  Next generation radio telescopes will require orders of magnitude more
computing power to provide a view of the universe with greater sensitivity. In
the initial stages of the signal processing flow of a radio telescope, signal
correlation is one of the largest challenges in terms of handling huge data
throughput and intensive computations. We implemented a GPU cluster based
software correlator with various data distribution models and give a systematic
comparison based on testing results obtained using the Fornax supercomputer. By
analyzing the scalability and throughput of each model, optimal approaches are
identified across a wide range of problem sizes, covering the scale of next
generation telescopes.
