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.
关键词
多维索引/八叉树/数据稀疏化/渐进式探索/大数据可视化
Key words
multidimensional Indexing/octree/data-thinning/progressive exploration/large data visu-alization
维普
万方数据