1061700625 / SUGAR

Code for the paper 'SUGAR: Efficient Subgraph-level Training via Resource-aware Graph Partitioning'

SUGAR

This repository contains the code for SUGAR: Efficient Subgraph-level Training via Resource-aware Graph Partitioning.

Prepare

Necessary package to start with: torch + dgl + ogb + metis

Set up the environment

conda create -n sugar python=3.8
conda activate sugar

# install torch
pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu116

# install dgl
pip install dgl-cu116 dglgo -f https://data.dgl.ai/wheels/repo.html
# (may need this step if 'import dgl' yields error)
conda install cudatoolkit

# install ogb
pip install ogb

# install metis
pip install metis

Steps to install METIS

(1) Download METIS here, and unzip the file.

(2) Build METIS

cd metis-5.1.0
make config shared=1
make install
make

(3) If libmetis.dylib or libmetis.so is not under /usr/local/lib, find it inside directory build, and set up the METIS_DLL environment

export METIS_DLL=/usr/local/lib/libmetis.dylib
# or
export METIS_DLL=/your_directory/libmetis.dylib

(4) Check METIS is properly installed

python
>>> import metis

Training

ogbn-arxiv

Run baseline GCN with the full graph

python train.py --run-full

Running this command will automatically download the dataset to ./dataset/.

Run SUGAR:

python train.py --first-time 

When running the command for first time, adding '--first-time' will first partition the graph, and then start subgraph-level training. The graph partitioning results are saved to ./partition.

 

Other graphs

Coming soon