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NeutronStarLite

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A Distributed System for GNN Training

// repository documentation

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**NeutronStar** is a distributed Graph Neural Networks (GNN) training framework that supports CPU-GPU heterogeneous computation on multiple workers.

NeutronStar distinguishes itself from other GNN training frameworks with the following new properties:

* The dependency management (how to acquire the embeddings and gradients from neighbors) plays a key role in distributed GNN training. NeutronStar combines the cache-based dependency management that are adopted by DGL and Aligraph and the communication-based dependency management method that are widely adopted by traditional graph computing systems, and proposes a hybrid dependency management GNN distributed training system. NeutronStar can determine the optimal way to acquire the embeddings (during forward propagation) and the gradients (during backward propagation) from neighboring vertices. 
 * NeutronStar integrates the pytorch automatic differentiation library libtorch and tensorflow to support automatic differentiation (automatic backpropagation) across workers. 
 * NeutronStar is enhanced with many optimization techniques from traditional distributed graph computing systems to effectively accelerate the performance of distributed GNN training, such as CPU-GPU I/O optimization, Ring-based Communication, Overlapping Communication with Computation, Lock-free Parallel Message Enqueuing, and so on.

The overall architecture of NeutronStar
![architecture](https://user-images.githubusercontent.com/11622204/157367313-275431a3-09f5-4a7c-a8eb-b86317ef6713.png)

Currently NeutronStar is under refactoring. We will release all features of NeutronStar soon.
(The optimizations and auto. diff. functions are almost finished.)
(The new DepCache engine is under progress.)

## Quick Start

A compiler supporting **OpenMP** and **C++11** features (e.g. lambda expressions, multi-threading, etc.) is required.

**cmake** >=3.14.3

**MPI** for inter-process communication

**cuda** > 9.0 for GPU based graph operation.

**libnuma** for NUMA-aware memory allocation.

**cub** for GPU-based graph propagation

```
sudo apt install libnuma-dev"
```

**libtorch** version > 1.7 with gpu support for nn computation

unzip the **libtorch** package in the root dir of **NeutronStar** and change CMAKE_PREFIX_PATH in "CMakeList.txt"to your own path

download **cub** to the ./NeutronStar/cuda/ dictionary.

configure PATH and LD_LIBRARY_PATH for **cuda** and **mpi**
```
export CUDA_HOME=/usr/local/cuda-10.2
export MPI_HOME=/path/to/your/mpi
export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH
export PATH=$MPI_HOME/bin:$CUDA_HOME/bin:$PATH
```

**clang-format** is optional for auto-formatting: 
```shell
sudo apt install clang-format
```

#configure "CUDA_ENABLE" flag in ./cuda/cuda_type.h (line 20) to '1' or '0' to enable or disable GPU compilation.
If you only need a CPU-base NeutronStar
please disable **GPU compilation** with the following command at configure time:
```shell
cmake -DCUDA_ENABLE=OFF ..
```

To build:
```shell
mkdir build

cd build

cmake ..

make -j4
```

To run:

List all nodes in ./NeutronStar/hostfile for MPI communication
```
ip1
ip2
ip3
```
copy NeutronStar to all your machines(copy_all.sh and make_and_copy.sh) and run the following command in your root DIR.

single-machine multi-slots:(strongly recommand use one slot, except for debugging)
```
./run_nts.sh $slot_number $configure_file
./run_nts.sh 1 gcn_cora.cfg
```
distributed:

```
./run_nts_dist.sh $nodes_number $configure_file
./run_nts_dist.sh 2 gcn_cora.cfg
```

ENGINE TYPE:
We list serveral example in the root dir for your reference
GCN:
gcn_cora.cfg

gcn_pubmed.cfg

gcn_citeseer.cfg

gcn_reddit.cfg

gcn_reddit_full.cfg

NeutronStarLite

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