A Fast Synthetic Aperture Radar Raw Data Simulation Using Cloud Computing.
A Fast Synthetic Aperture Radar Raw Data Simulation Using Cloud Computing.
复制标题
使用云计算的快速合成孔径雷达原始数据模拟
DOI：
10.3390/s17010113
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发表时间：
2017-01-08
期刊：
影响因子：
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通讯作者：
Li R
中科院分区：
文献类型：
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作者：
Li Z;Su D;Zhu H;Li W;Zhang F;Li R
合成孔径雷达（SAR）原始数据仿真是雷达系统设计和成像算法研究的基础问题。测绘带和分辨率的增长导致数据量和模拟周期的显著增加，这可以被认为是一个综合的数据密集型和计算密集型问题。尽管一些高性能计算（HPC）方法已经证明了它们在加速模拟方面的潜力，但大量原始数据的输入/输出（I/O）瓶颈并没有得到缓解。本文提出了一种基于云计算的SAR原始数据仿真算法，该算法采用MapReduce模型加速原始数据计算，采用Hadoop分布式文件系统（HDFS）实现快速I/O访问。MapReduce模型是针对模拟原始数据的不规则并行积累而设计的，它大大降低了基于图形处理器（GPU）的模拟方法的并行效率。此外，从编程模型、HDFS配置和调度等方面提出了三种优化策略。实验结果表明，基于云计算的算法在8节点云环境下实现了4倍于基线串行方法的加速比，每种优化策略都能提高约20%。实验结果表明，该算法能够有效解决SAR原始数据模拟中的计算密集型和数据密集型问题，并且易于扩展到大规模计算中，从而获得更高的加速性能。
Synthetic Aperture Radar (SAR) raw data simulation is a fundamental problem in radar system design and imaging algorithm research. The growth of surveying swath and resolution results in a significant increase in data volume and simulation period, which can be considered to be a comprehensive data intensive and computing intensive issue. Although several high performance computing (HPC) methods have demonstrated their potential for accelerating simulation, the input/output (I/O) bottleneck of huge raw data has not been eased. In this paper, we propose a cloud computing based SAR raw data simulation algorithm, which employs the MapReduce model to accelerate the raw data computing and the Hadoop distributed file system (HDFS) for fast I/O access. The MapReduce model is designed for the irregular parallel accumulation of raw data simulation, which greatly reduces the parallel efficiency of graphics processing unit (GPU) based simulation methods. In addition, three kinds of optimization strategies are put forward from the aspects of programming model, HDFS configuration and scheduling. The experimental results show that the cloud computing based algorithm achieves 4× speedup over the baseline serial approach in an 8-node cloud environment, and each optimization strategy can improve about 20%. This work proves that the proposed cloud algorithm is capable of solving the computing intensive and data intensive issues in SAR raw data simulation, and is easily extended to large scale computing to achieve higher acceleration.