EGPGV19: Eurographics Symposium on Parallel Graphics and Visualization

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https://diglib.eg.org/handle/10.2312/2632789

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Browsing EGPGV19: Eurographics Symposium on Parallel Graphics and Visualization by Subject "Human"

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    Fast Mesh Validation in Combustion Simulations through In-Situ Visualization
    (The Eurographics Association, 2019) Shudler, Sergei; Ferrier, Nicola; Insley, Joseph; Papka, Michael E.; Patel, Saumil; Rizzi, Silvio; Childs, Hank and Frey, Steffen
    In-situ visualization and analysis is a powerful concept that aims to give users the ability to process data while it is still resident in memory, thereby vastly reducing the amount of data left for post-hoc analysis. The problem of having too much data for posthoc analysis is exacerbated in large-scale high-performance computing applications such as Nek5000, a massively-parallel CFD (Computational Fluid Dynamics) code used primarily for thermal hydraulics problems. Specifically, one problem users of Nek5000 often face is validating the mesh, that is identifying the exact location of problematic mesh elements within the whole mesh. Employing the standard post-hoc approach to address this problem is both time consuming and requires vast storage space. In this paper, we demonstrate how in-situ visualization, produced with SENSEI, a generic in-situ platform, helps users quickly validate the mesh. We also provide a bridge between Nek5000 and SENSEI that enables users to use any existing and future analysis routines in SENSEI. The approach is evaluated on a number of realistic datasets.
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    Scalable Parallel Flow Visualization Using 3D Line Integral Convolution for Large Scale Unstructured Simulation Data
    (The Eurographics Association, 2019) Liao, Yangguang; Matsui, Hiroaki; Kreylos, Oliver; Kellogg, Louise; Childs, Hank and Frey, Steffen
    To address the need of highly efficient and scalable parallel flow visualization methods, we developed a flow visualization system for large unstructured simulation data using parallel 3D line integral convolution (LIC). The main consideration for a parallel LIC implementation is a trade-off between the additional memory cost of replicating cells at sub-domain boundaries, or the communication cost of exchanging those data among computation nodes. To improve scalability, we introduce a load-balancing scheme that partitions datasets based on estimated LIC computation time. We also introduce a data-driven sub-domain extension scheme that determines which external cells at sub-domain boundary need to be added based on current boundary cells, which reduces memory overhead because the same visual quality can be achieved with a significantly smaller number of replicated external cells. We evaluate our visualization method by first comparing its parallel scalability to traditional integral field lines methods. Next, we compare our cost-driven domain decomposition method to existing methods to verify that ours leads to more balanced computation and improved scalability. Finally, we compare our data-driven sub-domain expansion method to traditional layer-based expansion methods in terms of memory overhead and visual quality. We conclude that our parallel 3D LIC method is an efficient and scalable approach to visualization of large and complex 3D vector fields.