Evolutionary algorithms (EAs) are naturally prone to parallel processing. However, when they are applied to data mining, the fitness calculations start to dominate and the typical population-based decomposition limits...
详细信息
Evolutionary algorithms (EAs) are naturally prone to parallel processing. However, when they are applied to data mining, the fitness calculations start to dominate and the typical population-based decomposition limits the parallel efficiency. When dealing with large-scale data, the scalable solution may become a real challenge. In this article, we propose a gpu-based parallelization of evolutionary induction of model trees. Such trees are a special case of decision tree (DT) that is designed to solve regression problems. The evolutionary approach allows not only a robust prediction but also to preserve the simplicity of DTs. However, the global approach is much more computationally demanding than state-of-the-art greedy inducers, and thus hard to apply to large-scale data mining directly. A parallelized induction of model trees (with univariate tests in the internal nodes and multiple linear regression models in the leaves) requires a carefully designed decomposition strategy. Six gpusupported procedures are designed to successively: redistribute, sort and rearrange dataset samples, next, calculate models and fitness, and finally gather the results. Experimental validation is performed on real-life and artificial datasets, using various (low- and high-end) gpu accelerators. Results show that the gpu-supported solution enables time-efficient global induction of model trees on large-scale data, which until now was reserved for greedy methods. The obtained speedup is very satisfactory (even up to hundreds of times). The solution is scalable for datasets of different sizes and dimensions. (c) 2022 Elsevier B.V. All rights reserved.
Passive bistatic radar is a novel radar technology that passively detects targets without actively emitting signals. Since passive bistatic radar entails larger data volumes and computations compared to traditional ac...
详细信息
Passive bistatic radar is a novel radar technology that passively detects targets without actively emitting signals. Since passive bistatic radar entails larger data volumes and computations compared to traditional active radiation radar, the development of hardware and software platforms capable of efficiently processing signals from passive bistatic radar has emerged as a research focus in this field. This research investigates the signal processing flow of passive bistatic radar based on its characteristics and devises a parallel signal processing scheme under graphic processing unit (gpu) architecture for computation-intensive tasks. The proposed scheme utilizes high-computing-power gpu as the hardware platform and compute unified device architecture (CUDA) as the software platform and optimizes the extensive cancellation algorithm batches (ECA-B), range Doppler and constant false alarm detection algorithms. The detection and tracking of a single target are realized on the passive bistatic radar dataset of natural scenarios, and experiments show that the design of this algorithm can achieve a maximum acceleration ratio of 113.13. Comparative experiments conducted with varying data volumes revealed that this method significantly enhances the signal processing rate for passive bistatic radar.
For the complex transient thermo-hydraulic restart process of a long-buried hot oil pipeline, it takes a long time to solve the restart simulation model, which cannot meet the requirements of rapid restart prediction ...
详细信息
For the complex transient thermo-hydraulic restart process of a long-buried hot oil pipeline, it takes a long time to solve the restart simulation model, which cannot meet the requirements of rapid restart prediction and assessment. The modern gpu is specialized for compute-intensive and massively parallel computation, and it has great potential to accelerate transient thermo-hydraulic restart simulation. According to the computational characteristics of restart model solutions, four types of gpuparallel strategies, from the mapping of grids to the convergence judgment of the discrete equation solution, are combined to accelerate the whole process of a transient thermo-hydraulic restart simulation. The results indicate that the total computation time of the tran-sient thermo-hydraulic simulation is reduced from 185.4 min to 8.8 min, corresponding to an acceleration rate of 21.1, which exhibits a good acceleration effect for a single gpu and is beneficial to the rapid assessment of restart schemes. Based on the gpu-accelerated numerical results, the complex transient thermo-hydraulic restart pro-cess with the changing soil temperature field, the non-Newtonian thixotropic behavior of crude oil and flow pattern evolution, is investigated. The analysis reveals that the dominant influencing factors of the restart process vary with the restart time, and the flow pattern evolution plays an important role in the thermo-hydraulic characteristics of the restart process.
