AMM: Adaptive Multilinear Meshes

Bhatia, Harsh; Hoang, Duong; Morrical, Nate; Pascucci, Valerio; Bremer, Peer-Timo; Lindstrom, Peter

doi:10.1109/tvcg.2022.3165392

Title: AMM: Adaptive Multilinear Meshes

Journal Article · Fri Apr 08 00:00:00 UTC 2022 · IEEE Transactions on Visualization and Computer Graphics

DOI: https://doi.org/10.1109/tvcg.2022.3165392 · OSTI ID:1872698

Bhatia, Harsh ^[1]; Hoang, Duong ^[2]; Morrical, Nate ^[2]; Pascucci, Valerio ^[2]; Bremer, Peer-Timo ^[1]; Lindstrom, Peter ^[1]

Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). Center for Applied Scientific Computing
Univ. of Utah, Salt Lake City, UT (United States). Scientific Computing and Imaging Inst.

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. Additionally, 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.

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Research Organization:: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Sponsoring Organization:: National Science Foundation (NSF); USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: AC52-07NA27344; FE0031880

OSTI ID:: 1872698

Report Number(s):: LLNL-JRNL-771697; 962548

Journal Information:: IEEE Transactions on Visualization and Computer Graphics, Journal Name: IEEE Transactions on Visualization and Computer Graphics Journal Issue: 6 Vol. 28; ISSN 1077-2626

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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