2015
Carr, Hamish; Sewell, Christopher; Lo, Li-Ta; james Ahrens,
Hybrid Data-Parallel Contour Tree Computation Proceedings Article
In: 2015, (LA-UR-15-24759).
Abstract | Links | BibTeX | Tags: and object reppresentations, computational geometry and object modeling, contour tree, data-parallel, gpu, multi-core, nvidia thrust, simulation output analysis, solid, surface, topological analysis
@inproceedings{Carr2015,
title = {Hybrid Data-Parallel Contour Tree Computation},
author = {Hamish Carr and Christopher Sewell and Li-Ta Lo and james Ahrens},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/HybridData-ParallelContourTreeComputaion.pdf},
year = {2015},
date = {2015-01-01},
number = {LA-UR-15-24759},
institution = {Los Alamos National Laboratory},
abstract = {As data sets increase in size beyond the petabyte, it is increasingly important to have automated methods for data analysis and visualization. While topological analysis tools such as the contour tree and Morse-Smale complex are now well established, there is still a shortage of efficient parallel algorithms for their computation, in particular for massively data-parallel computation on a SIMD model. We report the first data-parallel algorithm for computing the fully augmented contour tree, using a quantized computation model. We then extend this to provide a hybrid data-parallel / distributed algorithm allowing scaling beyond a single GPU or CPU, and provide results for its computation. Our implementation uses the portable data-parallel primitives provided by Nvidia’s Thrust library, allowing us to compile our same code for both GPUs and multi-core CPUs.},
note = {LA-UR-15-24759},
keywords = {and object reppresentations, computational geometry and object modeling, contour tree, data-parallel, gpu, multi-core, nvidia thrust, simulation output analysis, solid, surface, topological analysis},
pubstate = {published},
tppubtype = {inproceedings}
}
As data sets increase in size beyond the petabyte, it is increasingly important to have automated methods for data analysis and visualization. While topological analysis tools such as the contour tree and Morse-Smale complex are now well established, there is still a shortage of efficient parallel algorithms for their computation, in particular for massively data-parallel computation on a SIMD model. We report the first data-parallel algorithm for computing the fully augmented contour tree, using a quantized computation model. We then extend this to provide a hybrid data-parallel / distributed algorithm allowing scaling beyond a single GPU or CPU, and provide results for its computation. Our implementation uses the portable data-parallel primitives provided by Nvidia’s Thrust library, allowing us to compile our same code for both GPUs and multi-core CPUs.
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1.
Carr, Hamish; Sewell, Christopher; Lo, Li-Ta; james Ahrens,
Hybrid Data-Parallel Contour Tree Computation Proceedings Article
In: 2015, (LA-UR-15-24759).
@inproceedings{Carr2015,
title = {Hybrid Data-Parallel Contour Tree Computation},
author = {Hamish Carr and Christopher Sewell and Li-Ta Lo and james Ahrens},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/HybridData-ParallelContourTreeComputaion.pdf},
year = {2015},
date = {2015-01-01},
number = {LA-UR-15-24759},
institution = {Los Alamos National Laboratory},
abstract = {As data sets increase in size beyond the petabyte, it is increasingly important to have automated methods for data analysis and visualization. While topological analysis tools such as the contour tree and Morse-Smale complex are now well established, there is still a shortage of efficient parallel algorithms for their computation, in particular for massively data-parallel computation on a SIMD model. We report the first data-parallel algorithm for computing the fully augmented contour tree, using a quantized computation model. We then extend this to provide a hybrid data-parallel / distributed algorithm allowing scaling beyond a single GPU or CPU, and provide results for its computation. Our implementation uses the portable data-parallel primitives provided by Nvidia’s Thrust library, allowing us to compile our same code for both GPUs and multi-core CPUs.},
note = {LA-UR-15-24759},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
As data sets increase in size beyond the petabyte, it is increasingly important to have automated methods for data analysis and visualization. While topological analysis tools such as the contour tree and Morse-Smale complex are now well established, there is still a shortage of efficient parallel algorithms for their computation, in particular for massively data-parallel computation on a SIMD model. We report the first data-parallel algorithm for computing the fully augmented contour tree, using a quantized computation model. We then extend this to provide a hybrid data-parallel / distributed algorithm allowing scaling beyond a single GPU or CPU, and provide results for its computation. Our implementation uses the portable data-parallel primitives provided by Nvidia’s Thrust library, allowing us to compile our same code for both GPUs and multi-core CPUs.