NeutronOrch is a system for sample-based GNN training that incorporates a layer-based task orches-trating method and ensures balanced utilization of the CPU and GPU. NeutronOrch distinguishes itself from other GNN training frameworks with the following new properties:

• NeutronOrch use a hotness-aware layer-based task orchestrating method that effectively leverages the computation and memory resources of the GPU-CPU heterogeneous system.
• NeutronOrch integrates the pytorch automatic differentiation library libtorch and tensorflow to support automatic differentiation (automatic backpropagation) across workers.
• NeutronOrch use a super-batch pipelined training, which fully overlaps different tasks on heterogeneous resources while strictly guaranteeing bounded staleness.

Currently NeutronOrch is under refactoring. We will release all features of NeutronOrch soon.

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 > 11.0 for GPU based graph operation.

TBB for C++ 17 feature.

libnuma for NUMA-aware memory allocation.

sudo apt install libnuma-dev

libtorch version > 1.11 with gpu support for nn computation

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

configure PATH and LD_LIBRARY_PATH for cuda and mpi

export CUDA_HOME=/usr/local/cuda
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:

sudo apt install clang-format

To build:

mkdir build

cd build

cmake ..

make -j4

To run NeutronOrch:

./nto_run.sh ${cfg_file} 

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