Getting Started

Hardware
- Intel CPU x86_64 with host memory 64GB. Tested on Intel Xeon Silver 4210 (10-core 20-thread) CPU with 512 GB host memory.
- NVIDIA GPU with device memory 24GB. Tested on RTX3090 and RTX2080. We mainly evaluate our design on RTX3090 and the execution time may be different across different devices but the peak memory usage remains same.
Software
- PyTorch 1.8.0+
- DGL 0.7.0+
- Ninja 1.10+
- GPUtil 1.4+
OS&Compilation
- Ubuntu 18.04+
- CUDA 11.0+

File Organization

src/: the CUDA source code (fused_edgeconv/fused_edgeconv.cu,fused_gat/fused_gat.cu, and fused_gmmconv/gmmconv.cu) for GNN sparse kernels implemented with our proposed techniques, python binding of kernels (fused_edgeconv/fused_edgeconv.cpp, fused_gat/fused_gat.cpp, and fused_gmmconv/gmmconv.cpp), and some utilizations (util/).
operators/: wrap our kernels into PyTorch modules.
layers/: use the operators above to build up GAT, EdgeConv, and MoNet layers.
script/: result analysis scripts to replicate our results.
example_data/: raw experiment results.
docker/: docker file for setting up the running environment.

Installation

To build our software, you need to install Ninja and PyTorch as shown in dependencies. We use the just-in-time compilation of the pytorch cpp-extension work flow.

Warning If you find the bebug such as below, please comment the relevant code.

  File "/usr/local/anaconda3/envs/pytorch/lib/python3.8/site-packages/torch/utils/cpp_extension.py", line 1606, in _get_cuda_arch_flags
    arch_list[-1] += '+PTX'
IndexError: list index out of range

Please make sure you have installed the nsight compute with your CUDA and configure your CUDA_HOME before replication. A typical path of nsight compute profiler is at /usr/local/bin/nv-nsight-cu-cli

Docker

To avoid different path of CUDA installation, we provide a docker to run our implementation. You could run our AE code in our docker container below. We are sorry that profiling result needed by nv-nsight-cu-cli couldn’t be accomplished in the docker.

cd docker
docker build -t gnn:v1 -f Dockerfile .
docker run -it --runtime=nvidia --rm -v /home/yzm18/ae_mlsys_gnn/:/AE gnn:v1 /bin/bash

Experiment Workflow

Go to script/ directory. First run bash config.sh.
Figure 7 result: ./figure7.sh to run end-to-end experiments on three GNN models. Generate figure7.csv and figure7_io.csv.
Figure 8 result: ./figure8.sh to run ablation study for operator reorganization. Generate figure8.csv and figure8_io.csv.
Figure 9 result: ./figure8.sh to run ablation study for operator fusion. Generate figure9.csv and figure9_io.csv.
Figure 10 result: ./figure10.sh to run ablation study for intermediate variable re-computation and generate figure10.csv.
Figure 11 result: run figure11_3090.sh on RTX3090 and figure11_2080.sh on RTX2080. Generate figure11_3090.csv and figure11_2080.csv.
IO result: run ./io.sh to get all the IO results. Generate figure7_io.csv, figure8_io.csv, and figure9_io.csv.
fuseGNN result: run training_main.py in the gcnLib submodule. Use the better result of gas and gar in --mode.

Evaluation and Expected Result

Once you have run the experiment workflow, you can see the .csv result under the script/ directory. The latency and memory results are stored in figureX.csv. The IO results can be seen in the corresponding figureX_io.csv.