Accelerating radio astronomy cross-correlation with graphics processing units


Clark M, LaPlante P, Greenhill L. Accelerating radio astronomy cross-correlation with graphics processing units. IJHPCA. 2013;27 :178-192.


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-Formula’ arrays of many radio antennas. The computational part of the algorithm, the X-engine, is implemented 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 with application-specific integrated circuit (ASIC) and field programmable gate array (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.

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Last updated on 02/25/2015