Tomographic imaging and computer simulations are increasingly yielding massive datasets. Interactive and exploratory visualizations have rapidly become indispensable tools to study large volumetric imaging and simulation data. Our scalable isosurface visualization framework on commodity off-the-shelf clusters is an end-to-end parallel and progressive platform, from initial data access to the final display. Interactive browsing of extracted isosurfaces is made possible by using parallel isosurface extraction, and rendering in conjunction with a new specialized piece of image compositing hardware called Metabuffer. In this paper, we focus on the back end scalability by introducing a fully parallel and out-of-core isosurface extraction algorithm. It achieves scalability by using both parallel and out-of-core processing and parallel disks. It statically partitions the volume data to parallel disks with a balanced workload spectrum, and builds I/O-optimal external interval trees to minimize the number of I/O operations of loading large data from disk. We also describe an isosurface compression scheme that is efficient for progress extraction, transmission and storage of isosurfaces.

中文翻译：

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商品现成集群上大量数据集的可扩展等值面可视化。

断层成像和计算机模拟越来越多地产生大量数据集。交互式和探索性可视化已迅速成为研究大型体积成像和模拟数据不可或缺的工具。我们在商品现成集群上的可扩展等值面可视化框架是一个端到端的并行渐进式平台，从初始数据访问到最终显示。通过使用并行等值面提取，并结合称为 Metabuffer 的新的专用图像合成硬件进行渲染，可以对提取的等值面进行交互式浏览。在本文中，我们通过引入完全并行和核外等值面提取算法来关注后端可扩展性。它通过使用并行和核外处理以及并行磁盘来实现可扩展性。它将卷数据静态分区到具有均衡工作负载谱的并行磁盘，并构建 I/O 优化的外部间隔树，以最大限度地减少从磁盘加载大数据的 I/O 操作次数。我们还描述了一种等值面压缩方案，该方案对于等值面的进程提取、传输和存储是有效的。