GraphAIBench

GraphAIBench is a C++ implemented Benchmark Suite for Graph AI. It includes the following benchmarks:

• Graph Neural Networks (GNN): GCN, GraphSAGE, GAT.
• Centrality: Betweenness Centrality (BC).
• Community: Community detection using Louvain algorithm.
• Components: Connected Components (CC), Srtongly Connected Components (SCC).
• Corness: k-core decomposition.
• Flitering: Minimum Spanning Tree (MST), Triangulated Maximally Filtered Graph (TMFG), Planar Maximally Filtered Graph (PMFG).
• Linear Assignment: Hungarian algorithm.
• Link Analysis: PageRank (PR).
• Link Prediction: Node2vec.
• Mining [1]: triangle counting, clique finding, motif counting, frequent subgraph mining (FSM).
• Sampling: Random walk.
• Structure: Graph coarsening.
• Traversal: Breadth-First Search (BFS) and Single-Source Shortest Paths (SSSP).

GraphAIBench is parallelized using OpenMP and CUDA, same as DGL, but runs much faster than DGL. Please see [2] for evaluation details.

Therefore, compared to DGL and PyG, GraphAIBench is better suited for evaluating specialized hardware design or low-level library (e.g. SpMM) implementations for GNNs.

Datasets for pattern mining (e.g. triangle counting) are available here. Datasets for collaborative filtering (i.e. bipartite graphs) are available here. Datasets for vertex programs (e.g. BFS, SSSP, CC, BC, PageRank) are available here (directed) and here (undirected). Datasets for graph neural networks (GNNs), e.g. GCN, GraphSAGE, GAT, are available here. Please contact the author for more datasets.

[1] Xuhao Chen, Arvind. Efficient and Scalable Graph Pattern Mining on GPUs, OSDI 2022

[2] Loc Hoang, Xuhao Chen, Hochan Lee, Roshan Dathathri, Gurbinder Gill, Keshav Pingali, Efficient Distribution for Deep Learning on Large Graphs, Workshop on Graph Neural Networks and Systems (GNNSys), 2021

Getting Started

The document is organized as follows:

• Requirements
• Quick start
• Supported graph formats
• Code Documentation
• Reporting bugs and contributing
• Notes
• Publications
• Developers
• License

Requirements

• CUDA toolkit 11.1.1 or greater.
• GCC 8.3.1 or greater.

Quick Start

Edit env.sh to let the libraries pointing to the right paths in your system, and then:

$ source env.sh

Then just make in the root directory:

$ make

Or go to each sub-directory, e.g. src/triangle, and then make:

$ cd src/triangle; make

Binaries will be in the bin directory. For example, tc_omp_base is the OpenMP version of triangle counting on CPU, tc_gpu_base is the single GPU version, and tc_multigpu is the multi-GPU version.

Find out commandline format by running executable without argument:

$ cd ../../bin
$ ./tc_omp_base

Datasets are available here. Run triangle counting with an undirected toy graph on CPU:

$ ./tc_omp_base ../inputs/citeseer/graph

You can find the expected outputs in the README of each benchmark see here for triangle.

To control the number of threads, set the following environment variable:

$ export OMP_NUM_THREADS=[ number of cores in system ]

Supported graph formats

The graph loading infrastructure understands the following formats:

• graph.meta.txt text file specifying the meta information of the graph, including the number of vertices and edges; number of bytes for vertex IDs, edge IDs, vertex labels, and edge labels; maximum degree; feature vector length; distinct vertex label count; distinct edge label count; start index, end index and count of train/validation/test vertices.

• graph.vertex.bin binary file containing the row pointers, with data type of edge IDs.

• graph.edge.bin binary file containing the column indices, with data type of vertex IDs.

• graph.vlabel.bin binary file containing the vertex labels (only needed for vertex labeled graphs)

• graph.elabel.bin binary file containing the edge labels (only needed for edge labeled graphs)

• graph.feats.bin binary file containing the vertex feature vectors (used for graph machine learning)

• train.masks.bin binary file containing the masks for train vertex set

• val.masks.bin binary file containing the masks for validation vertex set

• test.masks.bin binary file containing the masks for test vertex set

An example graph is in inputs/citeseer

Other graph input formats to be supported:

• Market (.mtx), The University of Florida Sparse Matrix Collection
• Metis (.graph), 10th DIMACS Implementation Challenge
• SNAP (.txt), Stanford Network Analysis Project
• Dimacs9th (.gr), 9th DIMACS Implementation Challenge
• The Koblenz Network Collection (out.< name >), The Koblenz Network Collection
• Network Data Repository (.edges), Network Data Repository
• Real-World Input Graphs (Misc), Real-World Input Graphs

Code Documentation

The code documentation is located in the docs directory (doxygen html format).

Reporting bugs and contributing

If you find any bugs please report them by using the repository (github issues panel). We are also ready to engage in improving and extending the framework if you request new features.

