EGPGV08: Eurographics Symposium on Parallel Graphics and Visualization
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Browsing EGPGV08: Eurographics Symposium on Parallel Graphics and Visualization by Subject "Categories and Subject Descriptors (according to ACM CCS): I.3.2 [Graphics Systems]: Distributed/network graphics I.3.6 [Methodology and Techniques]: Graphics data structures and data types"
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Item Parallel Simplification of Large Meshes on PC Clusters(The Eurographics Association, 2008) Xiong, Hua; Jiang, Xiaohong; Zhang, Yaping; Shi, Jiaoying; Jean M. Favre and Kwan-Liu MaLarge meshes are becoming commonplace with the advance of 3D scanning, scientific simulation and CAD technology. While there are many algorithms proposed to simplify these large meshes, the time of simplification process is usually very long, especially for those algorithms based on iterative edge collapse. To address this problem, we propose two parallel schemes to speed up simplifying large meshes on a PC cluster. The first parallel simplification scheme partitions a large mesh into small sub-meshes, simplifies these sub-meshes in parallel in an in-core way and finally stitches the simplified versions together. The second scheme generates multiple mesh streams, applies stream simplification to them in parallel in an out-of-core way, and composes the final simplified mesh streams. We have implemented these two parallel simplification schemes and the experimental results show that our methods are able to speed up the iterative simplification of large meshes by a factor of 8 to 19 on a cluster of 24 PCs.Item Time-Critical Distributed Visualization with Fault Tolerance(The Eurographics Association, 2008) Gao, Jinzhu; Liu, Huadong; Huang, Jian; Beck, Micah; Wu, Qishi; Moore, Terry; Kohl, James; Jean M. Favre and Kwan-Liu MaIt is often desirable or necessary to perform scientific visualization in geographically remote locations, away from the centralized data storage systems that hold massive amounts of scientific results. The larger such scientific datasets are, the less practical it is to move these datasets to remote locations for collaborators. In such scenarios, efficient remote visualization solutions can be crucial. Yet the use of distributed or heterogeneous computing resources raises several challenges for large-scale data visualization. Algorithms must be robust and incorporate advanced load balancing and scheduling techniques. In this paper, we propose a time-critical remote visualization system that can be deployed over distributed and heterogeneous computing resources. We introduce an "importance" metric to measure the need for processing each data partition based on its degree of contribution to the final visual image. Factors contributing to this metric include specific application requirements, value distributions inside the data partition, and viewing parameters. We incorporate "visibility" in our measurement as well so that empty or invisible blocks will not be processed. Guided by the data blocks' importance values, our dynamic scheduling scheme determines the rendering priority for each visible block. That is, more important blocks will be rendered first. In time-critical scenarios, our scheduling algorithm also dynamically reduces the level-of-detail for the less important regions so that visualization can be finished in a user-specified time limit with highest possible image quality. This system enables interactive sharing of visualization results. To evaluate the performance of this system, we present a case study using a 250 Gigabyte dataset on 170 distributed processors.