{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/fourier-domain-dedispersion","title":"Fourier-domain dedispersion","arxiv_id":"2110.03482","date":"2021-10-07","proceeding":null,"authors":["C. G. Bassa","J. W. Romein","B. Veenboer","S. van der Vlugt","S. J. Wijnholds"],"abstract":"We present and implement the concept of the Fourier-domain dedispersion (FDD) algorithm, a brute-force incoherent dedispersion algorithm. This algorithm corrects the frequency-dependent dispersion delays in the arrival time of radio emission from sources such as radio pulsars and fast radio bursts. Where traditional time-domain dedispersion algorithms correct time delays using time shifts, the FDD algorithm performs these shifts by applying phase rotations to the Fourier-transformed time-series data. Incoherent dedispersion to many trial dispersion measures (DMs) is compute, memory-bandwidth and I/O intensive and dedispersion algorithms have been implemented on Graphics Processing Units (GPUs) to achieve high computational performance. However, time-domain dedispersion algorithms have low arithmetic intensity and are therefore often memory-bandwidth limited. The FDD algorithm avoids this limitation and is compute limited, providing a path to exploit the potential of current and upcoming generations of GPUs. We implement the FDD algorithm as an extension of the DEDISP time-domain dedispersion software. We compare the performance and energy-to-completion of the FDD implementation using an NVIDIA Titan RTX GPU against the standard as well as an optimized version of DEDISP. The optimized implementation already provides a factor of 1.5 to 2 speedup at only 66% of the energy utilization compared to the original algorithm. We find that the FDD algorithm outperforms the optimized time-domain dedispersion algorithm by another 20% in performance and 5% in energy-to-completion when a large number of DMs (>=512) are required. The FDD algorithm provides additional performance improvements for FFT-based periodicity surveys of radio pulsars, as the FFT back to the time domain can be omitted. We expect that this computational performance gain will further improve in the future.","url_abs":"https://arxiv.org/abs/2110.03482v1","url_pdf":"https://arxiv.org/pdf/2110.03482v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"fourier-domain-dedispersion","repo_url":"https://github.com/svlugt/dedisp","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}