Optimization of large-scale frame structures consumes a vast amount of time since the analysis of such complex systems contains several iterative processes. Mitigating computational burden and reducing this time to a ...
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Optimization of large-scale frame structures consumes a vast amount of time since the analysis of such complex systems contains several iterative processes. Mitigating computational burden and reducing this time to a reasonable level is possible by running gpu (Graphical Processing Unit) processors, which can be found on standard computers. This study presents an algorithm for the acceleration of size optimization of steel frames by using the BBO (Biogeography-based Optimization) method that is suitable for gpu architecture. The gpu-based parallel algorithm, designed for FEM (Finite Element Method) analysis, is applied to three hypothetical steel-frame case structures with different numbers of members and nodes;and processed on four different computers which are available on the market. The presented case studies revealed that the proposed solution's efficiency increases as the number of members increases and confirmed the ability of the acceleration algorithm for optimization of large-scale frame structures and provided time efficiency.
Association rule mining is a popular data mining task, which has important in many domains. Because the task of association rule mining is very time consuming, evolutionary and swarm basedalgorithms have been designe...
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Association rule mining is a popular data mining task, which has important in many domains. Because the task of association rule mining is very time consuming, evolutionary and swarm basedalgorithms have been designed to find approximate solutions. However, these approaches still have long execution times, especially when applied on dense and big databases, or when low minsup and minconf threshold values are used. Moreover, these approaches suffer from the lack of diversity in the rules presented to the user. To address these drawbacks of previous algorithms, this paper proposes an efficient parallel algorithm named CgpuGA. It is a genetic algorithm that runs on clusters of CPUs to efficiently discover diversified association rules. It benefits from cluster computing to generate rules. Then, to evaluate rules, which is the most time consuming task, the designed algorithm relies on the massively parallel gpu threads. Furthermore, to deal with the issue of rule quality, the search space of rules is partitioned into several regions assigned to different workers, and rules found by each workers are the merged to ensure diversification. The designed approach has been empirically compared with state-of-the-art algorithms using small, medium, large and big datasets. Results reveal that CgpuGA is 600 times faster than the sequential version of the algorithm for big datasets. Moreover, it outperforms state-ofthe-art high performance computing based association rule mining algorithms for real big datasets such as Pokec, Webdocs and Wikilinks. In terms of rule quality, results show that the designed CgpuGA algorithm provides rules of higher quality compared to the state-ofthe-art NIGGAR, MSP-MPSO and MPGA algorithms for diversified association rule mining. (C) 2018 Elsevier Inc. All rights reserved.
Seismic acquisition geometries have a significant influence on the quality of seismic data in the oil and gas exploration process. Therefore, prior analyses are beneficial to the design of acquisition geometries befor...
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Seismic acquisition geometries have a significant influence on the quality of seismic data in the oil and gas exploration process. Therefore, prior analyses are beneficial to the design of acquisition geometries before implementation of seismic acquisition. The focal beam analysis method can provide quantitative insights into the combined influence of acquisition geometries and subsurface structures. This approach involves a large calculation burden concerning 3D wavefield extrapolation in the case of complex media, thus inhibiting the practical application of focal beam analysis in complex media when using regular CPUs. Therefore, using a graphics processing unit (gpu) to accelerate focal beam analysis becomes imperative. We have developed a fast parallel algorithm to speed up the focal beam analysis for 3D acquisition geometries in complex media on gpus. Three-dimensional numerical examples show that the gpu-based focal beam analysis runs about 17 times faster than a serial CPU-based one. We also demonstrate the validity and scalability of the proposed approach with numerical examples. The boost in performance afforded by the gpu architecture allows us to analyse 3D acquisition geometries in complex media with less time and at lower cost of hardware.
In X-ray computed tomography (CT) iterative methods are more suitable for the reconstruction of images with high contrast and precision in noisy conditions from a small number of projections. However, in practice, the...
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In X-ray computed tomography (CT) iterative methods are more suitable for the reconstruction of images with high contrast and precision in noisy conditions from a small number of projections. However, in practice, these methods are not widely used due to the high computational cost of their implementation. Nowadays technology provides the possibility to reduce effectively this drawback. It is the goal of this work to develop a fast gpu-based algorithm to reconstruct high quality images from under sampled and noisy projection data. (C) 2013 Elsevier Ltd. All rights reserved.
In this paper we consider the pair-wise sequence alignment problem with gaps, which is motivated by the re sequencing problem that requires to assemble short reads sequences into a genome sequence by referring to a re...
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ISBN:
(纸本)9781467365987
In this paper we consider the pair-wise sequence alignment problem with gaps, which is motivated by the re sequencing problem that requires to assemble short reads sequences into a genome sequence by referring to a reference sequence. The problem has been studied before for single gap and bounded number of gaps. For single gap, there was a gpu-based algorithm proposed. In our work we propose a gpu-based algorithm for the bounded number of gaps case. We implemented the algorithm and compare the performance with the CPU-basedalgorithm in a multithreadded environment;the results are promising with the gpu version achieving a speedup of 30 times.
Numerical methods have become useful tools for predicting vehicle mobility performance on granular terrain. However, due to the large number of soil particles and complex contact search in tire-granular terrain simula...
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Numerical methods have become useful tools for predicting vehicle mobility performance on granular terrain. However, due to the large number of soil particles and complex contact search in tire-granular terrain simulation, time-consuming calculation has become the most critical problem restricting the application of these methods. In this study, an efficient gpu-based DEM-FEM for simulation of the interactions between an off-road tire and granular terrain is implemented to improve computational efficiency. In this method, the main body of calculation is executed on gpu and programmed into an in-house developed code CDFP. The new gpu-based computing framework consists of 14 kernels, including efficient contact calculation between large-scale particles, novel contact calculation between particles and complex tread, contact calculation between particles and boundary wall, internal force calculation of finite elements, and information update. As a result, the efficient contact search and the complex interactions between an off-road tire and granular terrain are accomplished. based on the self-developed single-wheel test device, an accurate simulation model consistent with the experiment is established. The validity of the proposed method is verified by comparing the simulation results with the experimental results. Finally, the discussion of computational efficiency shows that the proposed gpu-based DEM-FEM can be a powerful tool to simulate the interactions between an off-road tire and granular terrain.
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