HUGE: An Efficient and Scalable Subgraph Enumeration System
Zhengyi Yang, Longbin Lai, Xuemin Lin, Kongzhang Hao, Wenjie Zhang
ACM SIGMOD International Conference on Management of Data (SIGMOD)
RAIDS Lab Authors
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Abstract
Subgraph enumeration is a fundamental problem in graph analytics, which aims to find all instances of a given query graph on a large data graph. In this paper, we propose a system called HUGE to efficiently process subgraph enumeration at scale in the distributed context. HUGE features 1) an optimiser to compute an advanced execution plan without the constraints of existing works; 2) a hybrid communication layer that supports both pushing and pulling communication; 3) a novel two-stage execution mode with a lock-free and zero-copy cache design; 4) a BFS/DFS-adaptive scheduler to bound memory consumption; and 5) two-layer intra- and inter-machine load balancing. HUGE is generic such that all existing distributed subgraph enumeration algorithms can be plugged in to enjoy automatic speed up and bounded-memory execution.
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BibTeX
@inproceedings{yang2021huge,
title = {HUGE: An Efficient and Scalable Subgraph Enumeration System},
author = {Yang, Zhengyi and Lai, Longbin and Lin, Xuemin and Hao, Kongzhang and Zhang, Wenjie},
series = {SIGMOD/PODS '21},
url = {http://dx.doi.org/10.1145/3448016.3457237},
doi = {10.1145/3448016.3457237},
booktitle = {Proceedings of the 2021 International Conference on Management of Data},
publisher = {ACM},
year = {2021},
month = June,
pages = {2049-2062},
collection = {SIGMOD/PODS '21}
}