Graph Pattern Mining (GPM) is a class of algorithms that identifies given shapes within a graph, e.g., cliques of a certain size. Any area of a graph can contain a shape of interest, but in real-world graphs, these shapes tend to be concentrated in areas deemed skewed. Because mining skewed areas can dominate GPM computations, the overwhelming majority of state-of-the-art GPM techniques break such areas into many small parts and load balance them across servers. This paper takes a diametrically opposite approach: we suggest a framework that concentrates rather than divides the skewed areas.Our framework, called GraphINC, relies on two key innovations. First, it introduces a new graph partitioning scheme capable of separating the skewed area from the rest of the graph. Second, it offloads the skewed part onto a new class of hardware accelerator, a programmable network switch. We implemented our framework to leverage a commercial 100 Gbps switch and obtained results 6.5 to 52.4× faster thanks to our novel offloading technique.
CITATION STYLE
Hussein, R., Lerner, A., Ryser, A., Bürgi, L. D., Blarer, A., & Cudre-Mauroux, P. (2023). GraphINC: Graph Pattern Mining at Network Speed. Proceedings of the ACM on Management of Data, 1(2), 1–28. https://doi.org/10.1145/3589329
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