Using many-core hardware to correlate radio astronomy signals

Rob V. van Nieuwpoort, John W. Romein
ASTRON, Netherlands Institute for Radio Astronomy, Dwingeloo, The Netherlands
Proceedings of the 23rd international conference on Supercomputing, ICS ’09, 2009


   title={Using many-core hardware to correlate radio astronomy signals},

   author={Van Nieuwpoort, R.V. and Romein, J.W.},

   booktitle={Proceedings of the 23rd international conference on Supercomputing},





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A recent development in radio astronomy is to replace traditional dishes with many small antennas. The signals are combined to form one large, virtual telescope. The enormous data streams are cross-correlated to filter out noise. This is especially challenging, since the computational demands grow quadratically with the number of data streams. Moreover, the correlator is not only computationally intensive, but also very I/O intensive. The LOFAR telescope, for instance, will produce over 100 terabytes per day. The future SKA telescope will even require in the order of exaflops, and petabits/s of I/O. A recent trend is to correlate in software instead of dedicated hardware. This is done to increase flexibility and to reduce development efforts. Examples include e-VLBI and LOFAR. In this paper, we evaluate the correlator algorithm on multi-core CPUs and many-core architectures, such as NVIDIA and ATI GPUs, and the Cell/B.E. The correlator is a streaming, real-time application, and is much more I/O intensive than applications that are typically implemented on many-core hardware today. We compare with the LOFAR production correlator on an IBM Blue Gene/P supercomputer. We investigate performance, power efficiency, and programmability. We identify several important architectural problems which cause architectures to perform suboptimally. Our findings are applicable to data-intensive applications in general. The results show that the processing power and memory bandwidth of current GPUs are highly imbalanced for correlation purposes. While the production correlator on the Blue Gene/P achieves a superb 96% of the theoretical peak performance, this is only 14% on ATI GPUs, and 26% on NVIDIA GPUs. The Cell/B.E. processor, in contrast, achieves an excellent 92%. We found that the Cell/B.E. is also the most energy-efficient solution, it runs the correlator 5-7 times more energy efficiently than the Blue Gene/P. The research presented is an important pathfinder for next-generation telescopes.
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