Luigi Cruz
SETI Institute

Wael Farah
SETI Institute

Richard Elkins
SETI Institute

Abstract

A common technique adopted by the Search For Extraterrestrial Intelligence (SETI) community is monitoring electromagnetic radiation for signs of extraterrestrial technosignatures using ground-based radio observatories. The analysis is made using a Python-based software called TurboSETI to detect narrowband drifting signals inside the recordings that can mean a technosignature. The data stream generated by a telescope can easily reach the rate of terabits per second. Our goal was to improve the processing speeds by writing a GPU-accelerated backend in addition to the original CPU-based implementation of the de-doppler algorithm used to integrate the power of drifting signals. We discuss how we ported a CPU-only program to leverage the parallel capabilities of a GPU using CuPy, Numba, and custom CUDA kernels. The accelerated backend reached a speed-up of an order of magnitude over the CPU implementation.

Keywords

gpu, numba, cupy, seti, turboseti

DOI

10.25080/majora-212e5952-004

Bibtex entry

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Copyright © 2022 Luigi Cruz et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.