GraphSAGE#
The Phase 1 writeup contains a detailed description of the application.
From the Phase 1 writeup:
GraphSAGE is a way to fit graphs into a neural network: instead of getting the embedding of a vertex from all its neighbors’ features as in conventional implementations, GraphSAGE selects some 1-hop neighbors, some 2-hop neighbors connected to those 1-hop neighbors, and computes the embedding based on the features of the 1-hop and 2-hop neighbors. The embedding can be considered as a vector containing hash values describing the interesting properties of a vertex.
Scalability Summary#
Bottlenecked by network bandwidth between GPUs
Summary of Results#
We rely on Gunrock’s multi-GPU ForALL operator to implement GraphSAGE. We see no scaling as we sweep from one to sixteen GPUs due to communication over GPU interconnects.
Summary of Gunrock Implementation#
The Phase 1 single-GPU implementation is here.
We parallelize across GPUs by utilizing a multi-GPU For-All operator and evenly distribute relevant arrays across multiple GPUs. Please see Gunrock’s ForAll Operator for more details.
Differences in implementation from Phase 1#
No change from Phase 1.
How To Run This Application on NVIDIA’s DGX-2#
Prerequisites#
git clone https://github.com/gunrock/gunrock -b multigpu
mkdir build
cd build/
cmake ..
make -j16 sage
Verify git SHA: commit d70a73c5167c5b59481d8ab07c98b376e77466cc
Partitioning the input dataset#
Partitioning is handled automatically as GraphSage relies on Gunrock’s multi-GPU ForALL operator and its frontier vertices are split evenly across all available GPUs. Please refer to the chapter on Gunrock’s ForAll Operator for additional information.
Running the application (default configurations)#
From the build directory
cd ../examples/sage/
./hive-mgpu-run.sh
This will launch jobs that sweep across 1 to 16 GPU configurations per dataset and application option as specified in hive-sage-test.sh.
Running the Applications chapter for details on running with additional datasets
for additional parameter information, review the provided script, and see Running the Applications chapter for details on running with additional datasets.
Datasets#
Default Locations:
/home/u00u7u37rw7AjJoA4e357/data/gunrock/hive_datasets/mario-2TB
/home/u00u7u37rw7AjJoA4e357/data/gunrock/gunrock_dataset/mario-2TB/large
Names:
pokec
dir_gs_twitter
europe_osm
Running the application (alternate configurations)#
hive-mgpu-run.sh#
Modify OUTPUT_DIR to store generated output and json files in an alternate location.
hive-sage-test.sh#
Modify APP_OPTIONS to specify alternate --undirected and --batch-size options. Please see the Phase 1 single-GPU implementation details here for additional parameter information, review the provided script, and see Running the Applications chapter for details on running with additional datasets.
Output#
No change from Phase 1.
Performance and Analysis#
No change from Phase 1.
Implementation limitations#
No change from Phase 1.
Performance limitations#
Single-GPU: No change from Phase 1.
Multiple-GPUs: Performance bottleneck is the remote memory accesses from one GPU to another GPU’s memory through NVLink.
Scalability behavior#
We observe no scaling with the current GraphSAGE implementation. Please see the chapter on Gunrock’s ForAll Operator for a discussion on future directions around more specialized operators to be designed with communication patterns in mind.