大规模元网格和三角网格结构

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC ACS Applied Electronic Materials Pub Date : 2024-05-28 DOI:10.1016/j.cad.2024.103732
Qiang Zou, Yunzhu Gao, Guoyue Luo, Sifan Chen
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引用次数: 0

摘要

晶格结构因其卓越的机械性能而被广泛应用。为了制造这种结构,通常会采用一个称为三角剖分的几何处理步骤,将其转换为 STL 格式,然后再发送给 3D 打印机。由于晶格结构往往具有较高的几何复杂性,这一步骤通常会生成大量三角形,是一项内存和计算密集型任务。这个问题在拥有数百万或数十亿支柱的大型网格结构中表现得非常明显。为解决这一问题,本文建议在进行真正的三角剖分之前,将网格结构转化为一种称为元网格的中间模型。与三角网格相比,元网格非常轻便,对计算的要求也低得多。元网格还可以作为基础网格,方便高效地对任意分辨率的网格结构进行三角剖分。我们开发了一种 CPU+GPU 异步元网格流水线,可从网格结构高效生成元网格。它将 CAD 中常用的以线程为中心的 GPU 算法设计范式转变为最新的以翘曲为中心的设计范式,以实现高性能。这是通过一种新的数据压缩方法、一种 GPU 缓存感知数据结构和一种可显著减少内存发散和分支发散的工作负载平衡调度方法来实现的。通过对各种十亿尺度晶格结构的实验,可以看到所提出的方法比以前实现的方法快两个数量级。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Meta-Meshing and Triangulating Lattice Structures at a Large Scale

Lattice structures have been widely used in applications due to their superior mechanical properties. To fabricate such structures, a geometric processing step called triangulation is often employed to transform them into the STL format before sending them to 3D printers. Because lattice structures tend to have high geometric complexity, this step usually generates a large amount of triangles, a memory and compute-intensive task. This problem manifests itself clearly through large-scale lattice structures that have millions or billions of struts. To address this problem, this paper proposes to transform a lattice structure into an intermediate model called meta-mesh before undergoing real triangulation. Compared to triangular meshes, meta-meshes are very lightweight and much less compute-demanding. The meta-mesh can also work as a base mesh reusable for conveniently and efficiently triangulating lattice structures with arbitrary resolutions. A CPU+GPU asynchronous meta-meshing pipeline has been developed to efficiently generate meta-meshes from lattice structures. It shifts from the thread-centric GPU algorithm design paradigm commonly used in CAD to the recent warp-centric design paradigm to achieve high performance. This is achieved by a new data compression method, a GPU cache-aware data structure, and a workload-balanced scheduling method that can significantly reduce memory divergence and branch divergence. Experimenting with various billion-scale lattice structures, the proposed method is seen to be two orders of magnitude faster than previously achievable.

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来源期刊
CiteScore
7.20
自引率
4.30%
发文量
567
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