Efficient and Scalable Graph Pattern Mining on GPUs


Xuhao Chen and Arvind, MIT CSAIL


Graph Pattern Mining (GPM) extracts higher-order information in a large graph by searching for small patterns of interest. GPM applications are computationally expensive, and thus attractive for GPU acceleration. Unfortunately, due to the complexity of GPM algorithms and parallel hardware, hand optimizing GPM applications suffers programming complexity, while existing GPM frameworks sacrifice efficiency for programmability. Moreover, little work has been done on GPU to scale GPM computation to large problem sizes.

We describe G2Miner, the first graph pattern mining (GPM) framework that runs efficiently on multiple GPUs. G2Miner uses pattern-aware, input-aware and architecture-aware search strategies to achieve high efficiency on GPUs. To simplify programming, it provides a code generator that automatically generates pattern-aware CUDA code. G2Miner flexibly supports both breadth-first search (BFS) and depth-first search (DFS) to maximize memory utilization and generate sufficient parallelism for GPUs. For the scalability of G2Miner, we propose a customized scheduling policy to balance workload among multiple GPUs. Experiments on a V100 GPU show that G2Miner is 5.4× and 7.2× faster than the two state-of-the-art single-GPU systems, Pangolin and PBE, respectively. In the multi-GPU setting, G2Miner achieves linear speedups from 1 to 8 GPUs, for various patterns and data graphs. We also show that G2Miner on a V100 GPU is 48.3× and 15.2× faster than the state-of-the-art CPU-based systems, Peregrine and GraphZero, on a 56-core CPU machine.

OSDI '22 Open Access Sponsored by NetApp

Open Access Media

USENIX is committed to Open Access to the research presented at our events. Papers and proceedings are freely available to everyone once the event begins. Any video, audio, and/or slides that are posted after the event are also free and open to everyone. Support USENIX and our commitment to Open Access.

@inproceedings {280876,
author = {Xuhao Chen and Arvind},
title = {Efficient and Scalable Graph Pattern Mining on {GPUs}},
booktitle = {16th USENIX Symposium on Operating Systems Design and Implementation (OSDI 22)},
year = {2022},
isbn = {978-1-939133-28-1},
address = {Carlsbad, CA},
pages = {857--877},
url = {https://www.usenix.org/conference/osdi22/presentation/chen},
publisher = {USENIX Association},
month = jul

Presentation Video