Scaling Radio Astronomy Signal Correlation on Heterogeneous Supercomputers Using Various Data Distribution Methodologies
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}
}
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.
May 27, 2013 by hgpu
