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Using the Fourier Domain Acceleration Search (FDAS) method to search forbinary pulsars is a computationally costly process. Next generation radiotelescopes will have to perform FDAS in real time, as data volumes are toolarge to store. FDAS is a matched filtering approach for searching time-domainradio astronomy datasets for the signatures of binary pulsars withapproximately linear acceleration. In this paper we will explore how we havereduced the energy cost of an SKA-like implementation of FDAS inAstroAccelerate, utilising a combination of mixed-precision computing anddynamic frequency scaling on NVIDIA GPUs. Combining the two approaches, we havemanaged to save 58% of the overall energy cost of FDAS with a (<3%) sacrificein numerical sensitivity.

Cutting the cost of pulsar astronomy: Saving time and energy when searching for binary pulsars using NVIDIA GPUs