Efficient Visualization Strategies for Large-Scale Finite Element Models

Author(s):  
Xu Liangyin ◽  
Li Yunpeng ◽  
Zhang Sheng ◽  
Chen Biaosong

In this paper, an effective strategy is proposed to realize the smooth visualization of large-scale finite element models on a desktop computer. Based on multicore parallel and graphics processing unit (GPU) computing techniques, the large-scale data of a finite element model and the corresponding graphics data can be handled and rendered effectively. The proposed strategies mainly consist of four parts. First, a parallel surface extraction technology based on the dual connections of elements and nodes is developed to reduce the graphics data. Second, the OpenGL vertex buffer object (VBO) technology is used to improve the rendering efficiency after surface extraction. Third, the element-hiding and cut-surface functions are implemented to facilitate the observation of the interior of the meshes. Finally, the stream/filter architecture, which has the advantages of efficient computation and communication, is introduced to meet the needs of large-scale data processing and various visualization methods. These strategies are developed on the general visualization system SiPESC.Post. Using these strategies, SiPESC.Post implements high-performance display and real-time operation for large-scale finite element models, especially for models containing millions or tens of millions of elements. To demonstrate the superiority and feasibility of the presented strategies, large-scale numerical examples are presented, and the strategies are compared with several commercial finite element software systems and open-source visual postprocessing packages in terms of visualization efficiency.

2009 ◽  
Vol 28 (11) ◽  
pp. 2737-2740
Author(s):  
Xiao ZHANG ◽  
Shan WANG ◽  
Na LIAN

2016 ◽  
Author(s):  
John W. Williams ◽  
◽  
Simon Goring ◽  
Eric Grimm ◽  
Jason McLachlan

2008 ◽  
Vol 9 (10) ◽  
pp. 1373-1381 ◽  
Author(s):  
Ding-yin Xia ◽  
Fei Wu ◽  
Xu-qing Zhang ◽  
Yue-ting Zhuang

2021 ◽  
Vol 77 (2) ◽  
pp. 98-108
Author(s):  
R. M. Churchill ◽  
C. S. Chang ◽  
J. Choi ◽  
J. Wong ◽  
S. Klasky ◽  
...  

Author(s):  
Krzysztof Jurczuk ◽  
Marcin Czajkowski ◽  
Marek Kretowski

AbstractThis paper concerns the evolutionary induction of decision trees (DT) for large-scale data. Such a global approach is one of the alternatives to the top-down inducers. It searches for the tree structure and tests simultaneously and thus gives improvements in the prediction and size of resulting classifiers in many situations. However, it is the population-based and iterative approach that can be too computationally demanding to apply for big data mining directly. The paper demonstrates that this barrier can be overcome by smart distributed/parallel processing. Moreover, we ask the question whether the global approach can truly compete with the greedy systems for large-scale data. For this purpose, we propose a novel multi-GPU approach. It incorporates the knowledge of global DT induction and evolutionary algorithm parallelization together with efficient utilization of memory and computing GPU’s resources. The searches for the tree structure and tests are performed simultaneously on a CPU, while the fitness calculations are delegated to GPUs. Data-parallel decomposition strategy and CUDA framework are applied. Experimental validation is performed on both artificial and real-life datasets. In both cases, the obtained acceleration is very satisfactory. The solution is able to process even billions of instances in a few hours on a single workstation equipped with 4 GPUs. The impact of data characteristics (size and dimension) on convergence and speedup of the evolutionary search is also shown. When the number of GPUs grows, nearly linear scalability is observed what suggests that data size boundaries for evolutionary DT mining are fading.


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