CUDA-Based Parallelization of Power Iteration Clustering for Large Datasets

Silva, Gustavo Rodrigues Lacerda; Medeiros, Rafael Ribeiro de; Jaimes, Brayan René Acevedo; Takahashi, Carla Caldeira; Vieira, D.A.G.; Braga, Antônio de Pádua

doi:10.1109/access.2017.2765380

Cited by 6 publications

(1 citation statement)

References 17 publications

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“…In addition, the proposed PWNet has some distribution structures. Thus, we will extend the proposed model to a distribution version by using the techniques of distributed processing [53] to further reduce the running time while maintaining the quality of the results.…”

Section: Discussionmentioning

confidence: 99%

PWNet: An Adaptive Weight Network for the Fusion of Panchromatic and Multispectral Images

et al. 2020

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Pansharpening is a typical image fusion problem, which aims to produce a high resolution multispectral (HRMS) image by integrating a high spatial resolution panchromatic (PAN) image with a low spatial resolution multispectral (MS) image. Prior arts have used either component substitution (CS)-based methods or multiresolution analysis (MRA)-based methods for this propose. Although they are simple and easy to implement, they usually suffer from spatial or spectral distortions and could not fully exploit the spatial and/or spectral information existed in PAN and MS images. By considering their complementary performances and with the goal of combining their advantages, we propose a pansharpening weight network (PWNet) to adaptively average the fusion results obtained by different methods. The proposed PWNet works by learning adaptive weight maps for different CS-based and MRA-based methods through an end-to-end trainable neural network (NN). As a result, the proposed PWN inherits the data adaptability or flexibility of NN, while maintaining the advantages of traditional methods. Extensive experiments on data sets acquired by three different kinds of satellites demonstrate the superiority of the proposed PWNet and its competitiveness with the state-of-the-art methods.

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Section: Discussionmentioning

confidence: 99%

PWNet: An Adaptive Weight Network for the Fusion of Panchromatic and Multispectral Images

et al. 2020

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Strength Check of Aircraft Parts Based on Multi-GPU Clusters for Fast Calculation of Sparse Linear Equations

Zhang

Hu²

2020

IEEE Access

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In order to improve the cost-effectiveness ratio, the next-generation vehicle needs to meet the requirements of reuse, while adopting a lighter structural weight, so it is necessary to realize the strength calculation and condition monitoring of key components in the digital twin. Most of the current monitoring methods are based on the characteristics of various data acquisition systems, but they need the support of a large number of flight data. The disadvantages of the above strategy can be avoided by reducing the structure of aircraft components to a finite element model and quickly checking the key components in the health management system. In order to solve the problem of fast calculation of the finite element model of the key components of the aircraft, a parallel algorithm and framework of large-scale sparse matrix preprocessing conjugate gradient method based on CUDA(Compute Unified Device Architecture) technology is proposed in the multi GPU(Graphics Processing Unit) workstation cluster environment. Once the sparse matrix is too large to be processed in a single workstation, this paper discusses how to realize the optimized data segmentation in the distributed multi-GPU computing environment. For the problem of iterative solution of matrix preprocessing, two preprocessing strategies of matrix bandwidth reduction parallelization and incomplete Cholesky decomposition are proposed, and asynchronous task concurrency and load balancing strategies are designed on the architecture. The calculation of some examples in the standard sparse matrix database shows that the algorithm and architecture proposed in this paper have the ability to solve large-scale sparse matrix quickly and efficiently, and can complete the fast strength verification of vehicle components.

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CUDA Optimization Method for Activation Function in Convolution Operation

Feng

Zheng

2019

2019 IEEE Intl Conf on Parallel &Amp; Distributed Processing With Applications, Big Data &Amp; Cloud Computing, Sustainable Com

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CUDA-Based Parallelization of Power Iteration Clustering for Large Datasets

Cited by 6 publications

References 17 publications

PWNet: An Adaptive Weight Network for the Fusion of Panchromatic and Multispectral Images

PWNet: An Adaptive Weight Network for the Fusion of Panchromatic and Multispectral Images

Strength Check of Aircraft Parts Based on Multi-GPU Clusters for Fast Calculation of Sparse Linear Equations

CUDA Optimization Method for Activation Function in Convolution Operation

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