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HipMCL is a high-performance parallel algorithm for large-scale network
clustering. HipMCL parallelizes popular Markov Cluster (MCL) algorithm that has
been shown to be one of the most successful and widely used algorithms for
network clustering. It is based on random walks and was initially designed to
detect families in protein-protein interaction networks. Despite MCL's
efficiency and multi-threading support, scalability remains a bottleneck as it
fails to process networks of several hundred million nodes and billion edges in
an affordable running time. HipMCL overcomes all of these challenges by
developing massively-parallel algorithms for all components of MCL. HipMCL can
be 1000 times faster than the original MCL without any information loss. It can
easily cluster a network of ~75 million nodes with ~68 billion edges in ~2.4
hours using ~2000 nodes of Cori supercomputer at NERSC. HipMCL is developed in
C++ language and uses standard OpenMP and MPI libraries for shared- and
distributed-memory parallelization.
WWW: https://bitbucket.org/azadcse/hipmcl/wiki/Home
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