cuFFS: A GPU-accelerated code for Fast Faraday rotation measure Synthesis

S. S. Sridhar*, G. Heald, J. M. van der Hulst

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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Rotation measure (RM) synthesis is a widely used polarization processing algorithm for reconstructing polarized structures along the line of sight. Performing RM synthesis on large datasets produced by telescopes like LOFAR can be computationally intensive as the computational cost is proportional to the product of the number of input frequency channels, the number of output Faraday depth values to be evaluated and the number of lines of sight present in the data cube. The required computational cost is likely to get worse due to the planned large area sky surveys with telescopes like the Low Frequency Array (LOFAR), the Murchison Widefield Array (MWA), and eventually the Square Kilometre Array (SKA). The massively parallel General Purpose Graphical Processing Units (GPGPUs) can be used to execute some of the computationally intensive astronomical image processing algorithms including RM synthesis. In this paper, we present a GPU-accelerated code, called cuFFS or CUDA-accelerated Fast Faraday Synthesis, to perform Faraday rotation measure synthesis. Compared to a fast single-threaded and vectorized CPU implementation, depending on the structure and format of the data cubes, our code achieves an increase in speed of up to two orders of magnitude. During testing, we noticed that the disk I/O when using the Flexible Image Transport System (FITS) data format is a major bottleneck and to reduce the time spent on disk I/O, our code supports the faster HDFITS format in addition to the standard FITS format. The code is written in C with GPU-acceleration achieved using Nvidia's CUDA parallel computing platform. The code is available at (C) 2018 Elsevier B.V. All rights reserved.

Original languageEnglish
Pages (from-to)205-212
Number of pages8
JournalAstronomy and Computing
Publication statusPublished - Oct-2018


  • Methods: data analysis
  • Techniques: image processing
  • Techniques: polarimetric
  • Computing methodologies: graphics processors

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