Improved Parallel Processing Function for High-Performance Large-Scale Astronomical Cross-Matching |
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| Authors: | ZHAO Qing SUN Jizhou YU Ce XIAO Jian CUI Chenzhou ZHANG Xiao |
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| Institution: | 1. School of Computer Science and Technology, Tianjin University, Tianjin 300072, China
2. National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012, China |
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| Abstract: | Astronomical cross-matching is a basic method for aggregating the observational data of different wavelengths. By data aggregation,
the properties of astronomical objects can be understood comprehensively. Aiming at decreasing the time consumed on I/O operations,
several improved methods are introduced, including a processing flow based on the boundary growing model, which can reduce
the database query operations; a concept of the biggest growing block and its determination which can improve the performance
of task partition and resolve data-sparse problem; and a fast bitwise algorithm to compute the index numbers of the neighboring
blocks, which is a significant efficiency guarantee. Experiments show that the methods can effectively speed up cross-matching
on both sparse datasets and high-density datasets. |
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| Keywords: | astronomical cross-matching boundary growing model HEALPix task partition data-sparse problem |
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