论文标题
异步和负载平衡的联合获取，用于分布式和平行的科学数据可视化和分析
Asynchronous and Load-Balanced Union-Find for Distributed and Parallel Scientific Data Visualization and Analysis
论文作者
论文摘要
我们提出了一种新颖的分布式联合获取算法，该算法具有异步并行性和基于K树的负载平衡，以进行可伸缩的可视化和科学数据的分析。 Union-Find的应用包括级别设置提取和关键点跟踪，但是分布式的联合信息可能会遭受高同步成本和在平行过程中的不平衡工作负载。在这项研究中，我们证明，可以消除现有分布式联合信息中的全球同步而不改变最终结果，从而允许重叠的通信和计算可扩展处理。我们还使用K-D树分解来重新分布输入，以改善工作量平衡。我们使用合成数据和应用程序数据基准使用多达1,024个过程的算法的可伸缩性。我们通过高速成像实验和融合等离子体模拟在临界点跟踪和超级水平设置提取中使用算法的使用。
We present a novel distributed union-find algorithm that features asynchronous parallelism and k-d tree based load balancing for scalable visualization and analysis of scientific data. Applications of union-find include level set extraction and critical point tracking, but distributed union-find can suffer from high synchronization costs and imbalanced workloads across parallel processes. In this study, we prove that global synchronizations in existing distributed union-find can be eliminated without changing final results, allowing overlapped communications and computations for scalable processing. We also use a k-d tree decomposition to redistribute inputs, in order to improve workload balancing. We benchmark the scalability of our algorithm with up to 1,024 processes using both synthetic and application data. We demonstrate the use of our algorithm in critical point tracking and super-level set extraction with high-speed imaging experiments and fusion plasma simulations, respectively.
