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Fast Acquisition of Spread Spectrum Signals Using Multiple GPUs.

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Názov: Fast Acquisition of Spread Spectrum Signals Using Multiple GPUs.
Autori: Liu, Ying, Cui, Hongyuan, Zhao, Renliang
Zdroj: IEEE Transactions on Aerospace & Electronic Systems; Dec2019, Vol. 55 Issue 6, p3117-3125, 9p
Predmety: TELECOMMUNICATION satellites, REAL-time computing, GRAPHICS processing units, TELEMETRY
Abstrakt: Spread spectrum (SS) signal acquisition in satellite communication is a very computation intensive technique, which hinders the development of real-time spread spectrum signal acquisition. In this paper, in order to achieve real-time acquisition, we propose a multi-GPU based SS signal acquisition algorithm. First, sliding correlation, the computation kernel, is formulated and efficiently parallelized by CUDA. Second, a CUDA-enabled SS signal acquisition algorithm is implemented by adopting the CUDA-enabled sliding correlation. Third, a multi-GPU based algorithm is implemented by using multi-GPU programming. The performance is evaluated in a real SS telemetry system. Real-time acquisition is achieved in all cases by using a single K40 GPU. Furthermore, an average of 374.7 $\times$ speedup (max 473 $\times$) in 6 datasets is achieved when using four K40 GPUs. Good scalability is observed when varying the parameters. [ABSTRACT FROM AUTHOR]
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Databáza: Complementary Index
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Abstrakt:Spread spectrum (SS) signal acquisition in satellite communication is a very computation intensive technique, which hinders the development of real-time spread spectrum signal acquisition. In this paper, in order to achieve real-time acquisition, we propose a multi-GPU based SS signal acquisition algorithm. First, sliding correlation, the computation kernel, is formulated and efficiently parallelized by CUDA. Second, a CUDA-enabled SS signal acquisition algorithm is implemented by adopting the CUDA-enabled sliding correlation. Third, a multi-GPU based algorithm is implemented by using multi-GPU programming. The performance is evaluated in a real SS telemetry system. Real-time acquisition is achieved in all cases by using a single K40 GPU. Furthermore, an average of 374.7 $\times$ speedup (max 473 $\times$) in 6 datasets is achieved when using four K40 GPUs. Good scalability is observed when varying the parameters. [ABSTRACT FROM AUTHOR]
ISSN:00189251
DOI:10.1109/TAES.2019.2902695