面向大体积可视化的分布式交互光线追踪

David E. DeMarle, S. Parker, M. Hartner, C. Gribble, C. Hansen
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引用次数: 86

摘要

我们已经构建了一个分布式并行光线追踪系统,该系统可以在商用pc集群上交互式地从大型数据集生成等值面渲染。该程序源自SCI研究所的交互式射线追踪器(*-Ray),它利用大小不等的共享内存平台,如SGI Origin系列,与非常大规模的数据集进行交互。要使这种方法在集群上有效地工作,需要注意许多系统级问题,特别是在呈现大于每个集群节点地址空间的数据集时。渲染引擎是一个具有主管/工人组织的图像并行光线跟踪器。集群中的每个节点都运行一个多线程应用程序。TCP之上的最小抽象层连接节点,并支持异步消息处理。对于大容量,呈现线程根据需要从基于对象的软件分布式共享内存中获取数据块。缓存通过在合理的工作集大小下减少数据传输量来提高性能。对于大型数据集,基于集群的交互式光线追踪器的性能与SGI Origin系统相当。我们检查了渲染器的参数空间,并提供了大型(7.5 GB)数据集的交互式渲染的实验结果。
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Distributed interactive ray tracing for large volume visualization
We have constructed a distributed parallel ray tracing system that interactively produces isosurface renderings from large data sets on a cluster of commodity PCs. The program was derived from the SCI Institute's interactive ray tracer (*-Ray), which utilizes small to large shared memory platforms, such as the SGI Origin series, to interact with very large-scale data sets. Making this approach work efficiently on a cluster requires attention to numerous system-level issues, especially when rendering data sets larger than the address space of each cluster node. The rendering engine is an image parallel ray tracer with a supervisor/workers organization. Each node in the cluster runs a multithreaded application. A minimal abstraction layer on top of TCP links the nodes, and enables asynchronous message handling. For large volumes, render threads obtain data bricks on demand from an object-based software distributed shared memory. Caching improves performance by reducing the amount of data transfers for a reasonable working set size. For large data sets, the cluster-based interactive ray tracer performs comparably with an SGI Origin system. We examine the parameter space of the renderer and provide experimental results for interactive rendering of large (7.5 GB) data sets.
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