A Hybrid Solution to Calculating Augmented Join Trees of 2D Scalar Fields in Parallel

Rosen, Paul; Tu, Junyi; Piegl, Les A.

doi:10.14733/cadconfp.2017.32-36

Cited by 7 publications

(4 citation statements)

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“…Acharya and Natarajan [49] specialized and improved monotone-path based computations for the special case of regular grids. Rosen et al also presented a hybrid CPU-GPU approach for regular grids [58]. In this work, we focus on triangulations because of the genericity of this representation: any mesh can be decomposed into a valid triangulation and regular grids can be implicitly triangulated with no memory overhead [59].…”

Section: A Related Workmentioning

confidence: 99%

Task-Based Augmented Contour Trees with Fibonacci Heaps

Gueunet

Fortin

Jomier³

et al. 2019

IEEE Trans. Parallel Distrib. Syst.

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This paper presents a new algorithm for the fast, shared memory, multi-core computation of augmented contour trees on triangulations. In contrast to most existing parallel algorithms our technique computes augmented trees, enabling the full extent of contour tree based applications including data segmentation. Our approach completely revisits the traditional, sequential contour tree algorithm to re-formulate all the steps of the computation as a set of independent local tasks. This includes a new computation procedure based on Fibonacci heaps for the join and split trees, two intermediate data structures used to compute the contour tree, whose constructions are efficiently carried out concurrently thanks to the dynamic scheduling of task parallelism. We also introduce a new parallel algorithm for the combination of these two trees into the output global contour tree. Overall, this results in superior time performance in practice, both in sequential and in parallel thanks to the OpenMP task runtime. We report performance numbers that compare our approach to reference sequential and multi-threaded implementations for the computation of augmented merge and contour trees. These experiments demonstrate the run-time efficiency of our approach and its scalability on common workstations. We demonstrate the utility of our approach in data segmentation applications.

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Section: A Related Workmentioning

confidence: 99%

Task-Based Augmented Contour Trees with Fibonacci Heaps

Gueunet

Fortin

Jomier³

et al. 2019

IEEE Trans. Parallel Distrib. Syst.

View full text Add to dashboard Cite

show abstract

“…Acharya and Natarajan [1] specialized and improved monotone-path based computations for the special case of regular grids. Rosen et al also presented a hybrid CPU-GPU approach for regular grids [42]. In this work, we focus on triangulations because of the genericity of this representation: any mesh can be decomposed into a valid triangulation and regular grids can be implicitly triangulated with no memory overhead [51].…”

Section: Related Workmentioning

confidence: 99%

Task-based augmented merge trees with Fibonacci heaps

Gueunet

Fortin

Jomier

et al. 2017

2017 IEEE 7th Symposium on Large Data Analysis and Visualization (LDAV)

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This paper presents a new algorithm for the fast, shared memory multi-core computation of augmented merge trees on triangulations. In contrast to most existing parallel algorithms, our technique computes augmented trees. This augmentation is required to enable the full extent of merge tree based applications, including data segmentation. Our approach completely revisits the traditional, sequential merge tree algorithm to re-formulate the computation as a set of independent local tasks based on Fibonacci heaps. This results in superior time performance in practice, in sequential as well as in parallel thanks to the OpenMP task runtime. In the context of augmented contour tree computation, we show that a direct usage of our merge tree procedure also results in superior time performance overall, both in sequential and parallel. We report performance numbers that compare our approach to reference sequential and multi-threaded implementations for the computation of augmented merge and contour trees. These experiments demonstrate the runtime efficiency of our approach as well as its scalability on common workstations. We demonstrate the utility of our approach in data segmentation applications. We also provide a lightweight VTK-based C++ implementation of our approach for reproduction purposes.

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“…The latest state-of-the-art regarding contour trees have been parallel or distributed implementations [9,14,25,33,38,39]. We use an approach described in [52], which is implemented under the piecewise linear setting.…”

Section: Technical Backgroundmentioning

confidence: 99%