Skip to content
Free planNo credit card requiredStart writing

article · Journal of Astronomical Telescopes Instruments and Systems · 2023

Efficient channelization on a graphics processing unit

Bruce Merry

Cited by 0Open access

Abstract

We present an implementation of a channelizer (F-engine) running on a graphics processing unit (GPU). While not the first GPU implementation of a channelizer, we have put significant effort into optimizing the implementation. We are able to process 4 antennas each with 2 Gsample / s, 10-bit dual-polarized input and 8-bit output, on a single commodity GPU. This fully utilizes the available peripheral component interconnect express (PCIe) bandwidth of the GPU. The system is not as optimized for a single high-bandwidth antenna but handles 6.2 Gsample / s, limited by single-core central processing unit (CPU) performance.

Cite this paper

APA 7

Merry, B. (2023). Efficient channelization on a graphics processing unit. Journal of Astronomical Telescopes Instruments and Systems, 9(03). https://doi.org/10.1117/1.jatis.9.3.038001

MLA 9

Merry, Bruce. “Efficient channelization on a graphics processing unit.” Journal of Astronomical Telescopes Instruments and Systems, vol. 9, no. 03, 2023, https://doi.org/10.1117/1.jatis.9.3.038001.

Chicago

Merry, Bruce. 2023. “Efficient channelization on a graphics processing unit.” Journal of Astronomical Telescopes Instruments and Systems 9 (03). https://doi.org/10.1117/1.jatis.9.3.038001.

Harvard

Merry, B. (2023) ‘Efficient channelization on a graphics processing unit’, Journal of Astronomical Telescopes Instruments and Systems, 9(03). Available at: https://doi.org/10.1117/1.jatis.9.3.038001.

BibTeX
@article{merry2023efficient,
  title = {{Efficient channelization on a graphics processing unit}},
  author = {Merry, Bruce},
  journal = {Journal of Astronomical Telescopes Instruments and Systems},
  volume = {9},
  number = {03},
  year = {2023},
  doi = {10.1117/1.jatis.9.3.038001}
}

Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.