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A FAST ITERATIVE METHOD FOR SOLVING THE EIKONAL EQUATION ON TRIANGULATED SURFACES

机译:快速求解三角曲面上人文方程的迭代方法

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摘要

This paper presents an efficient, fine-grained parallel algorithm for solving the Eikonal equation on triangular meshes. The Eikonal equation, and the broader class of Hamilton-Jacobi equations to which it belongs, have a wide range of applications from geometric optics and seismology to biological modeling and analysis of geometry and images. The ability to solve such equations accurately and efficiently provides new capabilities for exploring and visualizing parameter spaces and for solving inverse problems that rely on such equations in the forward model. Efficient solvers on state-of-the-art, parallel architectures require new algorithms that are not, in many cases, optimal, but are better suited to synchronous updates of the solution. In previous work [W. K. Jeong and R. T. Whitaker, SIAM J. Sci. Comput., 30 (2008), pp. 2512-2534], the authors proposed the fast iterative method (FIM) to efficiently solve the Eikonal equation on regular grids. In this paper we extend the fast iterative method to solve Eikonal equations efficiently on triangulated domains on the CPU and on parallel architectures, including graphics processors. We propose a new local update scheme that provides solutions of first-order accuracy for both architectures. We also propose a novel triangle-based update scheme and its corresponding data structure for efficient irregular data mapping to parallel single-instruction multiple-data (SIMD) processors. We provide detailed descriptions of the implementations on a single CPU, a multicore CPU with shared memory, and SIMD architectures with comparative results against state-of-the-art Eikonal solvers.
机译:本文提出了一种有效的细粒度并行算法,用于求解三角网格上的Eikonal方程。 Eikonal方程以及它所属的更广泛的Hamilton-Jacobi方程类别,具有广泛的应用范围,从几何光学和地震学到生物学建模以及几何和图像分析。准确有效地求解此类方程的能力为探索和可视化参数空间以及解决正向模型中依赖于此类方程的逆问题提供了新的功能。最新的并行架构上的高效求解器需要在很多情况下不是最佳的新算法,但更适合同步更新解决方案。在以前的工作中[W. SIAM J. Sci。的K. Jeong和R. T. Whitaker。 [Comput。30(2008),pp。2512-2534],作者提出了一种快速迭代方法(FIM),可以有效地求解规则网格上的Eikonal方程。在本文中,我们扩展了快速迭代方法,以在CPU和并行体系结构(包括图形处理器)上的三角域上有效地求解Eikonal方程。我们提出了一种新的本地更新方案,该方案可为两种体系结构提供一阶精度的解决方案。我们还提出了一种新颖的基于三角形的更新方案及其相应的数据结构,以有效地将不规则数据映射到并行单指令多数据(SIMD)处理器。我们提供了有关单个CPU,具有共享内存的多核CPU和SIMD架构上的实现的详细说明,并与最新的Eikonal求解器进行了比较。

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