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Parallel GLRT-Based SAR Tomographic Processing on Distributed Multi-GPU Platforms
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DOI：10.1109/JSTARS.2026.3670717.png)
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Abstract
En 中文
This article addresses the development of a multilevel parallel algorithm for detecting single scatterers in SAR tomography, targeted at multinode multi-GPU distributed computational architectures. Taking into account the computational structure of the canonical processing scheme based on the generalized likelihood ratio test considered in this article, an appropriate problem decomposition is adopted to devise and formulate an efficient parallel algorithm tailored for heterogeneous high-performance computing platforms, thereby enabling a hierarchical exploitation of different levels of parallelism, including both distributed and shared memory models. Experimental evaluations conducted on a multinode, GPU-enabled HPC cluster, using a high-resolution SAR dataset, exhibit substantial acceleration and scalability. These results quantitatively confirm the effectiveness of the proposed multilevel parallel framework and position the developed prototype as a promising, scalable solution for large-scale SAR tomographic processing applications. This is particularly relevant in light of upcoming SAR missions, which are expected to generate unprecedented volumes of data, thus demanding scalable and high-performance processing solutions.
Keywords:
Synthetic aperture radar
Tomography
Radar polarimetry
Parallel algorithms
Earth
Vectors
Three-dimensional displays
Monitoring
High performance computing
Surface treatment
Electromagnetic scattering
graphical processing unit (GPU)
high performance computing (HPC)
parallel algorithms
synthetic aperture radar (SAR)
tomography
Journal
IF:
5.3
Papers:
1.3K
Citations:
3.0W
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