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Parallel Performance Studies for a Clustering Algorithm

Affinity propagation is a clustering algorithm that functions by identifying similar datapoints in an iterative process. Its structure allows for taking full advantage of parallel computing by enabling the solution of larger problems and by solving them faster than possible in serial. We show that our memory-optimal implementation with minimal number of communication commands per iteration performs excellently on the distributed-memory cluster hpc and that it is efficient to use all 128 processor cores currently available.