Romano Diego, Lapegna Marco
Institute for High Performance Computing and Networking (ICAR), CNR, 80131 Naples, Italy.
Department of Mathematics and Applications, University of Naples Federico II, 80126 Naples, Italy.
Sensors (Basel). 2021 Sep 2;21(17):5916. doi: 10.3390/s21175916.
Image Coregistration for InSAR processing is a time-consuming procedure that is usually processed in batch mode. With the availability of low-energy GPU accelerators, processing at the edge is now a promising perspective. Starting from the individuation of the most computationally intensive kernels from existing algorithms, we decomposed the cross-correlation problem from a multilevel point of view, intending to design and implement an efficient GPU-parallel algorithm for multiple settings, including the edge computing one. We analyzed the accuracy and performance of the proposed algorithm-also considering power efficiency-and its applicability to the identified settings. Results show that a significant speedup of InSAR processing is possible by exploiting GPU computing in different scenarios with no loss of accuracy, also enabling onboard processing using SoC hardware.
用于InSAR处理的图像配准是一个耗时的过程，通常以批处理模式进行。随着低能耗GPU加速器的出现，在边缘进行处理现在是一个很有前景的方向。从现有算法中识别出计算量最大的内核开始，我们从多层次的角度分解了互相关问题，旨在为包括边缘计算在内的多种设置设计并实现一种高效的GPU并行算法。我们分析了所提算法的准确性、性能——同时考虑功率效率——及其对所识别设置的适用性。结果表明，通过在不同场景中利用GPU计算，可以显著加快InSAR处理速度，且不会损失准确性，还能使用SoC硬件进行机载处理。
