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.

中文翻译：

---

用于分布式并行科学数据可视化和分析的异步和负载平衡联合查找


我们提出了一种新颖的分布式联合查找算法，该算法具有异步并行性和基于 kd 树的负载平衡，用于可扩展的可视化和科学数据分析。 union-find 的应用包括水平集提取和关键点跟踪，但分布式 union-find 可能会遭受高同步成本和跨并行进程的不平衡工作负载的困扰。在这项研究中，我们证明可以在不改变最终结果的情况下消除现有分布式联合查找中的全局同步，从而允许重叠通信和计算以进行可扩展处理。我们还使用 kd 树分解来重新分配输入，以改善工作负载平衡。我们使用合成数据和应用程序数据，通过多达 1,024 个进程对算法的可扩展性进行基准测试。我们分别通过高速成像实验和聚变等离子体模拟演示了我们的算法在临界点跟踪和超水平集提取中的使用。