EGPGV: Eurographics Workshop on Parallel Graphics and Visualization

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Browsing EGPGV: Eurographics Workshop on Parallel Graphics and Visualization by Subject "Approximate methods"

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    Hybrid Remote Visualization in Immersive Virtual Environments with Vistle
    (The Eurographics Association, 2019) Aumüller, Martin; Childs, Hank and Frey, Steffen
    Because of the spatial separation of high performance compute resources and immersive visualization systems, their combined use requires remote visualization. Remote rendering incurs increased latency from user interaction to display. For immersive virtual environments, this latency is a bigger problem than for desktop visualization. With hybrid remote visualization we enable the exploration of large-scale remote data sets from immersive virtual environments. This is based on three factors: When appropriate, we enable the local rendering of remote objects. We decouple local interaction from remote rendering as far as possible by depth compositing of remote and local images at a rate independent from remote rendering. Finally, we try to hide this latency by reprojecting 2.5D images for changed viewer positions. In this paper we describe the integration of hybrid remote rendering into the data-parallel visualization system Vistle as well its extension to a distributed system. Thereby arbitrary combinations of object-based and image-based remote visualization become possible.
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    A Study of Parallel Data Compression Using Proper Orthogonal Decomposition on the K Computer
    (The Eurographics Association, 2014) Bi, Chongke; Ono, Kenji; Ma, Kwan-Liu; Wu, Haiyuan; Imamura, Toshiyuki; Margarita Amor and Markus Hadwiger
    The growing power of supercomputers continues to improve scientists' ability to model larger, more sophisticated problems in science with higher accuracy. An equally important ability is to make full use of the data output from the simulations to help clarify the modeled phenomena and facilitate the discovery of new phenomena. However, along with the scale of computation, the size of the resulting data has exploded; it becomes infeasible to output most of the data, which defeats the purpose of conducting large-scale simulations. In order to address this issue so that more data may be archived and studied, we have developed a scalable parallel data compression solution to reduce the size of large-scale data with low computational cost and minimal error. We use the proper orthogonal decomposition (POD) method to compress data because this method can effectively extract the main features from the data, and the resulting compressed data can be decompressed in linear time. Our implementation achieves high parallel efficiency with a binary load-distributed approach, which is similar to the binary-swap image composition method. This approach allows us to effectively use all of the processors and to reduce the interprocessor communication cost throughout the parallel compression calculations. The results of tests using the K computer indicate the superior performance of our design and implementation