Community Detection (Louvain)#
The Phase 1 writeup contains a detailed description of the application.
From the Phase 1 writeup:
Community detection in graphs means grouping vertices together, so that those vertices that are closer (have more connections) to each other are placed in the same cluster. A commonly used algorithm for community detection is Louvain (https://arxiv.org/pdf/0803.0476.pdf).
Scalability Summary#
Application is nonfunctional
Summary of Results#
The application has a segmentation fault and is currently nonfunctional.
Summary of Gunrock Implementation#
The Phase 1 single-GPU implementation is here.
We parallelize across GPUs by utilizing Gunrock’s multi-GPU ForAll operator described here.
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 louvain
Verify git SHA: commit d70a73c5167c5b59481d8ab07c98b376e77466cc
Partitioning the input dataset#
Partitioning is handled automatically as Community Detection relies on Gunrock’s multi-GPU ForALL operator and its data is split evenly across all available GPUs
Running the application#
Once functional, the application will follow the two script approach described in Running the Applications (i.e., using hive-mgpu-run.sh and hive-louvain-test.sh scripts).
Datasets#
Final datasets will be listed when the application is functional.
Output#
No change from Phase 1.
Performance and Analysis#
No change from Phase 1.
Implementation limitations#
Currently nonfunctional.
Performance limitations#
Currently nonfunctional.
Scalability behavior#
Currently unavailable, but unlikely to scale given its ForAll based implementation. See Gunrock’s ForAll Operator for additional information.