A hybrid solution to parallel calculation of augmented join trees of scalar fields in any dimension

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

doi:10.1080/16864360.2017.1419648

Cited by 8 publications

(3 citation statements)

References 8 publications

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“…While algorithms of this second family [17], [18], [19], [32], [33] have parts that expose fine-grained parallelism suitable for GPU or distributed settings, they introduce considerable extra work and communication, by tracing paths through the entire height of the tree. Most do not produce an augmentation and still contain globally sequential parts, harming scalability.…”

Section: Fine-grained Per-arc Constructionmentioning

confidence: 99%

Unordered Task-Parallel Augmented Merge Tree Construction

Werner

Garth

2021

IEEE Trans. Visual. Comput. Graphics

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Contemporary scientific data sets require fast and scalable topological analysis to enable visualization, simplification and interaction. Within this field, parallel merge tree construction has seen abundant recent contributions, with a trend of decentralized, task-parallel or SMP-oriented algorithms dominating in terms of total runtime. However, none of these recent approaches computed complete merge trees on distributed systems, leaving this field to traditional divide & conquer approaches. This paper introduces a scalable, parallel and distributed algorithm for merge tree construction outperforming the previously fastest distributed solution by a factor of around three. This is achieved by a task-parallel identification of individual merge tree arcs by growing regions around critical points in the data, without any need for ordered progression or global data structures, based on a novel insight introducing a sufficient local boundary for region growth.

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Section: Fine-grained Per-arc Constructionmentioning

confidence: 99%

Unordered Task-Parallel Augmented Merge Tree Construction

Werner

Garth

2021

IEEE Trans. Visual. Comput. Graphics

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“…We briefly describe the computation of the contour tree. For a detailed description and efficient algorithm, see [4,15].…”

Section: Computing the Contour Treementioning

confidence: 99%

Topologically-Guided Color Image Enhancement

Rosen

2019

Advances in Visual Computing

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Enhancement is an important step in post-processing digital images for personal use, in medical imaging, and for object recognition. Most existing manual techniques rely on region selection, similarity, and/or thresholding for editing, never really considering the topological structure of the image. In this paper, we leverage the contour tree to extract a hierarchical representation of the topology of an image. We propose 4 topology-aware transfer functions for editing features of the image using local topological properties, instead of global image properties. Finally, we evaluate our approach with grayscale and color images.

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“…In serial, a single sweep computes the join tree, a second one computes the split tree, then superarcs are transferred from the outside of the merge trees inwards to construct the contour tree [8]. Subsequent work parallelised this in shared memory [6], [11], [15], [16], in distributed clusters [18], [23], [24], [26] or on hybrid models [1], [20], [28]. The most performant shared-memory approach is the PPP algorithm [6], [11], which we use as the basis for our computations.…”

Section: Introductionmentioning

confidence: 99%

Data Parallel Hypersweeps for in Situ Topological Analysis

Hristov

Weber

Carr

et al. 2020

2020 IEEE 10th Symposium on Large Data Analysis and Visualization (LDAV)

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The contour tree is a tool for understanding the topological structure of a scalar field. Recent work has built efficient contour tree algorithms for shared memory parallel computation, driven by the need to analyze large data sets in situ while the simulation is running. Unfortunately, methods for using the contour tree for practical data analysis are still primarily serial, including single isocontour extraction, branch decomposition and simplification. We report data parallel methods for these tasks using a data structure called the hyperstructure and a general purpose approach called a hypersweep. We implement and integrate these methods with a Cinema database that stores features as depth images and with a web server that reconstructs the features for direct visualization.

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A hybrid solution to parallel calculation of augmented join trees of scalar fields in any dimension

Cited by 8 publications

References 8 publications

Unordered Task-Parallel Augmented Merge Tree Construction

Unordered Task-Parallel Augmented Merge Tree Construction

Topologically-Guided Color Image Enhancement

Data Parallel Hypersweeps for in Situ Topological Analysis

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