Kairos: Efficient Temporal Graph Analytics on a Single Machine
CoRR(2024)
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.
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