Accelerating Radio Astronomy Cross-Correlation with Graphics Processing Units

July 22nd, 2011

Abstract:

We present a highly parallel implementation of the cross-correlation of time-series data using graphics processing units (GPUs), which is scalable to hundreds of independent inputs and suitable for the processing of signals from “Large-N” arrays of many radio antennas. The computational part of the algorithm, the X-engine, is implementated efficiently on Nvidia’s Fermi architecture, sustaining up to 79% of the peak single precision floating-point throughput. We compare performance obtained for hardware- and software-managed caches, observing significantly better performance for the latter. The high performance reported involves use of a multi-level data tiling strategy in memory and use of a pipelined algorithm with simultaneous computation and transfer of data from host to device memory. The speed of code development, flexibility, and low cost of the GPU implementations compared to ASIC and FPGA implementations have the potential to greatly shorten the cycle of correlator development and deployment, for cases where some power consumption penalty can be tolerated.

(M. A. Clark, P. C. La Plante, L. J. Greenhill: “Accelerating Radio Astronomy Cross-Correlation with Graphics Processing Units”, July 2011. [Preprint on ARXIV] [Sources on GITHUB])

  • wsbriggs

    Unfortunately, this paper neglects to mention the poster talk given by
    Kjeld v.d. Schaaf and Ruud Ovrem (both from Astron-LOFAR). at the GPGPU Workshop just prior to SIGGRAPH 2004.

    It’s a pity, as they had recognized the potential of GPU acceleration while the majority of astronomers were still looking at ASICs, and FPGAs, not to mention the IBM Cell processor.

  • Mike Clark

    Thank you for pointing this out. I was completely unaware of this earlier work. This citation will be added to the final paper (still in review).

    I would suggest though, that your comment would have been better served if you had contacted on of us (the authors) directly.