PaGraph

Scaling GNN Training on Large Graphs via Computation-aware Caching and Partitioning. Build based on DGL with PyTorch backend.

PaGraph in master branch supports data caching and graph partition (paper). In overlap branch it additionally supports overlapping data loading and GPU computation (paper).

Prerequisite

• Python 3

• PyTorch (v >= 1.3)

• DGL (v == 0.4.1)

Prepare Dataset

• Dataset Format (vnum denotes vertex number):

  • adj.npz: graph adjacancy matrix with (vnum, vnum) shape. Saved in scipy.sparse coo matrix format.

  • labels.npy: vertex label with (vnum,) shape. Saved in numpy.array format.

  • test.npy, train.npy, val.npy: boolean array with (vnum,) shape. Saved as numpy.array. Each element indicates whether the vertex is a train/test/val vertex.

  • (Optional) feat.npy: feature of vertex with (vnum, feat-size) shape. Saved as numpy.array. If not provided, will be randomly initialized (feat size is defaultly set to 600, can be changed in Pagraph/data/get_data line 27).

• Convert dataset from DGL:

  $ python PaGraph/data/dgl2pagraph.py --dataset reddit --self-loop --out-dir /folders/to/save

• For randomly generated dataset:

  • Use PaRMAT to generate a graph:

    $ ./PaRMAT -noDuplicateEdges -undirected -threads 16 -nVertices 10 -nEdges 25 -output /path/to/datafolder/pp.txt
    

  • Generate random features, labels, train/val/test datasets:

    $ python data/preprocess.py --ppfile pp.txt --gen-feature --gen-label --gen-set --dataset xxx/datasetfolder

    This may take a while to generate all of these.

• Generating partitions (naive partition):

  • For hash partition:

    $ python PaGraph/partition/hash.py --num-hops 1 --partition 2 --dataset xxx/datasetfolder

  • For dg-based partition:

    $ python PaGraph/partition/dg.py --num-hops 1 --partition 2 --dataset xxx/datasetfolder

Run

Install

$ python setup.py develop
$ python
>> import PaGraph
>> 

Launch Graph Server

• PaGraph Store Server:

  $ python server/pa_server.py --dataset xxx/datasetfolder --num-workers [gpu-num] [--preprocess] [--sample]

  Note --sample is for enabling remote sampling.

• DGL+Cache Store Server:

  $ python server/cache_server.py --dataset xxx/datasetfolder --num-workers [gpu-num] [--preprocess] [--sample]

For more instructions, checkout server launch files.

Run Trainer

• Graph Convolutional Network (GCN)

  • DGL benchmark

    $ python examples/profile/dgl_gcn.py --dataset xxx/datasetfolder --gpu [gpu indices, splitted by ','] [--preprocess] [--remote-sample]

  • PaGraph

    $ python examples/profile/pa_gcn.py --dataset xxx/datasetfolder --gpu [gpu indices, splitted by ','] [--preprocess] [--remote-sample]

Note: --remote-sample is for enabling isolation. This should be cooperated with server command --sample.

Note: multi-gpus training require OMP_NUM_THREADS settings, or it will show low scalability.

Reminder

Partition is aware of GNN model layers. Please guarantee the consistency of --num-hops, --preprocess when partitioning and training, respectively. Specifically, if --preprocess is enabled in both server and trainer, --num-hops should be the Num of model-layer - 1. Otherwise, keep --num-hops the same as number of GNN layers. In our settings, GCN and GraphSAGE has 2 layers.

Profiling

• NVProf on multi-processes command line:

  $ nvprof --profile-all-processes --csv --log-file %pprof.csv

• Pytorch Profiler:

  Run script in examples/ as mentioned above.

Citing PaGraph

@inproceedings{lin2020pagraph,
  title={PaGraph: Scaling GNN training on large graphs via computation-aware caching},
  author={Lin, Zhiqi and Li, Cheng and Miao, Youshan and Liu, Yunxin and Xu, Yinlong},
  booktitle={Proceedings of the 11th ACM Symposium on Cloud Computing},
  pages={401--415},
  year={2020}
}

@article{bai2021efficient,
  title={Efficient Data Loader for Fast Sampling-based GNN Training on Large Graphs},
  author={Bai, Youhui and Li, Cheng and Lin, Zhiqi and Wu, Yufei and Miao, Youshan and Liu, Yunxin and Xu, Yinlong},
  journal={IEEE Transactions on Parallel \& Distributed Systems},
  number={01},
  pages={1--1},
  year={2021},
  publisher={IEEE Computer Society}
}

License

This project is under MIT License.

Future Plan

We plan to support PaGraph on MindSpore