Notes

Existing state-of-the-art frameworks:

Pangolin [1]: source code is in src/pangolin/

PBE [2,3]: https://github.com/guowentian/SubgraphMatchGPU

Peregrine [4]: https://github.com/pdclab/peregrine

Sandslash [5]: source code is in src/*/cpu_kernels/*_cmap.h

FlexMiner [6]: the CPU baseline code is in */cpu_kernels/*_base.h

DistTC [7]: source code is in src/triangle/

DeepGalois [8]: https://github.com/chenxuhao/GraphAIBench

GraphPi [9]: https://github.com/thu-pacman/GraphPi

[1] Xuhao Chen, Roshan Dathathri, Gurbinder Gill, Keshav Pingali. Pangolin: An Efficient and Flexible Graph Pattern Mining System on CPU and GPU. VLDB 2020

[2] Wentian Guo, Yuchen Li, Mo Sha, Bingsheng He, Xiaokui Xiao, Kian-Lee Tan. GPU-Accelerated Subgraph Enumeration on Partitioned Graphs. SIGMOD 2020.

[3] Wentian Guo, Yuchen Li, Kian-Lee Tan. Exploiting Reuse for GPU Subgraph Enumeration. TKDE 2020.

[4] Kasra Jamshidi, Rakesh Mahadasa, Keval Vora. Peregrine: A Pattern-Aware Graph Mining System. EuroSys 2020

[5] Xuhao Chen, Roshan Dathathri, Gurbinder Gill, Loc Hoang, Keshav Pingali. Sandslash: A Two-Level Framework for Efficient Graph Pattern Mining, ICS 2021

[6] Xuhao Chen, Tianhao Huang, Shuotao Xu, Thomas Bourgeat, Chanwoo Chung, Arvind. FlexMiner: A Pattern-Aware Accelerator for Graph Pattern Mining, ISCA 2021

[7] Loc Hoang, Vishwesh Jatala, Xuhao Chen, Udit Agarwal, Roshan Dathathri, Grubinder Gill, Keshav Pingali. DistTC: High Performance Distributed Triangle Counting, HPEC 2019

[8] Loc Hoang, Xuhao Chen, Hochan Lee, Roshan Dathathri, Gurbinder Gill, Keshav Pingali. Efficient Distribution for Deep Learning on Large Graphs, GNNSys 2021

[9] Tianhui Shi, Mingshu Zhai, Yi Xu, Jidong Zhai. GraphPi: high performance graph pattern matching through effective redundancy elimination. SC 2020

Publications

Please cite the following paper if you use this code:

@article{Pangolin,
	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={2020},
	journal = {Proc. VLDB Endow.},
	issue_date = {August 2020},
	volume = {13},
	number = {8},
	month = aug,
	year = {2020},
	numpages = {12},
	publisher = {VLDB Endowment},
}

@INPROCEEDINGS{FlexMiner,
  author={Chen, Xuhao and Huang, Tianhao and Xu, Shuotao and Bourgeat, Thomas and Chung, Chanwoo and Arvind},
  booktitle={2021 ACM/IEEE 48th Annual International Symposium on Computer Architecture (ISCA)}, 
  title={FlexMiner: A Pattern-Aware Accelerator for Graph Pattern Mining}, 
  year={2021},
  volume={},
  number={},
  pages={581-594},
  doi={10.1109/ISCA52012.2021.00052}
}

@inproceedings{DistTC,
  title={DistTC: High performance distributed triangle counting},
  author={Hoang, Loc and Jatala, Vishwesh and Chen, Xuhao and Agarwal, Udit and Dathathri, Roshan and Gill, Gurbinder and Pingali, Keshav},
  booktitle={2019 IEEE High Performance Extreme Computing Conference (HPEC)},
  pages={1--7},
  year={2019},
  organization={IEEE}
}

@inproceedings{Sandslash,
  title={Sandslash: a two-level framework for efficient graph pattern mining},
  author={Chen, Xuhao and Dathathri, Roshan and Gill, Gurbinder and Hoang, Loc and Pingali, Keshav},
  booktitle={Proceedings of the ACM International Conference on Supercomputing},
  pages={378--391},
  year={2021}
}

@inproceedings{hoang2019disttc,
  title={DistTC: High performance distributed triangle counting},
  author={Hoang, Loc and Jatala, Vishwesh and Chen, Xuhao and Agarwal, Udit and Dathathri, Roshan and Gill, Gurbinder and Pingali, Keshav},
  booktitle={2019 IEEE High Performance Extreme Computing Conference (HPEC)},
  pages={1--7},
  year={2019},
  organization={IEEE}
}

@inproceedings{DeepGalois,
  title={Efficient Distribution for Deep Learning on Large Graphs},
  author={Hoang, Loc and Chen, Xuhao and Lee, Hochan and Dathathri, Roshan and Gill, Gurbinder and Pingali, Keshav},
  booktitle={Workshop on Graph Neural Networks and Systems},
  volume={1050},
  pages={1-9},
  year={2021}
}

Developers

• Xuhao Chen, Research Scientist, MIT, cxh@mit.edu
• Tianhao Huang, PhD student, MIT

License

Copyright (c) 2021, MIT All rights reserved.