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