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Title: Parallel Unstructured Volume Rendering in ParaView (Presentation).


Abstract not provided.

Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
Report Number(s):
DOE Contract Number:
Resource Type:
Resource Relation:
Conference: Proposed for presentation at the IS&T/SPIE 19th Annual Symposium Electronic Imaging Science and Technology held January 28 - February 1, 2007 in San Jose, CA.
Country of Publication:
United States

Citation Formats

Moreland, Kenneth D. Parallel Unstructured Volume Rendering in ParaView (Presentation).. United States: N. p., 2007. Web.
Moreland, Kenneth D. Parallel Unstructured Volume Rendering in ParaView (Presentation).. United States.
Moreland, Kenneth D. Mon . "Parallel Unstructured Volume Rendering in ParaView (Presentation).". United States. doi:.
title = {Parallel Unstructured Volume Rendering in ParaView (Presentation).},
author = {Moreland, Kenneth D.},
abstractNote = {Abstract not provided.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Mon Jan 01 00:00:00 EST 2007},
month = {Mon Jan 01 00:00:00 EST 2007}

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  • No abstract prepared.
  • We discuss two volume rendering methods developed at Lawrence Livermore National Laboratory. The first, cell projection, renders the polygons in the projection of each cell. It requires a global visibility sort in order to composite the cells in back to front order, and we discuss several different algorithms for this sort. The second method uses regularly spaced slice planes perpendicular to the X, Y, or Z axes, which slice the cells into polygons. Both methods are supplemented with anti-aliasing techniques to deal with small cells that might fall between pixel samples or slice planes, and both have been parallelized.
  • Parallel ray casting volume rendering is implemented and tested on an IBM Blue Gene distributed memory parallel architecture. Data are presented from experiments under a number of different conditions, including dataset size, number of processors, low and high quality rendering, offline storage of results, and streaming of images for remote display. Performance is divided into three main sections of the algorithm: disk I/O, rendering, and compositing. The dynamic balance between these tasks varies with the number of processors and other conditions. Lessons learned from the work include understanding the balance between parallel I/O, computation, and communication within the context ofmore » visualization on supercomputers, recommendations for tuning and optimization, and opportunities for scaling further in the future. Extrapolating these results to very large data and image sizes suggests that a distributed memory HPC architecture such as the Blue Gene is a viable platform for some types of visualization at very large scales.« less
  • This paper presents a divide-and-conquer ray-traced volume rendering algorithm and its implementation on networked workstations and a massively parallel computer, the Connection Machine CM-5. This algorithm distributes the data and the computational load to individual processing units to achieve fast, high-quality rendering of high-resolution data, even when only a modest amount of memory is available on each machine. The volume data, once distributed, is left intact. The processing nodes perform local ray-tracing of their subvolume concurrently. No communication between processing units is needed during this locally ray-tracing process. A subimage is generated by each processing unit and the final imagemore » is obtained by compositing subimages in the proper order, which can be determined a priori. Implementations and tests on a group of networked workstations and on the Thinking Machines CM-5 demonstrate the practicality of our algorithm and expose different performance tuning issues for each platform. We use data sets from medical imaging and computational fluid dynamics simulations in the study of this algorithm.« less
  • Many diverse areas of industry benefit from the use of volume of fluid methods to predict the movement of materials. Casting is a common method of part fabrication. The accurate prediction of the casting process is pivotal to industry. Mold design and casting is currently considered an art by industry. It typically involves many trial mold designs, and the rejection of defective parts is costly. Failure of cast parts, because residual stresses reduce the part`s strength, can be catastrophic. Cast parts should have precise geometric details that reduce or eliminate the need for machining after casting. Volume of fluid codesmore » will help designers predict how the molten metal fills a mold and where ay trapped voids remain. Prediction of defects due to thermal contraction or expansion will eliminate defective, trial mold designs and speed the parts to market with fewer rejections. Increasing the predictability and therefore the accuracy of the casting process will reduce the art that is involved in mold design and parts casting. Here, recent enhancements to multidimensional volume-tracking algorithms are presented. Illustrations in two dimensions are given. The improvements include new, local algorithms for interface normal constructions and a new full remapping algorithm for time integration. These methods are used on structured and unstructured grids.« less