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Parallel Computation of Doppler Spectral from Dynamic Sea Surfaces at Microwave Bands

机译:微波频带动态海面多普勒光谱的并行计算

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The Doppler spectrum of the electromagnetic (EM) scattering field from the two-dimensional dynamic sea surface is calculated based on the composite scattering model. The two-dimensional dynamic sea surfaces are generally simulated as a superposition of large-scale gravity waves and small-scale capillary ripples. On this basis, the Doppler spectrum of the EM scattering field from the two-dimensional dynamic sea surface can be calculated based on the composite scattering model, which takes both the quasi-specular scattering and Bragg scattering mechanism into account. However, due to the high resolution and real-time dynamic complexity of the dynamic sea surfaces, the calculation of the Doppler spectrum will be computationally expensive and very time-consuming. In this paper, a GPU-based algorithm of Doppler spectrum was proposed by utilizing the Tesla K80 GPUs with diverse CUDA optimization techniques. The GPU-based Doppler spectrum implementation includes five optimization strategies: first, the temporary arrays are utilized to reduce the repeat float-points operations in the loop; then the device memory was effectively exploited to reduce the data transfer time between the CPU and GPU; the fast math compiler option was also utilized to further improve the computation performance of the Doppler spectrum calculation; finally the data transfer time between the device and host memories can be effectively hide by using the asynchronous data transfer (ADT). Compared to the CPU serial program executed on Intel(R) Core(TM) i5-3450 CPU, the GPU-based Doppler spectrum implementation can achieve a significant speedup of1200x.
机译:基于复合散射模型计算了来自二维动态海面的电磁(EM)散射场的多普勒频谱。二维动态海面通常被模拟为大规模重力波和小尺寸毛细血管涟漪的叠加。在此基础上,可以基于复合散射模型计算来自二维动态海面的EM散射场的多普勒频谱,这既考虑了准镜面散射和布拉格散射机制。然而,由于动态海面的高分辨率和实时动态复杂性,多普勒频谱的计算将计算昂贵并且非常耗时。本文通过利用具有多种CUDA优化技术的TESLA K80 GPU,提出了一种基于GPU的多普勒谱算法。基于GPU的多普勒频谱实现包括五个优化策略:首先,利用临时阵列来减少环路中的重复浮点数操作;然后有效地利用设备存储器以减少CPU和GPU之间的数据传输时间;还利用快速数学编译器选项来进一步提高多普勒频谱计算的计算性能;最后,通过使用异步数据传输(ADT),设备和主机存储器之间的数据传输时间可以有效地隐藏。与Intel(R)核心(TM)I5-3450 CPU上执行的CPU串行程序相比,基于GPU的多普勒频谱实现可以实现1200x的显着加速。

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