Parallel implementation of w-projection wide-field imaging

w-Projection is a wide-field imaging technique that is widely used in radio synthesis arrays. Processing the wide-field big data generated by the future Square Kilometre Array (SKA) will require significant updates to current methods to significantly reduce the time consumed on data processing. Data...

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Bibliographic Details
Published in:arXiv.org 2019-05
Main Authors: Lao, Baoqiang, An, Tao, Ang, Yu, Zhang, Wenhui, Wang, Junyi, Guo, Quan, Guo, Shaoguang, Wu, Xiaocong
Format: Article
Language:English
Subjects:
Central processing units
Computation
Computer architecture
Convolution
CPUs
Data management
Data processing
Graphics processing units
Imaging
Message passing
Microprocessors
Nodes
Pipelining (computers)
Projection
Robustness (mathematics)
Run time (computers)
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Summary:w-Projection is a wide-field imaging technique that is widely used in radio synthesis arrays. Processing the wide-field big data generated by the future Square Kilometre Array (SKA) will require significant updates to current methods to significantly reduce the time consumed on data processing. Data loading and gridding are found to be two major time-consuming tasks in w-projection. In this paper, we investigate two parallel methods of accelerating w-projection processing on multiple nodes: the hybrid Message Passing Interface (MPI) and Open Multi-Processing (OpenMP) method based on multicore Central Processing Units (CPUs) and the hybrid MPI and Compute Unified Device Architecture (CUDA) method based on Graphics Processing Units (GPUs). Both methods are successfully employed and operated in various computational environments, confirming their robustness. The experimental results show that the total runtime of both MPI + OpenMP and MPI + CUDA methods is significantly shorter than that of single-thread processing. MPI + CUDA generally shows faster performance when running on multiple nodes than MPI + OpenMP, especially on large numbers of nodes. The single-precision GPU-based processing yields faster computation than the double-precision processing; while the single- and doubleprecision CPU-based processing shows consistent computational performance. The gridding time remarkably increases when the support size of the convolution kernel is larger than 8 and the image size is larger than 2,048 pixels. The present research offers useful guidance for developing SKA imaging pipelines.
ISSN:2331-8422
DOI:10.48550/arxiv.1905.03213