2018
DOI: 10.1016/j.ascom.2018.10.002
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cuFFS: A GPU-accelerated code for Fast Faraday rotation measure Synthesis

Abstract: 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… Show more

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Cited by 7 publications
(6 citation statements)
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“…We then performed RM synthesis (e.g., Burn 1966;Brentjens & de Bruyn 2005) using the Compute Unified Device Architectureaccelerated Fast Faraday Synthesis (cuFFS; Sridhar, Heald, & van der Hulst 2018) software. As presented by Brentjens & de Bruyn (2005), the key parameters for RM synthesis-the RM resolution ( (RM)), maximum RM (|RM max |), and maximum scale in RM space (max.scale)-are defined as…”
Section: Data Reductionmentioning
confidence: 99%
“…We then performed RM synthesis (e.g., Burn 1966;Brentjens & de Bruyn 2005) using the Compute Unified Device Architectureaccelerated Fast Faraday Synthesis (cuFFS; Sridhar, Heald, & van der Hulst 2018) software. As presented by Brentjens & de Bruyn (2005), the key parameters for RM synthesis-the RM resolution ( (RM)), maximum RM (|RM max |), and maximum scale in RM space (max.scale)-are defined as…”
Section: Data Reductionmentioning
confidence: 99%
“…We used the new GPU-based cuFFS recipe b (Sridhar et al 2018) to perform RM synthesis on our mosaicked frequency cubes (each of which covers approximately 2 400-3 000 deg 2 of sky). cuFFS has been optimised for processing large data cubes on GPU-based systems, primarily for processing LOFAR polarimetric data.…”
Section: Rm Synthesismentioning
confidence: 99%
“…For example, RM Synthesis needs O(N 2 ) calculations if the discrete FT is used, or O(N log 2 N) if the Fast FT is used (e.g., [15]), where N is the number of frequency channels in the observation. A more detailed analysis and the opportunities presented by GPU acceleration are presented by Sridhar et al [16]. RM CLEAN involves an iterative loop for subtracting the factor-multiplied RMSF fromF(φ), and thus the computational cost is O (N × N CLEAN ), where N CLEAN is the number of required RM CLEAN iterations.…”
Section: Comparison Of Methodsmentioning
confidence: 99%