The analytical engine in GraphScope originated from GRAPE, a system that implemented the fix-point model proposed in the paper Parallelizing Sequential Graph Computations.
GRAPE differs from prior systems in its ability to parallelize sequential graph algorithms as a whole by following the PIE programming model from the paper. Sequential algorithms can be easily "plugged into" GRAPE with only minor changes and get parallelized to handle large graphs efficiently. In addition to the ease of programming, GRAPE is designed to be highly efficient and flexible, to cope the scale, variety and complexity from real-life graph applications.
A lightweight version of GRAPE is open-sourced as libgrape-lite. The analytical engine extends libgrape-lite with features for mutable fragments, vineyard support, and the service mode for the engine, etc.
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Wenfei Fan, Jingbo Xu, Wenyuan Yu, Jingren Zhou, Xiaojian Luo, Ping Lu, Qiang Yin, Yang Cao, and Ruiqi Xu. Parallelizing Sequential Graph Computations. ACM Transactions on Database Systems (TODS) 43(4): 18:1-18:39.
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Wenfei Fan, Jingbo Xu, Yinghui Wu, Wenyuan Yu, Jiaxin Jiang. GRAPE: Parallelizing Sequential Graph Computations. The 43rd International Conference on Very Large Data Bases (VLDB), demo, 2017 (the Best Demo Award).
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Wenfei Fan, Jingbo Xu, Yinghui Wu, Wenyuan Yu, Jiaxin Jiang, Zeyu Zheng, Bohan Zhang, Yang Cao, and Chao Tian. Parallelizing Sequential Graph Computations. ACM SIG Conference on Management of Data (SIGMOD), 2017 (the Best Paper Award).