PDG: A Prefetcher for Dynamic Graph Updating

Xinmiao Zhang, Cheng Liu, Jiacheng Ni, Yuanqing Cheng, Lei Zhang, Huawei Li, Xiaowei Li

Abstract: Dynamic graphs can be utilized to model many real-world applications like social media analysis in which the connections and entities evolve continuously. Hence, the processing of dynamic graphs is gaining increasing popularity. However, prior dynamic graph processing systems mainly focus on the optimization of graph analytics but overlook graph updating which manages the evolving graph structure and presents a unified view to graph analytics. Since graph updating operates on evolving graphs and involves a large number of irregular memory accesses, it poses a substantial influence on the performance of dynamic graph processing systems. In this work, we observe that graph updating is mainly bottlenecked by a frequent indirect memory access pattern ${^{*}(^{*}(B[A[i]]+{\mathrm{ offset}}))}$ . The pattern is inherent to the typical graph updating from the incoming edge stream to the base data store organized with either an adjacent list or a compressed sparse row. With this observation, we propose a novel prefetcher for dynamic graph updating abbreviated as PDG. PDG is a lightweight pipelined instruction-based prefetcher specialized for graph updating and it is also compatible with the irregular memory access pattern ${B[A[i]]}$ widely used in graph analytics. In addition, it leverages a monitor of the instruction queue to decide the appropriate timing of prefetching to make the best use of the cache. According to our experiments, PDG achieves $1.60\times $ , $1.26\times $ , and $1.30\times $ performance speedup compared to three representative prefetchers, respectively, with negligible hardware overhead in graph updating.