High-Speed Visualization of Time-Varying Data in Large-Scale Structural Dynamic Analyses with a GPU
Abstract
Large-scale structural dynamic analyses generally produce massive amount of time-varying data. Inefficient rendering of these data seriously affects the quality of display of and user interaction with the analysis results. A high-speed visualization solution using a GPU (graphics processing unit) is thus developed in this study. Based on the clustering concept, a key frame extraction algorithm specific to the GPU-based rendering is proposed, which significantly reduces the data size to satisfy the GPU memory requirement. Using the key frames, a GPU-based parallel frame interpolation algorithm is also proposed to reconstruct ...
View more >Large-scale structural dynamic analyses generally produce massive amount of time-varying data. Inefficient rendering of these data seriously affects the quality of display of and user interaction with the analysis results. A high-speed visualization solution using a GPU (graphics processing unit) is thus developed in this study. Based on the clustering concept, a key frame extraction algorithm specific to the GPU-based rendering is proposed, which significantly reduces the data size to satisfy the GPU memory requirement. Using the key frames, a GPU-based parallel frame interpolation algorithm is also proposed to reconstruct the complete structural dynamic process. Particularly, a novel data access model considering the features of time-varying data and GPU memory is designed to improve the interpolation efficiency. Two case studies including an arch bridge and a high-rise building are presented, confirming the ability of the proposed solution to provide a high-speed and interactive visualization environment for large-scale structural dynamic analyses.
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View more >Large-scale structural dynamic analyses generally produce massive amount of time-varying data. Inefficient rendering of these data seriously affects the quality of display of and user interaction with the analysis results. A high-speed visualization solution using a GPU (graphics processing unit) is thus developed in this study. Based on the clustering concept, a key frame extraction algorithm specific to the GPU-based rendering is proposed, which significantly reduces the data size to satisfy the GPU memory requirement. Using the key frames, a GPU-based parallel frame interpolation algorithm is also proposed to reconstruct the complete structural dynamic process. Particularly, a novel data access model considering the features of time-varying data and GPU memory is designed to improve the interpolation efficiency. Two case studies including an arch bridge and a high-rise building are presented, confirming the ability of the proposed solution to provide a high-speed and interactive visualization environment for large-scale structural dynamic analyses.
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Journal Title
Automation in Construction
Volume
42
Copyright Statement
© 2014 Elsevier B.V.. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. Please refer to the journal's website for access to the definitive, published version.
Subject
Structural Engineering