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Time Dependent Processing in a Parallel Pipeline Architecture
November/December 2007 (vol. 13 no. 6)
pp. 1376-1383
ASCII Text
x 
 
John Biddiscombe, Berk Geveci, Ken Martin, Kenneth Moreland, David Thompson, "Time Dependent Processing in a Parallel Pipeline Architecture," IEEE Transactions on Visualization and Computer Graphics, vol. 13, no. 6, pp. 1376-1383, November/December, 2007.
 
 
BibTex
x
 
@article{ 10.1109/TVCG.2007.70600,
author = {John Biddiscombe and Berk Geveci and Ken Martin and Kenneth Moreland and David Thompson},
title = {Time Dependent Processing in a Parallel Pipeline Architecture},
journal ={IEEE Transactions on Visualization and Computer Graphics},
volume = {13},
number = {6},
issn = {1077-2626},
year = {2007},
pages = {1376-1383},
doi = {http://doi.ieeecomputersociety.org/10.1109/TVCG.2007.70600},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Visualization and Computer Graphics
TI - Time Dependent Processing in a Parallel Pipeline Architecture
IS - 6
SN - 1077-2626
SP1376
EP1383
EPD - 1376-1383
A1 - John Biddiscombe,
A1 - Berk Geveci,
A1 - Ken Martin,
A1 - Kenneth Moreland,
A1 - David Thompson,
PY - 2007
KW - data-parallel visualization pipeline
KW - time-varying data
VL - 13
JA - IEEE Transactions on Visualization and Computer Graphics
ER -
 
Pipeline architectures provide a versatile and efficient mechanism for constructing visualizations, and they have been implemented in numerous libraries and applications over the past two decades. In addition to allowing developers and users to freely combine algorithms, visualization pipelines have proven to work well when streaming data and scale well on parallel distributed-memory computers. However, current pipeline visualization frameworks have a critical flaw: they are unable to manage time varying data. As data flows through the pipeline, each algorithm has access to only a single snapshot in time of the data. This prevents the implementation of algorithms that do any temporal processing such as particle tracing; plotting over time; or interpolation, fitting, or smoothing of time series data. As data acquisition technology improves, as simulation time-integration techniques become more complex, and as simulations save less frequently and regularly, the ability to analyze the time-behavior of data becomes more important. This paper describes a modification to the traditional pipeline architecture that allows it to accommodate temporal algorithms. Furthermore, the architecture allows temporal algorithms to be used in conjunction with algorithms expecting a single time snapshot, thus simplifying software design and allowing adoption into existing pipeline frameworks. Our architecture also continues to work well in parallel distributed-memory environments. We demonstrate our architecture by modifying the popular VTK framework and exposing the functionality to the ParaView application. We use this framework to apply time-dependent algorithms on large data with a parallel cluster computer and thereby exercise a functionality that previously did not exist.

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Index Terms:
data-parallel visualization pipeline, time-varying data
Citation:
John Biddiscombe, Berk Geveci, Ken Martin, Kenneth Moreland, David Thompson, "Time Dependent Processing in a Parallel Pipeline Architecture," IEEE Transactions on Visualization and Computer Graphics, vol. 13, no. 6, pp. 1376-1383, Nov.-Dec. 2007, doi:10.1109/TVCG.2007.70600
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