From Cpu to Gpu: Gpu-Based Electromagnetic Computing (Gpueco)

Tao, Yubo; Lin, Hai; Bao, Hu

doi:10.2528/pier07121302

Cited by 20 publications

(14 citation statements)

References 27 publications

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“…All these operations can be performed in parallel. The computation of the back-projection procedure at every cross-track dimension sample and every along-track dimension sample can also be performed in parallel [21,22]. Consequently, parallel implementation of the convolution back-projection imaging algorithm can be implemented in parallel on multi-core CPU platform via OpenMP or MPI (Message Passing Interface), or on GPU (Graphics processing units) platform via CUDA/OpenCL(Open Computing Language).…”

Section: Convolution Back-projection Algorithm Flowmentioning

confidence: 99%

“…In this paper, we offer a new point of view to DLSLA 3D SAR echo signal acquisition on the basis of projection-slice theorem. And, the CBP imaging algorithm which compensates the flying platform motion effect during the timedivided transmitting-receiving procedure and contains the advantage of parallel implementation [21,22], is introduced for DLSLA 3D SAR.…”

Section: Introductionmentioning

confidence: 99%

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Convolution Back-Projection Imaging Algorithm for Downward-Looking Sparse Linear Array Three Dimensional Synthetic Aperture Radar

Peng¹,

Tan²,

Wang³

et al. 2012

PIER

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Abstract-General side-looking synthetic aperture radar (SAR) cannot obtain scattering information about the observed scenes which are constrained by lay over and shading effects. Downward-looking sparse linear array three-dimensional SAR (DLSLA 3D SAR) can be placed on small and mobile platform, allows for the acquisition of full 3D microwave images and overcomes the restrictions of shading and lay over effects in side-looking SAR. DLSLA 3D SAR can be developed for various applications, such as city planning, environmental monitoring, Digital Elevation Model (DEM) generation, disaster relief, surveillance and reconnaissance, etc. In this paper, we give the imaging geometry and dechirp echo signal model of DLSLA 3D SAR. The sparse linear array is composed of multiple transmitting and receiving array elements placed sparsely along cross-track dimension. The radar works on time-divided transmitting-receiving mode. Particularly, the platform motion impact on the echo signal during the time-divided transmitting-receiving procedure is considered. Then we analyse the wave propagation, along-track and cross-track dimensional echo signal bandwidth before and after dechrip processing. In the following we extend the projection-slice theorem which is widely used in computerized axial tomography (CAT) to DLSLA 3D SAR imaging. In consideration of the flying platform motion compensation during timedivided transmitting-receiving procedure and parallel implementation on multi-core CPU or Graphics processing units (GPU) processor, the convolution back-projection (CBP) imaging algorithm is proposed for DLSLA 3D SAR image reconstruction. At last, the focusing capabilities

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Section: Convolution Back-projection Algorithm Flowmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Convolution Back-Projection Imaging Algorithm for Downward-Looking Sparse Linear Array Three Dimensional Synthetic Aperture Radar

Peng¹,

Tan²,

Wang³

et al. 2012

PIER

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“…In order to reduce the processing time, the computational electromagnetics community has been exploring the possibility to use GPUs for accelerating various numerical techniques including the Finite Difference Time Domain (FDTD) method [1][2][3][4], Alternating Direction Implicit (ADI) method [5], Transmission Line Modeling (TLM) method [6,7] and also for applications such as Radar Cross Section Prediction (RCS) [8][9][10]. Relative little attention has been so far given to the Finite Element Method (FEM).…”

Section: Introductionmentioning

confidence: 99%

A Memory Efficient and Fast Sparse Matrix Vector Product on a Gpu

Dziekonski¹,

Lamęcki²,

Mrozowski³

2011

PIER

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Abstract-This paper proposes a new sparse matrix storage format which allows an efficient implementation of a sparse matrix vector product on a Fermi Graphics Processing Unit (GPU). Unlike previous formats it has both low memory footprint and good throughput. The new format, which we call Sliced ELLR-T has been designed specifically for accelerating the iterative solution of a large sparse and complex-valued system of linear equations arising in computational electromagnetics. Numerical tests have shown that the performance of the new implementation reaches 69 GFLOPS in complex single precision arithmetic. Compared to the optimized six core Central Processing Unit (CPU) (Intel Xeon 5680) this performance implies a speedup by a factor of six. In terms of speed the new format is as fast as the best format published so far and at the same time it does not introduce redundant zero elements which have to be stored to ensure fast memory access. Compared to previously published solutions, significantly larger problems can be handled using low cost commodity GPUs with limited amount of on-board memory.

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“…Nevertheless, despite having a more efficient and simpler implementation using the FADI-FDTD, continuing efforts are still being made to further increase the overall efficiency. Of late, programmable graphics processing units (GPUs) with highly parallel processors have led to the interest in using GPUs for general purpose programming [9][10][11][12]. Such highly parallel processing feature of the GPU further motivates us into exploring the implementation of the FADI-FDTD.…”

Section: Introductionmentioning

confidence: 99%

Gpu-Accelerated Fundamental Adi-FDTD With Complex Frequency Shifted Convolutional Perfectly Matched Layer

Tay

Heh²,

Tan³

2010

PIER M

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Abstract-This paper presents the graphics processing unit (GPU) accelerated fundamental alternating-direction-implicit finite-difference time-domain (FADI-FDTD) with complex frequency shifted convolutional perfectly matched layer (CFS-CPML). The compact matrix form of the conventional ADI-FDTD method with CFS-CPML is formulated into FADI-FDTD with its right-hand-sides free of matrix operators, resulting in simpler and more concise update equations. Using Compute Unified Device Architecture (CUDA), the FADI-FDTD with CFS-CPML is further incorporated into the GPU to exploit data parallelism. Numerical results show that a much higher efficiency gain of up to 15 times can be achieved.

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From Cpu to Gpu: Gpu-Based Electromagnetic Computing (Gpueco)

Cited by 20 publications

References 27 publications

Convolution Back-Projection Imaging Algorithm for Downward-Looking Sparse Linear Array Three Dimensional Synthetic Aperture Radar

Convolution Back-Projection Imaging Algorithm for Downward-Looking Sparse Linear Array Three Dimensional Synthetic Aperture Radar

A Memory Efficient and Fast Sparse Matrix Vector Product on a Gpu

Gpu-Accelerated Fundamental Adi-FDTD With Complex Frequency Shifted Convolutional Perfectly Matched Layer

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