9471

Scaling Radio Astronomy Signal Correlation on Heterogeneous Supercomputers Using Various Data Distribution Methodologies

Ruonan Wang, Christopher Harris
ICRAR, M468, The University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia
arXiv:1305.5639 [astro-ph.IM], (24 May 2013)

@article{2013arXiv1305.5639W,

   author={Wang}, R. and {Harris}, C.},

   title={"{Scaling Radio Astronomy Signal Correlation on Heterogeneous Supercomputers Using Various Data Distribution Methodologies}"},

   journal={ArXiv e-prints},

   archivePrefix={"arXiv"},

   eprint={1305.5639},

   primaryClass={"astro-ph.IM"},

   keywords={Astrophysics – Instrumentation and Methods for Astrophysics, Computer Science – Distributed, Parallel, and Cluster Computing},

   year={2013},

   month={may},

   adsurl={http://adsabs.harvard.edu/abs/2013arXiv1305.5639W},

   adsnote={Provided by the SAO/NASA Astrophysics Data System}

}

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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.
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