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Contents

This folder contains codes for implementing Louvain method for graph community detection/clustering. Currently, it only implements the first phase of Louvain.

This code requires an OpenMP runtime and a and C++11 compliant compiler for building. Experimental OpenMP offloading support is included (must pass -DUSE_OMP_OFFLOAD macro).

Please contact the following for any queries or support: Sayan Ghosh, PNNL (sg0 at pnnl dot gov)

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Compile

Just invoking `make should build the program, without any changes made to the Makefile. Please pass -DUSE_OMP_OFFLOAD and specific compiler options.

If you are running the code on a multi-socket system, pass -DGRAPH_FT_LOAD= while building where x == #sockets or #NUMA-nodes (it is 1 by default) to leverage first-touch access.

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Input

We require graphs to be converted from its native format to a binary format. The binary converter is part of another application, please follow the instructions for using Vite for file conversion: https://github.com/Exa-Graph/vite

Please note, this code requires weighted graphs (weight==1 is fine, outcome unknown for negative weights). There is an option in the Vite converter to ignore weights in graphs, which makes all edge weights zero. Such a graph won't be processed in this case.

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Execute

./matching_omp -f karate.bin

Apart from using external file, it is possible to generate in-memory a random geometric graph in a distributed fashion.

Possible options (can be combined):

1. -f : Specify input binary file after this argument.
2. -n : Pass total number of vertices of the generated graph.
3. -l : Use distributed LCG for randomly choosing edges. If this option is not used, we will use C++ random number generator (using std::default_random_engine).
4. -p : Specify percent of overall edges to be randomly generated between processes.
5. -w : Use Euclidean distance as edge weight. If this option is not used, edge weights are considered as 1.0. Generate edge weight uniformly between (0,1) if Euclidean distance is not available (applicable to randomly generated edges).
6. -u : Unit edge weights, i.e., make edge weights 1. This can potentially change outcome of the program, as original edge weights will be ignored when this option is used. If you are unsure about the weights of the original graph and want to check a sample, invoke g->print_preview() on main (if nothing is shown, there is some issue with the binary graph itself).
7. -h : Print sample instances to run this code.