Accelerating aerial image simulation using improved CPU/GPU collaborative computing

Zhang, Fan; Hu, Chen; Wu, Pei; Wong, D.F.

doi:10.1016/j.compeleceng.2015.05.018

Cited by 4 publications

(2 citation statements)

References 30 publications

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“…The combination of AVX and multi-core parallel can make the computing capability of CPU comparable with GPU. Our previous works [ 31 , 32 , 33 ] have a preliminary discussion on this, and proved that CPU had competitive computing power. In terms of that, CPU no longer just takes on some auxiliary tasks of SAR imaging processing, but can really become more involved in the computing works of imaging processing.…”

Section: Introductionmentioning

confidence: 90%

Accelerating Spaceborne SAR Imaging Using Multiple CPU/GPU Deep Collaborative Computing

Zhang

et al. 2016

Sensors

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With the development of synthetic aperture radar (SAR) technologies in recent years, the huge amount of remote sensing data brings challenges for real-time imaging processing. Therefore, high performance computing (HPC) methods have been presented to accelerate SAR imaging, especially the GPU based methods. In the classical GPU based imaging algorithm, GPU is employed to accelerate image processing by massive parallel computing, and CPU is only used to perform the auxiliary work such as data input/output (IO). However, the computing capability of CPU is ignored and underestimated. In this work, a new deep collaborative SAR imaging method based on multiple CPU/GPU is proposed to achieve real-time SAR imaging. Through the proposed tasks partitioning and scheduling strategy, the whole image can be generated with deep collaborative multiple CPU/GPU computing. In the part of CPU parallel imaging, the advanced vector extension (AVX) method is firstly introduced into the multi-core CPU parallel method for higher efficiency. As for the GPU parallel imaging, not only the bottlenecks of memory limitation and frequent data transferring are broken, but also kinds of optimized strategies are applied, such as streaming, parallel pipeline and so on. Experimental results demonstrate that the deep CPU/GPU collaborative imaging method enhances the efficiency of SAR imaging on single-core CPU by 270 times and realizes the real-time imaging in that the imaging rate outperforms the raw data generation rate.

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Section: Introductionmentioning

confidence: 90%

Accelerating Spaceborne SAR Imaging Using Multiple CPU/GPU Deep Collaborative Computing

Zhang

et al. 2016

Sensors

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“…In the heterogeneous soil model, OpenMP parallel optimization is used for multi-core parallelism implementation [27]. In our previous work, various parallel mechanisms have been introduced to accelerate the SAR raw data simulation, including clouding computing, GPU parallel, CPU parallel, and hybrid CPU/GPU parallel [28][29][30][31][32][33][34][35]. As far as the inversion algorithms are concerned, the time cost is only minute-level.…”

Section: Introductionmentioning

confidence: 99%

GPU-Based Soil Parameter Parallel Inversion for PolSAR Data

Yin¹,

You²,

Zhang³

et al. 2020

Remote Sensing

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With the development of polarimetric synthetic aperture radar (PolSAR), quantitative parameter inversion has been seen great progress, especially in the field of soil parameter inversion, which has achieved good results for applications. However, PolSAR data is also often many terabytes large. This huge amount of data also directly affects the efficiency of the inversion. Therefore, the efficiency of soil moisture and roughness inversion has become a problem in the application of this PolSAR technique. A parallel realization based on a graphics processing unit (GPU) for multiple inversion models of PolSAR data is proposed in this paper. This method utilizes the high-performance parallel computing capability of a GPU to optimize the realization of the surface inversion models for polarimetric SAR data. Three classical forward scattering models and their corresponding inversion algorithms are analyzed. They are different in terms of polarimetric data requirements, application situation, as well as inversion performance. Specifically, the inversion process of PolSAR data is mainly improved by the use of the high concurrent threads of GPU. According to the inversion process, various optimization strategies are applied, such as the parallel task allocation, and optimizations of instruction level, data storage, data transmission between CPU and GPU. The advantages of a GPU in processing computationally-intensive data are shown in the data experiments, where the efficiency of soil roughness and moisture inversion is increased by one or two orders of magnitude.

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A survey on techniques for cooperative CPU-GPU computing

Raju¹,

Chiplunkar²

2018

Sustainable Computing: Informatics and Systems

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Accelerating aerial image simulation using improved CPU/GPU collaborative computing

Cited by 4 publications

References 30 publications

Accelerating Spaceborne SAR Imaging Using Multiple CPU/GPU Deep Collaborative Computing

Accelerating Spaceborne SAR Imaging Using Multiple CPU/GPU Deep Collaborative Computing

GPU-Based Soil Parameter Parallel Inversion for PolSAR Data

A survey on techniques for cooperative CPU-GPU computing

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