GraphMinerBench is a C++ implemented Benchmark Suite for Graph Pattern Mining (GPM), based on the implementations of state-of-the-art GPM Frameworks including Pangolin [1], Sandslash [2], FlexMiner [3] and G2Miner [4]. GraphMinerBench supports both multicore CPU and GPU, and is parallelized using OpenMP and CUDA respectively.
Unlike those GPM frameworks, GraphMinerBench is inherently designed for benchmarking hardware architecture design. It includes various GPM workloads (e.g., TC, k-CL, SgL, k-MC, FSM) as well as representative graph datasets (Mico, Patents, Youtube, LiveJournal, Orkut, Twitter, Friendster).
Datasets are available here. Please contact the author for more datasets.
[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] Xuhao Chen, Roshan Dathathri, Gurbinder Gill, Loc Hoang, Keshav Pingali. Sandslash: A Two-Level Framework for Efficient Graph Pattern Mining, ICS 2021
[3] Xuhao Chen, Tianhao Huang, Shuotao Xu, Thomas Bourgeat, Chanwoo Chung, Arvind. FlexMiner: A Pattern-Aware Accelerator for Graph Pattern Mining, ISCA 2021
[4] Xuhao Chen, Arvind. Efficient and Scalable Graph Pattern Mining on GPUs, OSDI 2022 Artifact
The document is organized as follows:
Edit env.sh to let the libraries pointing to the right paths in your system, and then:
$ source env.shJust make in the root directory:
$ makeOr go to each sub-directory, e.g. src/triangle, and then make:
$ cd src/triangle; makeBinaries 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_baseDatasets are available here. Run triangle counting with an undirected toy graph on CPU:
$ ./tc_omp_base ../inputs/citeseer/graphRun subgraph listing/counting for the diamond pattern with the LiveJournal graph on GPU:
$./sgl_gpu_base ../inputs/livej/graph diamondYou 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 ]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
You can find a graph converter here to generate the binary files from mtx, lg, sadj or txt format.
Other graph input formats to be supported:
The code documentation is located in the docs directory (doxygen html format).
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.
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
Please cite the following paper if you use this code:
@inproceedings {G2Miner,
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,
}@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}
}Copyright (c) 2021, MIT All rights reserved.