一种以划分为中心的大型图中欧拉回路的分布式识别算法
A Partition-centric Distributed Algorithm for Identifying Euler Circuits in Large Graphs
摘要：
在图中寻找欧拉回路是一个经典的问题，但在并行计算中还没有得到充分的探索。随着这种循环在神经科学和物联网中用于大型图，设计一种分布式算法来找到欧拉电路是很重要的。现有的并行算法是不切实际的商品集群和云。我们提出了一种新的分区为中心的算法来找到欧拉电路，在大型图形划分跨分布式机器和迭代执行使用批量同步并行（BSP）模型。该算法在每个分区中找到部分路径和循环，并通过递归合并分区将其细化为更长的路径。我们描述了该算法，分析了其复杂性，在Apache Spark上对大型图进行了验证，并提供了实验结果。我们还确定了算法中的内存瓶颈，并提出了一个增强的设计来解决它。
英文摘要：
Finding the Eulerian circuit in graphs is a classic problem, but inadequately explored for parallel computation. With such cycles finding use in neuroscience and Internet of Things for large graphs, designing a distributed algorithm for finding the Euler circuit is important. Existing parallel algorithms are impractical for commodity clusters and Clouds. We propose a novel partition-centric algorithm to find the Euler circuit, over large graphs partitioned across distributed machines and executed iteratively using a Bulk Synchronous Parallel (BSP) model. The algorithm finds partial paths and cycles within each partition, and refines these into longer paths by recursively merging the partitions. We describe the algorithm, analyze its complexity, validate it on Apache Spark for large graphs, and offer experimental results. We also identify memory bottlenecks in the algorithm and propose an enhanced design to address it.
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