AMM: Adaptive Multilinear Meshes

被引:3
|
作者
Bhatia, Harsh [1 ]
Hoang, Duong [2 ]
Morrical, Nate [2 ]
Pascucci, Valerio [2 ]
Bremer, Peer-Timo [1 ]
Lindstrom, Peter [1 ]
机构
[1] Lawrence Livermore Natl Lab, Ctr Appl Sci Comp, Livermore, CA 94550 USA
[2] Univ Utah, Sci Comp & Imaging Inst, Salt Lake City, UT 84112 USA
基金
美国国家科学基金会;
关键词
Adaptive Meshes; Wavelets; Compression Techniques; Multiresolution Techniques; Streaming Data; Scalar Field Data; COMPRESSION; EFFICIENT; VISUALIZATION; INSTABILITY; REFINEMENT; SIMULATION; FRAMEWORK; WAVELETS;
D O I
10.1109/TVCG.2022.3165392
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
Adaptive representations are increasingly indispensable for reducing the in-memory and on-disk footprints of large-scale data. Usual solutions are designed broadly along two themes: reducing data precision, e.g., through compression, or adapting data resolution, e.g., using spatial hierarchies. Recent research suggests that combining the two approaches, i.e., adapting both resolution and precision simultaneously, can offer significant gains over using them individually. However, there currently exist no practical solutions to creating and evaluating such representations at scale. In this work, we present a new resolution-precision-adaptive representation to support hybrid data reduction schemes and offer an interface to existing tools and algorithms. Through novelties in spatial hierarchy, our representation, Adaptive Multilinear Meshes (AMM), provides considerable reduction in the mesh size. AMM creates a piecewise multilinear representation of uniformly sampled scalar data and can selectively relax or enforce constraints on conformity, continuity, and coverage, delivering a flexible adaptive representation. AMM also supports representing the function using mixed-precision values to further the achievable gains in data reduction. We describe a practical approach to creating AMM incrementally using arbitrary orderings of data and demonstrate AMM on six types of resolution and precision datastreams. By interfacing with state-of-the-art rendering tools through VTK, we demonstrate the practical and computational advantages of our representation for visualization techniques. With an open-source release of our tool to create AMM, we make such evaluation of data reduction accessible to the community, which we hope will foster new opportunities and future data reduction schemes.
引用
收藏
页码:2350 / 2363
页数:14
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