Dist-GPU-sampling
ChunkTensor
Hybrid-Memory Graph Store (HMGStore) supports using memory from different GPUs and CPU as a shared memory system. Based on HMGStore, we develop ChunkTensor which enable storing graph dataset cross different devices and host to reduce data transmission and improve GNN training performance.
A case for ChunkTensor usage:
c_a = torch.classes.dgs_classes.ChunkTensor([100], torch.int64, capacity_per_gpu=200)
if dist.get_rank(local_subgroup) == 0:
a = torch.arange(100).long()
c_a._CAPI_load_from_tensor(a)Note: Currently, this repo is only supported on systems where the GPUs have peer-to-peer access to each other.
Install
Requirement:
- CUDA >= 11.3
- NCCL >= 2.x
Install python dependencies.
$ pip3 install torch --extra-index-url https://download.pytorch.org/whl/cu116
$ pip install --pre dgl -f https://data.dgl.ai/wheels/cu116/repo.htmlInstall the system packages for building the shared library.
$ sudo apt-get update
$ sudo apt-get install -y build-essential python3-dev make cmakeDownload the source files.
$ git clone git@github.com:gpzlx1/Dist-GPU-sampling.git
$ git checkout v0.1Build.
$ mkdir build && cd build
$ cmake ..
$ make -j16After building, it will generate libdgs.so in ${WORKSPACE}/build.
Run demo
cd ${WORKSPACE}
$ torchrun --nproc_per_node 1 example/demo.py
[rank=0] Create ChunkTensor
[rank=0] Load data
[rank=0] Print HostTensor in ChunkTensor:
tensor([25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42,
43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60,
61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,
79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96,
97, 98, 99])
[rank=0] Print DeviceTensor in ChunkTensor:
tensor([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,
18, 19, 20, 21, 22, 23, 24], device='cuda:0')