Description
High Performance Visualization
Enabling Extreme-Scale Scientific Insight
Chapman & Hall/CRC Computational Science Series
Coordinators: Bethel E. Wes, Childs Hank, Hansen Charles
Language: English
Subjects for High Performance Visualization:
Keywords
Volume Rendering; High Performance Visualization; high performance computing and scientific visualization; Lawrence Berkeley National Laboratory; Exaflops computing; Visualization Algorithms; understand complex simulation data; Visualization Pipeline; Visualization and analysis tools; Ray Casting; scientific visualization and large computational platforms; Los Alamos National Laboratory; many-core computational platforms; NERSC; multi-core computational platforms; Data Blocks; high performance visualization implementations; Parallel Coordinate Plots; parallel visualization; Direct Volume Rendering; Simulation Codes; distributed memory parallel systems; Situ Processing; GPU-based visualization techniques; Wavelet Transform; hybrid parallelism; Visualization System; exascale class; Execution Time; algorithm design; Tunable Algorithmic Parameters; Tiled Displays; Parallel Rendering; Data Parallelization; Visualization Application; Bitmap Index; Adaptor Layer; MPI Task; Thread Block
Publication date: 11-2016
· 15.6x23.4 cm · Paperback
Publication date: 11-2012
446 p. · 15.6x23.4 cm · Hardback
Description
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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. I Distributed Memory Parallel Concepts and Systems: Parallel Visualization Frameworks. Remote and Distributed Visualization Architectures. Rendering. Parallel Image Compositing Methods. Parallel Integral Curves. II Advanced Processing Techniques: Query-Driven Visualization and Analysis. Progressive Data Access for Regular Grids: In Situ Processing. Streaming and Out-of-Core Methods. III Advanced Architectural Challenges and Solutions: GPU-Accelerated Visualization. Hybrid Parallelism. Visualization at Extreme Scale Concurrency. Performance Optimization and Auto-tuning. The Path to Exascale. IV High Performance Visualization Implementations: VisIt: An End-User Tool for Visualizing and Analyzing Very Large Data. IceT. The ParaView Visualization Application. The ViSUS Visualization Framework. The VAPOR Visualization Application. The EnSight Visualization Application. Index.