This paper introduces a simulation calculation model of ground radiation field. The simulation calculation model includes the calculation of incident radiation field and the calculation of outgoing radiation field. Th...
详细信息
This paper introduces a simulation calculation model of ground radiation field. The simulation calculation model includes the calculation of incident radiation field and the calculation of outgoing radiation field. The calculation of incident radiation field is mainly divided into direct solar radiation, diffuse sky radiation and background to target reflected radiation. The parallelcomputing technology of gpu is introduced, and the process of simulation calculation is implemented based on CUDA framework. The ground radiation field simulation is carried out on several different 3D scenes, and the parallelcomputing time is compared with the computing time before parallelcomputing. Through the comparison and analysis, the use of parallelcomputing can significantly improve the time of simulation calculation. At the same time, with the increase of the number of triangular facets in the 3D scene, the efficiency of acceleration will continue to improve, but the extent of the upgrade will be affected by the conditions of gpu hardware equipment.
Nowadays, passwords have become closely associated with our daily activities. However, the development of technology also increases the risk of password leak. For example, the graphics processing unit (gpu)-parallel-c...
详细信息
Nowadays, passwords have become closely associated with our daily activities. However, the development of technology also increases the risk of password leak. For example, the graphics processing unit (gpu)-parallel-computing-based brute force attack and birthday attack algorithms have greatly reduced password security;in addition, passwords are usually transmitted through wired or wireless communication media and thus are vulnerable to attack and easily exposed to illegal users. In this study, we propose a biometric authentication method to identify and block illegal users, even if the entire password is exposed. Our method simultaneously records scan codes and the keystroke sequence of passwords;furthermore, by deep learning of convolutional neural networks (CNNs), it can effectively distinguish legal users from illegal users. We first compare recognition rates between the CNN and the neural network (NN) and prove that the CNN is the better choice. The experimental results show that the proposed CNN model can block all illegal users even if the password is known by them. By using equal amounts of password data from legal and illegal users, the average login failure rate of legal users is 6%, and they can always enter passwords again to be admitted. Finally, by gpu parallel computing, we further accelerate the system performance by 4.45 times.
With the further development of the electromagnetic exploration technologies, the forward and inversion modeling of geophysical in three-dimensional numerical simulation fields is confronted with huge challenges. Duri...
详细信息
With the further development of the electromagnetic exploration technologies, the forward and inversion modeling of geophysical in three-dimensional numerical simulation fields is confronted with huge challenges. During the process of solving the partial differential equations, the methods of finite difference, finite element and volume element methods are usually adopted. For the complex topographic condition and geological structure, the conditions of the matrix formed finally will be very poor, seriously affecting the iterative and convergence rate in equation solution. In this paper, the algebraic multigrid preconditioned methods and conjugate gradient solution process are adopted to conduct parallel processing in combination of graphics processing unit (gpu), and the efficiency of DCs threedimensional forward modeling will be effectively improved.
作者：
YI YANGWENJIE CHENSchool of Automation
Beijing Institute of Technology Beijing 100081 Beijing Key Laboratory of Automatic Control System (Beijing Institute of Technology) Beijing China
Foreground detection in dynamic background has become a hot topic in video surveillance in recent years. In this paper we propose a new foreground detection approach based on gpu in dynamic background. With the pr...
详细信息
Foreground detection in dynamic background has become a hot topic in video surveillance in recent years. In this paper we propose a new foreground detection approach based on gpu in dynamic background. With the proposed method, SIFT features are first extracted from two adjacent frames in video sequences, which can be utilized to compute the parameters of affine transform model and to solve global motion compensation. Then improving background subtraction approach with dynamic background updating module is adopted to detect foreground objects. gpu method is used to improve application performance. Combined with CUDA, three mainly algorithm modules, which are so called Global Motion Compensation Module, Updating Background Module and Foreground Detection Module, are improved. In this paper, gpu and CPU are used as a combined computing unit, which makes good use of strong parallelcomputing ability. The effectiveness of the method has been proved. Finally, the contrasting experiments on processing time show that the proposed algorithm based on gpu is better in speed.
暂无评论