19206

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

Xuhao Chen, Roshan Dathathri, Gurbinder Gill, Keshav Pingali
The University of Texas at Austin
arXiv:1911.06969 [cs.DC], (16 Nov 2019)

@misc{chen2019pangolin,

   title={Pangolin: An Efficient and Flexible Graph Mining System on CPU and GPU},

   author={Xuhao Chen and Roshan Dathathri and Gurbinder Gill and Keshav Pingali},

   year={2019},

   eprint={1911.06969},

   archivePrefix={arXiv},

   primaryClass={cs.DC}

}

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There is growing interest in graph mining algorithms such as motif counting. Generic graph mining systems have been developed to provide unified interfaces for programming these algorithms. However, existing systems take minutes or even 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 graph mining framework targeting both shared-memory CPUs and GPUs. Pangolin is the first graph mining system that supports GPU processing. We provide a simple embedding-centric programming interface based on the extend-reduce-filter model, which enables user to specify application-specific knowledge like aggressive enumeration search space pruning and isomorphism test elimination. We also describe novel optimizations that exploit locality, reduce memory consumption, and mitigate overheads of dynamic memory allocation and synchronization. Evaluation on a 28-core CPU demonstrates that Pangolin outperforms Arabesque and RStream, two state-of-the-art graph mining frameworks, by 49x and 88x 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 mining applications, Pangolin provides competitive performance with much less programming effort.
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