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首页> 外文期刊>IEEE Transactions on Parallel and Distributed Systems >Hypergraph-Partitioning-Based Remapping Models for Image-Space-Parallel Direct Volume Rendering of Unstructured Grids
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Hypergraph-Partitioning-Based Remapping Models for Image-Space-Parallel Direct Volume Rendering of Unstructured Grids

机译：非结构化网格的图像空间并行直接体积渲染的基于超图分区的重映射模型

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摘要

In this work, image-space-parallel direct volume rendering (DVR) of unstructured grids is investigated for distributed-memory architectures. A hypergraph-partitioning-based model is proposed for the adaptive screen partitioning problem in this context. The proposed model aims to balance the rendering loads of processors while trying to minimize the amount of data replication. In the parallel DVR framework we adopted, each data primitive is statically owned by its home processor, which is responsible from replicating its primitives on other processors. Two appropriate remapping models are proposed by enhancing the above model for use within this framework. These two remapping models aim to minimize the total volume of communication in data replication while balancing the rendering loads of processors. Based on the proposed models, a parallel DVR algorithm is developed. The experiments conducted on a PC cluster show that the proposed remapping models achieve better speedup values compared to the remapping models previously suggested for image-space-parallel DVR
机译：在这项工作中，针对分布式内存体系结构，研究了非结构化网格的图像空间并行直接体积渲染（DVR）。在这种情况下，针对自适应屏幕分区问题，提出了一种基于超图分区的模型。提出的模型旨在平衡处理器的呈现负载，同时尽量减少数据复制量。在我们采用的并行DVR框架中，每个数据原语均由其本地处理器静态拥有，该处理器负责将其原语复制到其他处理器上。通过增强上述模型以在此框架中使用，提出了两个适当的重映射模型。这两个重映射模型旨在最大程度地减少数据复制中的通信总量，同时平衡处理器的呈现负载。基于提出的模型，开发了并行DVR算法。在PC机群上进行的实验表明，与之前针对图像空间并行DVR建议的重新映射模型相比，所提出的重新映射模型可实现更好的加速值。

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