Venkatesan T. Chakaravarthy, Fabio Checconi, et al.
IEEE TPDS
In this paper we present and evaluate a parallel community detection algorithm derived from the state-of-the-artLouvain modularity maximization method. Our algorithm adoptsa novel graph mapping and data representation, and relies onan efficient communication runtime, specifically designed forfine-grained applications executed on large-scale supercomputers. We have been able to parallelize graphs with up to 138 billion edges on 8, 192 Blue Gene/Q nodes and 1, 024 P7-IH nodes. Leveraging the convergence properties of our algorithm and the efficient implementation, we can analyze communities of large scalegraphs in just a few seconds. To the best of our knowledge, this is the first parallel implementation of the Louvain algorithm that scales to these large data and processor configurations.
Venkatesan T. Chakaravarthy, Fabio Checconi, et al.
IEEE TPDS
Fabio Checconi, Luigi Rizzo, et al.
IEEE/ACM TON
Fabrizio Petrini, Virat Agarwal, et al.
SC 2009
Sarunya Pumma, Daniele Buono, et al.
CCGRID 2020