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Building Correlators with Many-Core Hardware

Rob V. van Nieuwpoort and John W. Romein
Stichting ASTRON (Netherlands Institute for Radio Astronomy), Oude Hoogeveensedijk 4, 7991 PD Dwingeloo, The Netherlands
IEEE Signal Processing Magazine, Issue Date: March 2010, Vol. 27, Issue:2, p.108-117

@article{van2010building,

   title={Building Correlators with Many-Core Hardware},

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

   journal={Signal Processing Magazine, IEEE},

   volume={27},

   number={2},

   pages={108–117},

   issn={1053-5888},

   year={2010},

   publisher={IEEE}

}

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Radio telescopes typically consist of multiple receivers whose signals are cross-correlated to filter out noise. A recent trend is to correlate in software instead of custom-built hardware, taking advantage of the flexibility that software solutions offer. Examples include e-VLBI and LOFAR. However, the data rates are usually high and the processing requirements challenging. Many-core processors are promising devices to provide the required processing power. In this paper, we explain how to implement and optimize signal-processing applications on multi-core CPUs and manycore architectures, such as the Intel Core i7, NVIDIA and ATI GPUs, and the Cell/B.E. We use correlation as a running example. The correlator is a streaming, possibly 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 discuss several important architectural problems which cause architectures to perform suboptimally, and also deal with programmability. The correlator on the Blue Gene/P achieves a superb 96% of the theoretical peak performance. We show that the processing power and memory bandwidth of current GPUs are highly imbalanced. Because of this, the correlator achieves only 16% of the peak on ATI GPUs, and 32% on NVIDIA GPUs. The Cell/B.E. processor, in contrast, achieves an excellent 92%. Many of the insights we discuss here are not only applicable to telescope correlators, are valuable when developing signal-processing applications in general.
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