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 article 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.

中文翻译：

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无序任务并行增强合并树构建


当代科学数据集需要快速且可扩展的拓扑分析，以实现可视化、简化和交互。在该领域中，并行合并树构建最近出现了丰富的贡献，在总运行时间方面，分散式、任务并行或面向 SMP 的算法占据主导地位。然而，这些最近的方法都没有在分布式系统上计算完整的合并树，从而将该领域留给了传统的分而治之的方法。本文介绍了一种用于合并树构建的可扩展、并行和分布式算法，其性能比以前最快的分布式解决方案高出大约三倍。这是通过在数据中的关键点周围生长区域来对各个合并树弧进行任务并行识别来实现的，无需有序级数或全局数据结构，基于为区域生长引入足够的局部边界的新颖见解。