Kairos：单机上的高效时序图分析
Kairos: Efficient Temporal Graph Analytics on a Single Machine
AI总结：
Kairos是一个单机时序图分析系统，利用fork-join并行性和选择性索引实现高效图处理，相比现有方法性能提升达60倍至数个数量级。
摘要：
许多重要的社会问题自然地被建模为时序图上的算法。然而，迄今为止，大多数图处理系统仍然效率低下，因为它们依赖分布式处理，即使图能很好地容纳在普通服务器的可用存储中。在本文中，我们介绍了Kairos，这是一个时序图分析系统，为应用程序开发者提供了一个在单机上高效实现和执行时序图算法的框架。具体而言，Kairos依赖fork-join并行性和高度优化的并行数据结构作为核心原语，以最大化时序图分析所需的图处理任务的性能。此外，我们引入了选择性索引的概念，并展示了如何将其与高效索引结合使用以加速时序查询。我们在24核服务器上的实验表明，我们的算法获得了良好的并行加速，并且比现有时序图处理系统中的等效算法快得多：相比共享内存方法快达60倍，与适应单机的图的分布式处理相比快几个数量级。
英文摘要：
Many important societal problems are naturally modeled as algorithms over temporal graphs. To date, however, most graph processing systems remain inefficient as they rely on distributed processing even for graphs that fit well within a commodity server's available storage. In this paper, we introduce Kairos, a temporal graph analytics system that provides application developers a framework for efficiently implementing and executing algorithms over temporal graphs on a single machine. Specifically, Kairos relies on fork-join parallelism and a highly optimized parallel data structure as core primitives to maximize performance of graph processing tasks needed for temporal graph analytics. Furthermore, we introduce the notion of selective indexing and show how it can be used with an efficient index to speedup temporal queries. Our experiments on a 24-core server show that our algorithms obtain good parallel speedups, and are significantly faster than equivalent algorithms in existing temporal graph processing systems: up to 60x against a shared-memory approach, and several orders of magnitude when compared with distributed processing of graphs that fit within a single server.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
