- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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A GPU IMPLEMENTATION OF THE INVERSE FAST MULTIPOLE METHOD FOR MULTI-BISTATIC IMAGING APPLICATIONS
摘要： This paper describes a parallel implementation of the Inverse Fast Multipole Method (IFMM) for multi-bistatic imaging configurations. NVIDIA's Compute Unified Device Architecture (CUDA) is used to parallelize and accelerate the imaging algorithm in a Graphics Processing Unit (GPU). The algorithm is validated with synthetic data generated by the Modified Equivalent Current Approximation (MECA) method and experimental data collected by a Frequency-Modulated Continuous Wave (FMCW) radar system operating in the 70–77 GHz frequency band. The presented results show that the IFMM implementation using the CUDA platform is effective at significantly reducing the algorithm computational time, providing a 300X speedup when compared to the single core OpenMP version of the algorithm.
关键词： CUDA,GPU,IFMM,MECA method,multi-bistatic imaging,Inverse Fast Multipole Method,FMCW radar
更新于2025-09-09 09:28:46
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GPU ACCELERATED DISCONTINUOUS GALERKIN TIME DOMAIN ALGORITHM FOR ELECTROMAGNETIC PROBLEMS OF ELECTRICALLY LARGE OBJECTS
摘要： In this paper, an e?cient time domain simulation algorithm is proposed to analyze the electromagnetic scattering and radiation problems. The algorithm is based on discontinuous Galerkin time domain (DGTD) method and parallelization acceleration technique using the graphics processing units (GPU), which o?ers the capability for accelerating the computational electromagnetics analyses. The bottlenecks using the GPU DGTD acceleration for electromagnetic analyses are investigated, and potential strategies to alleviate the bottlenecks are proposed. We ?rst discuss the e?cient parallelization strategies handling the local-element di?erentiation, surface integrals, RK time-integration assembly on the GPU platforms, and then, we explore how to implement the DGTD method on the Compute Uni?ed Device Architecture (CUDA). The accuracy and performance of the DGTD method are analyzed through illustrated benchmarks. We demonstrate that the DGTD method is better suitable for GPUs to achieve signi?cant speedup improvement over modern multi-core CPUs.
关键词： GPU,electromagnetic scattering,DGTD,parallelization acceleration,CUDA
更新于2025-09-04 15:30:14
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[IEEE 2018 15th International Multi-Conference on Systems, Signals & Devices (SSD) - Yassmine Hammamet, Tunisia (2018.3.19-2018.3.22)] 2018 15th International Multi-Conference on Systems, Signals & Devices (SSD) - Real Time Stereo Matching Using Two Step Zero-Mean SAD and Dynamic Programing
摘要： Dense depth map extraction is a dynamic research field in a computer vision that tries to recover three-dimensional information from a stereo image pair. A large variety of algorithms has been developed. The local methods based on block matching that are prevalent due to the linear computational complexity and easy implementation. This local cost is used on global methods as graph cut and dynamic programming in order to reduce sensitivity to local to occlusion and uniform texture. This paper proposes a new method for matching images based on a two-stage of block matching as local cost function and dynamic programming as energy optimization approach. In our work introduce the two stage of the zero-mean sum of absolute differences (ZSAD) combined with dynamic programming: the smoothness and ordering constraints are used to optimize correspondences. Stereo matching accuracy and runtime are the fundamental metrics to evaluate the stereo matching methods. The real-time has become a reality through the complexity reduction of the calculation and the use of parallel high-performance graphics hardware. In this paper we evaluate the developed method on using Middlebury stereo benchmark and, we propose a GPU CUDA implementation in order to accelerate our algorithm and reach the real time.
关键词： dynamic programming,stereo matching,GPU,CUDA implementation,cost function,block matching
更新于2025-09-04 15:30:14
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[IEEE 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE) - Nara, Japan (2018.10.9-2018.10.12)] 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE) - CUDA-Based Computation for Visual Odometry
摘要： An enhanced visual odometry (VO) system is proposed to improve the accuracy of pose estimation based on a corrected model, and the matching algorithm is implemented on graphical processing units (GPUs) so that the computation can be accelerated in parallel and in real-time using the compute unified device architecture (CUDA) programming model. To evaluate the performance of the proposed approach, an ASUS Xtion 3D camera, laptop, and NVIDIA TX2 are employed to conduct extensive experiments. The experimental results show that compared with the traditional VO algorithm, the proposed approach gives better results over the traditional VO algorithm.
关键词： CUDA,GPU,Visual odometry
更新于2025-09-04 15:30:14