compressgraph's Introduction

CompressGraph

Description

This is the open-source implementation for CompressGraph:

CompressGraph: Efficient Parallel Graph Analytics with Rule-Based Compression”, Zheng Chen, Feng Zhang, Jiawei Guan, Jidong Zhai, Xipeng Shen, Huanchen Zhan, Wentong Shu, Xiaoyong Du. SIGMOD/PODS '23: Proceedings of the 2023 International Conference on Management of Data. https://dl.acm.org/doi/10.1145/3588684

System Dependency

CMake
Optional(CPU): Ligra
Optional(GPU): Gunrock

Compilation

git clone https://github.com/ZhengChenCS/CompressGraph.git --recursive
cd CompressGraph
mkdir -p build
cd build
cmake ..
make -j

Graph Compression

The Compression Mudule accepts binary CSR(Compressed Sparse Row) graph data as input. The dataset folder provides an example.

To compress a input graph, run:

compress <csr_vlist.bin> <csr_elist.bin>

To filter the rule by the threshold(16 by default), run:

filter <csr_vlist.bin> <csr_elist.bin> <info.bin> 16

The script folder provides the example of invoke compression and filtering.

Graph analytics on CPU

CompressGraph implements multi-core parallel graph analytics on CPU based on Ligra.

Graph analytics on GPU

CompressGraph implements multi-core parallel graph analytics on CPU based on Gunrock.

Citation

If you use our code, please cite our paper:

@article{chen2023compressgraph,
  title={CompressGraph: Efficient Parallel Graph Analytics with Rule-Based Compression},
  author={Chen, Zheng and Zhang, Feng and Guan, JiaWei and Zhai, Jidong and Shen, Xipeng and Zhang, Huanchen and Shu, Wentong and Du, Xiaoyong},
  journal={Proceedings of the ACM on Management of Data},
  volume={1},
  number={1},
  pages={1--31},
  year={2023},
  publisher={ACM New York, NY, USA}
}

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