Custom Computing Design and Implementation for Multiple Dedispersion with GPU

Kong, Xiangcong; Zheng, Xiaoying; Zhu, Yongxin; Zhang, Qinrun; Huang, Ying

doi:10.1109/cscloud-edgecom52276.2021.00028

Cited by 3 publications

(2 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, the subband division cannot be infinitely small, so the dispersion effect in the subband cannot be removed by the incoherent dispersion algorithm. For precision observation, incoherent dedispersion technology cannot meet the accuracy requirement (Kong et al 2021). In 1975, Rickett (1975 proposed a pulsar coherent dispersion algorithm.…”

Section: Introductionmentioning

confidence: 99%

Research on a Coherent Dedispersion Algorithm for Pulsar Baseband Data

Zhang¹,

ZHANG²,

Wang³

et al. 2023

Res. Astron. Astrophys.

View full text Add to dashboard Cite

When the pulsar signal propagates in the interstellar medium(ISM), the high frequency and low frequency components of the signal reach the radio telescope with a certain delay. Therefore, the pulsar signal will appear energy dispersion, which will broaden the pulse profile, decrease the signal to noise ratio(SNR), and even lead to the disappearance of the pulse signal. In this paper, we analyze the sampling, polarization and arrangement of baseband data based on the coherent dedispersion algorithm for the problem of pulsar baseband data dedispersion. We systematically study the coherent dedispersion data processing procedure and test the pulse profile changes under different FFT block sizes. An optimal selection strategy of FFT block sizes is proposed for reducing the operation time and obtaining a better pulse profile. We propose two methods, one is the generation of ISM transfer function, the other is the pulsar period and phase prediction method at a certain time, and discuss integral and folding strategies. We test the algorithm based on the baseband data of CASPSR and Medusa terminals observed by Parkes 64m radio telescope, and analyze the reading and processing methods of baseband data of different terminals. The experimental results show that the pulse profile with SNR greater than 200 is obtained, which verifies the effectiveness of the algorithm.

show abstract

Section: Introductionmentioning

confidence: 99%

Research on a Coherent Dedispersion Algorithm for Pulsar Baseband Data

Zhang¹,

ZHANG²,

Wang³

et al. 2023

Res. Astron. Astrophys.

View full text Add to dashboard Cite

show abstract

“…Several different codes exist with differing implementations of dedispersion specifically for graphics-processing units (GPUs; Magro et al 2011;Barsdell et al 2012;Sclocco et al 2016;Bassa et al 2017;Zackay & Ofek 2017;Kong et al 2021). There are other implementations of the dedispersion transform-for example, using fast Fourier transforms (Bassa et al 2022).…”

Section: Introductionmentioning

confidence: 99%

Accelerating Dedispersion Using Many-core Architectures

Novotný,

Adámek,

Clark

et al. 2023

ApJS

View full text Add to dashboard Cite

Astrophysical radio signals are excellent probes of extreme physical processes that emit them. However, to reach Earth, electromagnetic radiation passes through the ionized interstellar medium, introducing a frequency-dependent time delay (dispersion) to the emitted signal. Removing dispersion enables searches for transient signals like fast radio bursts or repeating signals from isolated pulsars or those in orbit around other compact objects. The sheer volume and high resolution of data that next-generation radio telescopes will produce require high-performance computing solutions and algorithms to be used in time-domain data-processing pipelines to extract scientifically valuable results in real time. This paper presents a state-of-the-art implementation of brute force incoherent dedispersion on NVIDIA graphics-processing units and on Intel and AMD central-processing units. We show that our implementation is 4× faster (8-bit 8192 channels input) than other available solutions, and we demonstrate, using 11 existing telescopes, that our implementation is at least 20× faster than real time. This work is part of the AstroAccelerate package.

show abstract