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https://github.com/MachineLearningSystem/CAGNET
https://github.com/MachineLearningSystem/CAGNET
Last synced: about 2 months ago
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- Host: GitHub
- URL: https://github.com/MachineLearningSystem/CAGNET
- Owner: MachineLearningSystem
- License: other
- Fork: true (PASSIONLab/CAGNET)
- Created: 2022-07-18T10:32:30.000Z (about 2 years ago)
- Default Branch: master
- Last Pushed: 2022-07-14T21:15:34.000Z (about 2 years ago)
- Last Synced: 2024-07-27T17:57:44.563Z (2 months ago)
- Size: 19.8 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-AI-system - Reducing Communication in Graph Neural Network Training SC'20
README
# CAGNET: Communication-Avoiding Graph Neural nETworks
## Description
CAGNET is a family of parallel algorithms for training GNNs that can asymptotically reduce communication compared to previous parallel GNN training methods. CAGNET algorithms are based on 1D, 1.5D, 2D, and 3D sparse-dense matrix multiplication, and are implemented with `torch.distributed` on GPU-equipped clusters. We also implement these parallel algorithms on a 2-layer GCN.
For more information, please read our ACM/IEEE SC'20 paper [Reducing Communication in Graph Neural Network Training](https://arxiv.org/pdf/2005.03300.pdf).
**Contact:** Alok Tripathy ()
## Dependencies
- Python 3.6.10
- PyTorch 1.3.1
- PyTorch Geometric (PyG) 1.3.2
- CUDA 10.1
- GCC 6.4.0
On OLCF Summit, all of these dependencies can be accessed with the following
```bash
module load cuda # CUDA 10.1
module load gcc # GCC 6.4.0
module load ibm-wml-ce/1.7.0-3 # PyTorch 1.3.1, Python 3.6.10
# PyG and its dependencies
conda create --name gnn --clone ibm-wml-ce-1.7.0-3
conda activate gnn
pip install --no-cache-dir torch-scatter==1.4.0
pip install --no-cache-dir torch-sparse==0.4.3
pip install --no-cache-dir torch-cluster==1.4.5
pip install --no-cache-dir torch-geometric==1.3.2
```
## Compiling
This code uses C++ extensions. To compile these, run
```bash
cd sparse-extension
python setup.py install
```
## Documentation
Each algorithm in CAGNET is implemented in a separate file.
- `gcn_distr.py` : 1D algorithm
- `gcn_distr_15d.py` : 1.5D algorithm
- `gcn_distr_2d.py` : 2D algorithm
- `gcn_distr_3d.py` : 3D algorithm
Each file also as the following flags:
- `--accperrank ` : Number of GPUs on each node
- `--epochs ` : Number of epochs to run training
- `--graphname ` : Graph dataset to run training on
- `--timing ` : Enable timing barriers to time phases in training
- `--midlayer ` : Number of activations in the hidden layer
- `--runcount ` : Number of times to run training
- `--normalization ` : Normalize adjacency matrix in preprocessing
- `--activations ` : Enable activation functions between layers
- `--accuracy ` : Compute and print accuracy metrics (Reddit only)
- `--replication ` : Replication factor (1.5D algorithm only)
- `--download ` : Download the Reddit dataset
Some of these flags do not currently exist for the 3D algorithm.
Amazon/Protein datasets must exist as COO files in `../data//processed/`, compressed with pickle.
For Reddit, PyG handles downloading and accessing the dataset (see below).
## Running on OLCF Summit (example)
To run the CAGNET 1.5D algorithm on Reddit with
- 16 processes
- 100 epochs
- 16 hidden layer activations
- 2-factor replication
run the following command to download the Reddit dataset:
`python gcn_distr_15d.py --graphname=Reddit --download=True`
This will download Reddit into `../data`. After downloading the Reddit dataset, run the following command to run training
`ddlrun -x WORLD_SIZE=16 -x MASTER_ADDR=$(echo $LSB_MCPU_HOSTS | cut -d " " -f 3) -x MASTER_PORT=1234 -accelerators 6 python gcn_distr_15d.py --accperrank=6 --epochs=100 --graphname=Reddit --timing=False --midlayer=16 --runcount=1 --replication=2`
## Citation
To cite CAGNET, please refer to:
> Alok Tripathy, Katherine Yelick, Aydın Buluç. Reducing Communication in Graph Neural Network Training. Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis (SC’20), 2020.