首页|EMRI-Tree:面向多分辨率可视化的层次式数据结构

EMRI-Tree:面向多分辨率可视化的层次式数据结构

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大规模科学数据的可视化要求极高的数据传输带宽和大量的内存,实现对可视化数据的高效处理是一个巨大的挑战.为了提高科学可视化的效率,最常见且直接的办法是减少需要处理的数据量.通过设计一种新的数据结构EMRI-Tree以及一种可行且灵活的渲染流程,提出了一种新的大规模数据量可视化方案.该方案的特点可以总结如下:首先,所提出的EMRI-Tree支持对大型3D模型进行高效的数据查询和感兴趣区域(ROI)数据获取,从而显著降低内存占用;其次,EMRI-Tree中不同分辨率级别的数据块以可变长度索引的形式存储在键值(KV)存储系统中,提高了存储的可扩展性和读取的并发性;最后,提出了一种基于射线行走的渐进式渲染预取方案,可以在交互时渲染出更精确的模型.综合上述优化方法,该方案可在内存开销有限的情况下,有效促进大规模高分辨率数据的可视化.通过使用80 GB的合成数据进行了 10 次模拟读取测试来评估方案效果,实验结果表明,该方案具有 2 000+QPS(每秒查询次数)和内存消耗线性增长的特点,是一种稳健且节省内存的方案.
EMRI-Tree:A hierarchical data structure for multi-resolution visualization
Visualizing large-scale scientific data requires high data transmission bandwidth and a large amount of memory.Efficient processing of visualization data poses a significant challenge.To improve the efficiency of scientific visualization,the most common and direct method is to reduce the amount of data that needs to be processed.A novel visualization scheme for large-scale volumes of data is proposed by designing a new data structure called EMRI-Tree as well as a flexible rendering workflow.The char-acteristics of our scheme can be summarized as follows.Firstly,the proposed EMRI-Tree supports memory-efficient data queries and ROI-data(ROI,region of interest)fetching on large 3 D models,thus reducing the memory footprint significantly.Secondly,data blocks at different resolution levels in the EMRI-Tree are stored in a key-value(KV)storage system with variable-length indices,which improves the scalability of storage and the concurrency of reading.Lastly,a prefetching scheme is proposed,which supports progressive rendering based on ray marching to render a more accurate model as inter-act.By combining the above optimizations,the proposed scheme facilitates the visualization of large vol-umes of high-resolution data with limited memory overhead.Evaluating the approach by using 80 GB of synthetic data in 10 simulated read tests.The experimental results demonstrate that the scheme has the characteristics of 2 000+QPS(queries per second)and linear growth in memory consumption,making it a robust and memory-efficient solution.

multidimensional Indexingoctreedata-thinningprogressive explorationlarge data visu-alization

钟权、陈志广、高蓝光

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中山大学计算机学院,广东 广州 510006

多维索引 八叉树 数据稀疏化 渐进式探索 大数据可视化

国家重点研发计划国家自然科学基金广东省特支计划

2021YFB0300103622724992021TQ06X160

2024

计算机工程与科学
国防科学技术大学计算机学院

计算机工程与科学

CSTPCD北大核心
影响因子:0.787
ISSN:1007-130X
年,卷(期):2024.46(5)
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