面向大规模时变数据可视化的多分辨率体绘制框架

Chaoli Wang, Jinzhu Gao, Liya Li, Han-Wei Shen
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引用次数: 63

摘要

我们提出了一种新的并行多分辨率体绘制框架,该框架采用基于小波的时空划分(WTSP)树进行大规模时变数据可视化。利用小波变换将大尺度时变数据集转换成时空多分辨率数据层次,并存储在一棵时空划分树中。为了消除重建过程中父子数据的依赖关系,实现负载均衡渲染,设计了一种对WTSP树进行分区的算法,并采用误差引导桶化方法将小波压缩后的数据沿分层空间填充曲线分布。在运行时,根据用户指定的时间步长和空间和时间错误的容忍度遍历WTSP树。对不同时空分辨率的数据块进行并行重构和渲染,构成最终图像。我们证明,当在PC集群上可视化千兆字节时变数据时,我们的算法可以将运行时通信成本降至最低,并确保处理器之间的工作负载均衡。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A multiresolution volume rendering framework for large-scale time-varying data visualization
We present a new parallel multiresolution volume rendering framework for large-scale time-varying data visualization using the wavelet-based time-space partitioning (WTSP) tree. Utilizing the wavelet transform, a large-scale time-varying data set is converted into a space-time multiresolution data hierarchy, and is stored in a time-space partitioning (TSP) tree. To eliminate the parent-child data dependency for reconstruction and achieve load-balanced rendering, we design an algorithm to partition the WTSP tree and distribute the wavelet-compressed data along hierarchical space-filling curves with error-guided bucketization. At run time, the WTSP tree is traversed according to the user-specified time step and tolerances of both spatial and temporal errors. Data blocks of different spatio-temporal resolutions are reconstructed and rendered to compose the final image in parallel. We demonstrate that our algorithm can reduce the run-time communication cost to a minimum and ensure a well-balanced workload among processors when visualizing gigabytes of time-varying data on a PC cluster.
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