WholeGraph is developed to help train large-scale Graph Neural Networks(GNN).
The software stack of WholeGraph shown below:
Based on CUDA and NVIDIA's hardware, a multi-GPU storage called WholeMemory is implemented. WholeMemory is used to support both graph structure and feature embedding storage. Based on the storage, WholeGraph Ops are built to support GNN applications, including sampling ops, embedding ops and some GNN layer ops On the other hand, sampled subgraph from WholeGraph can also be converted to support PyG or DGL layers. So, GNN applications can utilize ops from WholeGraph and other GNN frameworks.
It is suggested to use NVLink systems, like DGX-A100 or similar systems.
It is recommended to use our Dockerfile
To compile WholeGraph, from source directory:
mkdir build
cd build
cmake ../
make -jOr you can build release version by replacing cmake ../ by cmake -DCMAKE_BUILD_TYPE=Release .., which has slightly better performance.
Checkout GNN example for more details.