HLC2: a highly efficient cross-matching framework for large astronomical catalogues on heterogeneous computing environments Article Swipe
Yajie Zhang
,
Ce Yu
,
Chao Sun
,
Jian Xiao
,
Kun Li
,
Yifei Mu
,
Chenzhou Cui
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1093/mnras/stad067
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1093/mnras/stad067
Cross-matching operation, which is to find corresponding data for the same celestial object or region from multiple catalogues, is indispensable to astronomical data analysis and research. Due to the large amount of astronomical catalogues generated by the ongoing and next-generation large-scale sky surveys, the time complexity of the cross-matching is increasing dramatically. Heterogeneous computing environments provide a theoretical possibility to accelerate the cross-matching, but the performance advantages of heterogeneous computing resources have not been fully utilized. To meet the challenge of cross-matching for substantial increasing amount of astronomical observation data, this paper proposes Heterogeneous-computing-enabled Large Catalogue Cross-matcher (HLC2), a high-performance cross-matching framework based on spherical position deviation on CPU-GPU heterogeneous computing platforms. It supports scalable and flexible cross-matching and can be directly applied to the fusion of large astronomical catalogues from survey missions and astronomical data centres. A performance estimation model is proposed to locate the performance bottlenecks and guide the optimizations. A two-level partitioning strategy is designed to generate an optimized data placement according to the positions of celestial objects to increase throughput. To make HLC2 a more adaptive solution, the architecture-aware task splitting, thread parallelization, and concurrent scheduling strategies are designed and integrated. Moreover, a novel quad-direction strategy is proposed for the boundary problem to effectively balance performance and completeness. We have experimentally evaluated HLC2 using public released catalogue data. Experiments demonstrate that HLC2 scales well on different sizes of catalogues and the cross-matching speed is significantly improved compared to the state-of-the-art cross-matchers.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/mnras/stad067
- OA Status
- green
- Cited By
- 4
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4315434542
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4315434542Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1093/mnras/stad067Digital Object Identifier
- Title
-
HLC2: a highly efficient cross-matching framework for large astronomical catalogues on heterogeneous computing environmentsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-01-10Full publication date if available
- Authors
-
Yajie Zhang, Ce Yu, Chao Sun, Jian Xiao, Kun Li, Yifei Mu, Chenzhou CuiList of authors in order
- Landing page
-
https://doi.org/10.1093/mnras/stad067Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2301.07331Direct OA link when available
- Concepts
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Scalability, Computer science, Supercomputer, Matching (statistics), Scheduling (production processes), Computational science, Distributed computing, Parallel computing, Real-time computing, Database, Mathematics, Operations management, Statistics, EconomicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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4Total citation count in OpenAlex
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2024: 1, 2023: 3Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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