Pangolin: An Efficient and Flexible Graph Mining System on CPU and GPU

Published in Proceedings of the International Conference on Very Large Data Bases (PVLDB), 2020

Recommended citation: Xuhao Chen, Roshan Dathathri, Gurbinder Gill, Keshav Pingali, “Pangolin: An Efficient and Flexible Graph Mining System on CPU and GPU,” Proceedings of the 46th International Conference on Very Large Data Bases (PVLDB), 13(8), April 2020.

(Download publication here) (Download source code here)

(Download an earlier arxiv version of the paper here)


There is growing interest in graph pattern mining (GPM) problems such as motif counting. GPM systems have been developed to provide unified interfaces for programming algorithms for these problems and for running them on parallel systems. However, existing systems may take hours to mine even simple patterns in moderate-sized graphs, which significantly limits their real-world usability.

We present Pangolin, a high-performance and flexible in-memory GPM framework targeting shared-memory CPUs and GPUs. Pangolin is the first GPM system that provides high-level abstractions for GPU processing. It provides a simple programming interface based on the extend-reduce-filter model, which allows users to specify application-specific knowledge for search space pruning and isomorphism test elimination. We describe novel optimizations that exploit locality, reduce memory consumption, and mitigate the overheads of dynamic memory allocation and synchronization.

Evaluation on a 28-core CPU demonstrates that Pangolin outperforms existing GPM frameworks Arabesque, RStream, and Fractal by 49x, 88x, and 80x on average, respectively. Acceleration on a V100 GPU further improves performance of Pangolin by 15x on average. Compared to state-of-the-art hand-optimized GPM applications, Pangolin provides competitive performance with less programming effort.