Optimization and Augmentation for Data Parallel Contour Trees.
Optimization and Augmentation for Data Parallel Contour Trees.
复制标题
数据并行轮廓树的优化和增强。
DOI：
10.1109/tvcg.2021.3064385
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发表时间：
2022
影响因子：
5.2
通讯作者：
Carr HA
中科院分区：
文献类型：
--
作者：
Carr HA
在科学可视化中，等高线树用于拓扑数据分析。虽然最初是使用串行算法进行计算，但最近的工作引入了向量并行算法。然而，对于许多实际数据分析任务所需的完全扩展轮廓树，该算法相对较慢。因此，我们引入了一种称为超结构的表示法，它能够有效地搜索轮廓树，并使用它来构造数据并行的完全扩充轮廓树，其性能平均比TTK拓扑工具包中最先进的并行算法快6倍。
Contour trees are used for topological data analysis in scientific visualization. While originally computed with serial algorithms, recent work has introduced a vector-parallel algorithm. However, this algorithm is relatively slow for fully augmented contour trees which are needed for many practical data analysis tasks. We therefore introduce a representation called the hyperstructure that enables efficient searches through the contour tree and use it to construct a fully augmented contour tree in data parallel, with performance on average 6 times faster than the state-of-the-art parallel algorithm in the TTK topological toolkit.