HCGrid: Mapping non-uniform radio astronomy data onto a uniformly distributed grid

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Ada Coda
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HCGrid: Mapping non-uniform radio astronomy data onto a uniformly distributed grid

Post by Ada Coda » Fri Jan 01, 2021 12:44 am

HCGrid: Mapping non-uniform radio astronomy data onto a uniformly distributed grid

Abstract: HCGrid maps non-uniform radio astronomy data onto a uniformly distributed grid using a convolution-based algorithm on CPU-GPU heterogeneous platforms. The package has three modules; the initialization module initializes parameters needed for the calculation process, such as setting the size of the sampling space and output resolution. The gridding module uses a parallel ordering algorithm to pre-order the sampling points based on HEALPix on the CPU platform and uses an efficient two-level lookup table to speed up the acquisition of sampling points; it then accelerates convolution by using the high parallelism of GPU and through related performance optimization strategies based on CUDA architecture to further improve the gridding performance. The third module processes the results; it visualizes the gridding and exports the final products as FITS files.

Credit: Wang, Hao; Yu, Ce; Zhang, Bo; Xiao, Jian; Luo, Qi

Site: https://github.com/HWang-Summit/HCGrid
https://ui.adsabs.harvard.edu/abs/2021MNRAS.501.2734W

Bibcode: 2020ascl.soft12023W

ID: ascl:2012.023
Last edited by Ada Coda on Tue Jan 26, 2021 2:40 am, edited 1 time in total.
Reason: Updated code entry.

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