AMM：自适应多线性网格
AMM: Adaptive Multilinear Meshes
摘要：
自适应表示对于减少大规模数据在内存和磁盘上的占用空间越来越不可或缺。商业解决方案主要围绕两个主题进行设计：降低数据精度，例如，通过压缩或调整数据分辨率，例如，使用空间层次。最近的研究表明，结合这两种方法，即，同时调整分辨率和精度可以提供比单独使用它们显著的增益。然而，目前不存在创建和评估这种大规模表示的实际解决方案。在这项工作中，我们提出了一个新的分辨率精度自适应表示，以支持混合数据减少计划，并提供了一个接口，现有的工具和算法。通过新奇的空间层次结构，我们的表示，自适应多线性网格（AMM），提供了相当大的减少网格大小。AMM创建均匀采样标量数据的分段多线性表示，并可以选择性地放松或强制执行一致性，连续性和覆盖范围的约束，提供灵活的自适应表示。AMM还支持使用混合精度值表示函数，以进一步减少数据量。我们描述了一个实用的方法来创建AMM增量使用任意顺序的数据，并证明AMM六种类型的分辨率和精度的数据流。通过与国家的最先进的渲染工具，通过VTK接口，我们展示了我们的可视化技术表示的实际和计算的优势。随着我们创建AMM的工具的开源版本，我们使社区可以访问这种数据减少的评估，我们希望这将促进新的机会和未来的数据减少计划
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
