ImplementationsVisIt: 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 NavrátilIntroduction Focal Points Design Successes Future Challenges Conclusion IceT, Kenneth MorelandIntroduction Motivation Implementation Application Programming Interface Conclusion The ParaView Visualization Application, Utkarsh Ayachit, Berk Geveci, Kenneth Moreland,John Patchett, and Jim AhrensIntroduction 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 KumarIntroduction ViSUS Software Architecture Applications The VAPOR Visualization Application, Alan Norton and John ClyneIntroduction Progressive Data Access Visualization-Guided Analysis Progressive Access Examination Discussion Conclusion The EnSight Visualization Application, Randall Frank and Michael F. KroghIntroduction EnSight Architectural Overview Cluster Abstraction: CEIShell Advanced Rendering Conclusion AcknowledgmentsIndex

E. Wes Bethel, Hank Childs, Charles Hansen