Distributed Data-Processing Pipeline for Mingantu Ultrawide Spectral Radioheliograph Article Swipe
Feng Wang
,
Ying Mei
,
Hui Deng
,
Weixin Wang
,
Cui-Yun Liu
,
D-H Liu
,
Shoulin Wei
,
Wei Dai
,
Bo Liang
,
Yongbo Liu
,
Xiaolin Zhang
,
Kailun Ji
·
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.1086/680852
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.1086/680852
The Mingantu Ultrawide Spectral Radioheliograph (MUSER) is a synthetic-aperture radio interferometer built in Ming'antu, Inner Mongolia, China. As a solar-dedicated interferometric array, the MUSER can produce high-quality radio images in a frequency range of 400 MHz–15 GHz with high temporal, spatial, and spectral resolution. Implementing of the data processing system for the MUSER is a major challenge to performing high-cadence imaging in wideband and obtaining more than two orders of higher multiple frequencies. There is an urgent need to build a pipeline for processing the massive amount of MUSER data generated each day. In this article, we present a high-performance distributed data processing pipeline (DDPP) built on the OpenCluster infrastructure for processing MUSER observational data, including data storage, preprocessing, image reconstruction, deconvolution, archiving, and real-time monitoring. We comprehensively elaborate the system architecture of the pipeline and the implementation of each subsystem. The DDPP is automatic, robust, scalable, and manageable. The processing performance under a parallel CPU/GPU hybrid system meets the requirements of MUSER data processing.
Related Topics
Concepts
Computer science
Pipeline (software)
Data processing
Hyperspectral imaging
Image processing
Interferometry
Deconvolution
Remote sensing
Data processing system
Data pre-processing
Preprocessor
Real-time computing
Computer hardware
Computer vision
Artificial intelligence
Database
Optics
Geology
Algorithm
Physics
Image (mathematics)
Programming language
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1086/680852
- https://iopscience.iop.org/article/10.1086/680852/pdf
- OA Status
- bronze
- Cited By
- 12
- References
- 14
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2319921835
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2319921835Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1086/680852Digital Object Identifier
- Title
-
Distributed Data-Processing Pipeline for Mingantu Ultrawide Spectral RadioheliographWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2015Year of publication
- Publication date
-
2015-03-19Full publication date if available
- Authors
-
Feng Wang, Ying Mei, Hui Deng, Weixin Wang, Cui-Yun Liu, D-H Liu, Shoulin Wei, Wei Dai, Bo Liang, Yongbo Liu, Xiaolin Zhang, Kailun JiList of authors in order
- Landing page
-
https://doi.org/10.1086/680852Publisher landing page
- PDF URL
-
https://iopscience.iop.org/article/10.1086/680852/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
-
https://iopscience.iop.org/article/10.1086/680852/pdfDirect OA link when available
- Concepts
-
Computer science, Pipeline (software), Data processing, Hyperspectral imaging, Image processing, Interferometry, Deconvolution, Remote sensing, Data processing system, Data pre-processing, Preprocessor, Real-time computing, Computer hardware, Computer vision, Artificial intelligence, Database, Optics, Geology, Algorithm, Physics, Image (mathematics), Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
12Total citation count in OpenAlex
- Citations by year (recent)
-
2022: 1, 2021: 1, 2020: 1, 2019: 1, 2017: 5Per-year citation counts (last 5 years)
- References (count)
-
14Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.data, | 114 |
| abstract_inverted_index.image | 119 |
| abstract_inverted_index.major | 55 |
| abstract_inverted_index.meets | 158 |
| abstract_inverted_index.radio | 9, 27 |
| abstract_inverted_index.range | 32 |
| abstract_inverted_index.under | 152 |
| abstract_inverted_index.(DDPP) | 104 |
| abstract_inverted_index.China. | 16 |
| abstract_inverted_index.amount | 86 |
| abstract_inverted_index.array, | 21 |
| abstract_inverted_index.higher | 70 |
| abstract_inverted_index.hybrid | 156 |
| abstract_inverted_index.images | 28 |
| abstract_inverted_index.orders | 68 |
| abstract_inverted_index.system | 49, 130, 157 |
| abstract_inverted_index.urgent | 76 |
| abstract_inverted_index.(MUSER) | 5 |
| abstract_inverted_index.CPU/GPU | 155 |
| abstract_inverted_index.imaging | 60 |
| abstract_inverted_index.massive | 85 |
| abstract_inverted_index.present | 97 |
| abstract_inverted_index.produce | 25 |
| abstract_inverted_index.robust, | 145 |
| abstract_inverted_index.MHz–15 | 35 |
| abstract_inverted_index.Mingantu | 1 |
| abstract_inverted_index.Spectral | 3 |
| abstract_inverted_index.article, | 95 |
| abstract_inverted_index.multiple | 71 |
| abstract_inverted_index.parallel | 154 |
| abstract_inverted_index.pipeline | 81, 103, 134 |
| abstract_inverted_index.spatial, | 40 |
| abstract_inverted_index.spectral | 42 |
| abstract_inverted_index.storage, | 117 |
| abstract_inverted_index.wideband | 62 |
| abstract_inverted_index.Mongolia, | 15 |
| abstract_inverted_index.Ultrawide | 2 |
| abstract_inverted_index.challenge | 56 |
| abstract_inverted_index.elaborate | 128 |
| abstract_inverted_index.frequency | 31 |
| abstract_inverted_index.generated | 90 |
| abstract_inverted_index.including | 115 |
| abstract_inverted_index.obtaining | 64 |
| abstract_inverted_index.real-time | 124 |
| abstract_inverted_index.scalable, | 146 |
| abstract_inverted_index.temporal, | 39 |
| abstract_inverted_index.Ming'antu, | 13 |
| abstract_inverted_index.archiving, | 122 |
| abstract_inverted_index.automatic, | 144 |
| abstract_inverted_index.performing | 58 |
| abstract_inverted_index.processing | 48, 83, 102, 111, 150 |
| abstract_inverted_index.subsystem. | 140 |
| abstract_inverted_index.OpenCluster | 108 |
| abstract_inverted_index.distributed | 100 |
| abstract_inverted_index.manageable. | 148 |
| abstract_inverted_index.monitoring. | 125 |
| abstract_inverted_index.performance | 151 |
| abstract_inverted_index.processing. | 164 |
| abstract_inverted_index.resolution. | 43 |
| abstract_inverted_index.Implementing | 44 |
| abstract_inverted_index.architecture | 131 |
| abstract_inverted_index.frequencies. | 72 |
| abstract_inverted_index.high-cadence | 59 |
| abstract_inverted_index.high-quality | 26 |
| abstract_inverted_index.requirements | 160 |
| abstract_inverted_index.observational | 113 |
| abstract_inverted_index.deconvolution, | 121 |
| abstract_inverted_index.implementation | 137 |
| abstract_inverted_index.infrastructure | 109 |
| abstract_inverted_index.interferometer | 10 |
| abstract_inverted_index.preprocessing, | 118 |
| abstract_inverted_index.Radioheliograph | 4 |
| abstract_inverted_index.comprehensively | 127 |
| abstract_inverted_index.interferometric | 20 |
| abstract_inverted_index.reconstruction, | 120 |
| abstract_inverted_index.solar-dedicated | 19 |
| abstract_inverted_index.high-performance | 99 |
| abstract_inverted_index.synthetic-aperture | 8 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 12 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.6399999856948853 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.85074471 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |