Scalable Parallel Feature Extraction and Tracking for Large Time-varying 3D Volume Data
Scalable Parallel Feature Extraction and Tracking for Large Time-varying 3D Volume Data
复制标题
针对大型时变 3D 体积数据的可扩展并行特征提取和跟踪
DOI：
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发表时间：
2013
期刊：
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通讯作者：
K. Ma
中科院分区：
文献类型：
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作者：
Yang Wang;Hongfeng Yu;K. Ma
大规模时变体数据集可能需要 TB 到 PB 的存储空间来存储和处理。一种有前景的方法是并行处理数据，然后仅提取和分析感兴趣的特征，从而将后续可视化任务所需的内存空间减少几个数量级。然而，并行提取体积特征是一项艰巨的任务，因为特征可能跨越多个处理器，并且局部部分特征仅在其自己的处理器内可见。在本文中，我们讨论如何生成和维护不同处理器之间的特征的连接信息。基于连通性信息，可以集成部分特征，这使得并行提取和跟踪大数据的特征成为可能。我们使用两个数据集（最多 16384 个处理器）展示了我们方法的有效性和可扩展性。
Large-scale time-varying volume data sets can take terabytes to petabytes of storage space to store and process. One promising approach is to process the data in parallel, and then extract and analyze only features of interest, reducing required memory space by several orders of magnitude for following visualization tasks. However, extracting volume features in parallel is a non-trivial task as features might span over multiple processors, and local partial features are only visible within their own processors. In this paper, we discuss how to generate and maintain connectivity information of features across different processors. Based on the connectivity information, partial features can be integrated, which makes it possible to extract and track features for large data in parallel. We demonstrate the effectiveness and scalability of our approach using two data sets with up to 16384 processors.