Browsing by Author "Ma, Kwan-Liu"

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    Memory-Efficient GPU Volume Path Tracing of AMR Data Using the Dual Mesh
    (The Eurographics Association and John Wiley & Sons Ltd., 2023) Zellmann, Stefan; Wu, Qi; Ma, Kwan-Liu; Wald, Ingo; Bujack, Roxana; Archambault, Daniel; Schreck, Tobias
    A common way to render cell-centric adaptive mesh refinement (AMR) data is to compute the dual mesh and visualize that with a standard unstructured element renderer. While the dual mesh provides a high-quality interpolator, the memory requirements of the dual mesh data structure are significantly higher than those of the original grid, which prevents rendering very large data sets. We introduce a GPU-friendly data structure and a clustering algorithm that allow for efficient AMR dual mesh rendering with a competitive memory footprint. Fundamentally, any off-the-shelf unstructured element renderer running on GPUs could be extended to support our data structure just by adding a gridlet element type in addition to the standard tetrahedra, pyramids, wedges, and hexahedra supported by default. We integrated the data structure into a volumetric path tracer to compare it to various state-of-the-art unstructured element sampling methods. We show that our data structure easily competes with these methods in terms of rendering performance, but is much more memory-efficient.
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    Resolving Conflicting Insights in Asynchronous Collaborative Visual Analysis
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Li, Jianping Kelvin; Xu, Shenyu; Ye, Yecong (Chris); Ma, Kwan-Liu; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
    Analyzing large and complex datasets for critical decision making can benefit from a collective effort involving a team of analysts. However, insights and findings from different analysts are often incomplete, disconnected, or even conflicting. Most existing analysis tools lack proper support for examining and resolving the conflicts among the findings in order to consolidate the results of collaborative data analysis. In this paper, we present CoVA, a visual analytics system incorporating conflict detection and resolution for supporting asynchronous collaborative data analysis. By using a declarative visualization language and graph representation for managing insights and insight provenance, CoVA effectively leverages distributed revision control workflow from software engineering to automatically detect and properly resolve conflicts in collaborative analysis results. In addition, CoVA provides an effective visual interface for resolving conflicts as well as combining the analysis results. We conduct a user study to evaluate CoVA for collaborative data analysis. The results show that CoVA allows better understanding and use of the findings from different analysts.