4.7 Article

Parallel Optimization for Large Scale Interferometric Synthetic Aperture Radar Data Processing

期刊

REMOTE SENSING
卷 15, 期 7, 页码 -

出版社

MDPI
DOI: 10.3390/rs15071850

关键词

InSAR; large-scale data processing; parallel optimization; task scheduling; STAR; supercomputing

向作者/读者索取更多资源

Interferometric synthetic aperture radar (InSAR) has rapidly developed and is considered an important method for monitoring surface deformation, benefiting from growing data quantities and improving data quality. Handling SAR big data poses challenges for algorithms and pipeline, particularly in large-scale SAR data processing. This paper designs parallel time-series InSAR processing models based on multi-thread technology to achieve high efficiency. The models focus on parallelizing critical algorithms and have shown a significant performance improvement. The paper also introduces a parallel optimization tool, STAR, which addresses the problem of low CPU utilization in the InSAR processing pipeline.
Interferometric synthetic aperture radar (InSAR) has developed rapidly over the past years and is considered as an important method for surface deformation monitoring, benefiting from growing data quantities and improving data quality. However, the handing of SAR big data poses significant challenges for related algorithms and pipeline, particularly in large-scale SAR data processing. In addition, InSAR algorithms are highly complex, and their task dependencies are intricate. There is a lack of efficient optimization models and task scheduling for InSAR pipeline. In this paper, we design parallel time-series InSAR processing models based on multi-thread technology for high efficiency in processing InSAR big data. These models concentrate on parallelizing critical algorithms that have high complexity, with a focus on deconstructing two computationally intensive algorithms through loop unrolling. Our parallel models have shown a significant improvement of 10-20 times in performance. We have also developed a parallel optimization tool, Simultaneous Task Automatic Runtime (STAR), which utilizes a data flow optimization strategy with thread pool technology to address the problem of low CPU utilization resulting from multiple modules and task dependencies in the InSAR processing pipeline. STAR provides a data-driven pipeline and enables concurrent execution of multiple tasks, with greater flexibility to keep the CPU busy and further improve CPU utilization through predetermined task flow. Additionally, a supercomputing-based system has been constructed for processing massive InSAR scientific big data and providing technical support for nationwide surface deformation measurement, in accordance with the framework of time series InSAR data processing. Using this system, we processed InSAR data with the volumes of 500 TB and 700 TB in 5 and 7 days, respectively. Finally we generated two maps of land surface deformation all over China.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据