A Scalable Hybrid Scheme for Ray-Casting of Unstructured Volume Data

Roba Binyahib, Tom Peterka, Matthew Larsen, Kwan-Liu Ma, Hank Childs

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

We present an algorithm for parallel volume rendering that is a hybrid between classical object order and image order techniques. The algorithm operates on unstructured grids (and structured ones), and thus can deal with block boundaries interleaving in complex ways. It also deals effectively with cases that are prone to load imbalance, i.e., cases where cell sizes differ dramatically, either because of the nature of the input data, or because of the effects of the camera transformation. The algorithm divides work over resources such that each phase of its processing is bounded in the amount of computation it can perform. We demonstrate its efficacy through a series of studies, varying over camera position, data set size, transfer function, image size, and processor count. At its biggest, our experiments scaled up to 8,192 processors and operated on data sets with more than one billion cells. In total, we find that our hybrid algorithm performs well in all cases. This is because our algorithm naturally adapts its computation based on workload, and can operate like either an object order technique or an image order technique in scenarios where those techniques are efficient.

Original languageEnglish (US)
JournalIEEE Transactions on Visualization and Computer Graphics
DOIs
StateAccepted/In press - May 4 2018

Keywords

  • Cameras
  • Computer architecture
  • Data visualization
  • Equalizers
  • large scale visualization
  • Load management
  • Microprocessors
  • parallel visualization
  • Rendering (computer graphics)
  • Volume rendering

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design

Fingerprint Dive into the research topics of 'A Scalable Hybrid Scheme for Ray-Casting of Unstructured Volume Data'. Together they form a unique fingerprint.

  • Cite this