Out-of-core visualization of time-varying hybrid-grid volume data

Min Shih, Yubo Zhang, Kwan-Liu Ma, Jayanarayanan Sitaraman, Dimitri Mavriplis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


Traditional computational fluid dynamics (CFD) solvers are usually written for a single gridding paradigm such as structured-Cartesian, structured-body-fitted, or unstructured grids. Each type of mesh paradigms has inherent advantages and disadvantages. Thus, the methods of coupling multiple mesh paradigms have been developed to facilitate the use of different solvers in different part of the computational domain. However, the complex hybrid gridding paradigm poses challenges to rendering calculations for visualizing the data. This paper describes a volume visualization system for time-varying adaptive moving-body CFD datasets, where the grid system consists of unstructured grids near the body surface, coupled with Structured Adaptive Mesh Refinement (SAMR) grid in the off-body domain. We present two approaches to the hybrid-grid volume ray casting: a KD-tree based single-pass algorithm, and a multi-pass algorithm using the depth peeling technique. The system has a three-level memory hierarchy: GPU memory, main memory, and a solid state drive (SSD). Through data caching and prefetching within the memory hierarchy, the latency of time-step swapping can be hidden. Experimental results show that our system allows interactive volume exploration on single-GPU commodity PCs.

Original languageEnglish (US)
Title of host publicationIEEE Symposium on Large Data Analysis and Visualization 2014, LDAV 2014 - Proceedings
EditorsHank Childs, Hank Childs, Renato Pajarola, Venkatram Vishwanath
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)9781479952151
StatePublished - Jan 16 2014
Event4th IEEE Symposium on Large Data Analysis and Visualization, LDAV 2014 - Paris, France
Duration: Oct 9 2014Oct 10 2014


Other4th IEEE Symposium on Large Data Analysis and Visualization, LDAV 2014

ASJC Scopus subject areas

  • Information Systems
  • Computer Vision and Pattern Recognition


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