EGPGV20: Eurographics Symposium on Parallel Graphics and Visualization

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https://diglib.eg.org/handle/10.2312/2632899

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Browsing EGPGV20: Eurographics Symposium on Parallel Graphics and Visualization by Subject "Human centered computing"

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    Alternative Parameters for On-The-Fly Simplification of MergeTrees
    (The Eurographics Association, 2020) Werner, Kilian; Garth, Christoph; Frey, Steffen and Huang, Jian and Sadlo, Filip
    Topological simplification of merge trees requires a user specified persistence threshold. As this threshold is based on prior domain knowledge and has an unpredictable relation to output size, its use faces challenges in large-data situations like online, distributed or out-of-core scenarios. We propose two alternative parameters, a targeted percentile size reduction and a total output size limit, to increase flexibility in those scenarios.
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    Approaches for In Situ Computation of Moments in a Data-Parallel Environment
    (The Eurographics Association, 2020) Tsai, Karen C.; Bujack, Roxana; Geveci, Berk; Ayachit, Utkarsh; Ahrens, James; Frey, Steffen and Huang, Jian and Sadlo, Filip
    Feature-driven in situ data reduction can overcome the I/O bottleneck that large simulations face in modern supercomputer architectures in a semantically meaningful way. In this work, we make use of pattern detection as a black box detector of arbitrary feature templates of interest. In particular, we use moment invariants because they allow pattern detection independent of the specific orientation of a feature. We provide two open source implementations of a rotation invariant pattern detection algorithm for high performance computing (HPC) clusters with a distributed memory environment. The first one is a straightforward integration approach. The second one makes use of the Fourier transform and the Cross-Correlation Theorem. In this paper, we will compare the two approaches with respect to performance and flexibility and showcase results of the in situ integration with real world simulation code.
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    High-Quality Rendering of Glyphs Using Hardware-Accelerated Ray Tracing
    (The Eurographics Association, 2020) Zellmann, Stefan; Aumüller, Martin; Marshak, Nathan; Wald, Ingo; Frey, Steffen and Huang, Jian and Sadlo, Filip
    Glyph rendering is an important scientific visualization technique for 3D, time-varying simulation data and for higherdimensional data in general. Though conceptually simple, there are several different challenges when realizing glyph rendering on top of triangle rasterization APIs, such as possibly prohibitive polygon counts, limitations of what shapes can be used for the glyphs, issues with visual clutter, etc. In this paper, we investigate the use of hardware ray tracing for high-quality, highperformance glyph rendering, and show that this not only leads to a more flexible and often more elegant solution for dealing with number and shape of glyphs, but that this can also help address visual clutter, and even provide additional visual cues that can enhance understanding of the dataset.
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    Improving Performance of M-to-N Processing and Data Redistribution in In Transit Analysis and Visualization
    (The Eurographics Association, 2020) Loring, Burlen; Wolf, Mathew; Kress, James; Shudler, Sergei; Gu, Junmin; Rizzi, Silvio; Logan, Jeremy; Ferrier, Nicola; Bethel, E. Wes; Frey, Steffen and Huang, Jian and Sadlo, Filip
    In an in transit setting, a parallel data producer, such as a numerical simulation, runs on one set of ranks M, while a data consumer, such as a parallel visualization application, runs on a different set of ranks N: One of the central challenges in this in transit setting is to determine the mapping of data from the set of M producer ranks to the set of N consumer ranks. This is a challenging problem for several reasons, such as the producer and consumer codes potentially having different scaling characteristics and different data models. The resulting mapping from M to N ranks can have a significant impact on aggregate application performance. In this work, we present an approach for performing this M-to-N mapping in a way that has broad applicability across a diversity of data producer and consumer applications. We evaluate its design and performance with a study that runs at high concurrency on a modern HPC platform. By leveraging design characteristics, which facilitate an ''intelligent'' mapping from M-to-N, we observe significant performance gains are possible in terms of several different metrics, including time-to-solution and amount of data moved.