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A New Software and Hardware Parallelized Floating Random-Walk Algorithm for the Modified Helmholtz Equation Subject to Neumann and Mixed Boundary Conditions

机译:修正的Helmholtz方程在Neumann和混合边界条件下的新的软硬件并行浮动随机游动算法

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A new floating random-walk algorithm for the one-dimensional modified Helmholtz equation subject to Neumann and mixed boundary conditions problems is developed in this paper. Traditional floating random-walk algorithms for Neumann and mixed boundary condition problems have involved "reflecting boundaries" resulting in relatively large computational times. In a recent paper, we proposed the elimination of the use of reflecting boundaries through the use of novel Green's functions that mimic the boundary conditions of the problem of interest. The methodology was validated by a solution of the one-dimensional Laplace's equation. In this paper, we extend the methodology to the floating random-walk solution of the one-dimensional modified Helmholtz equation, and excellent agreement has been obtained between an analytical solution and floating random-walk results. The algorithm has been parallelized and a near linear rate of parallelization has been obtained with as many as thirty-two processors. These results have previously been published in [1]. In addition, a GPU implementation employing 4096 simultaneous threads displayed a similar near-linear parallelization gain and a one to two orders of magnitude improvement over the CPU implementation. An immediate application of this research is in the numerical solution of the electromagnetic diffusionrnequation in magnetically permeable and electrically conducting objects with applications in dielectrometry and magnetometry sensors that have the ability to detect sub-surface objects such as landmines. The ultimate goal of this research is the application of this methodology to the solution of aerodynamical flow problems.
机译:针对一维修正的亥姆霍兹方程,考虑了诺伊曼和混合边界条件问题,提出了一种新的浮动随机游走算法。用于Neumann和混合边界条件问题的传统浮动随机游走算法涉及“反射边界”,导致计算时间相对较长。在最近的一篇论文中,我们提出了通过使用新颖的格林函数来模仿所关注问题的边界条件,从而消除反射边界的使用。通过一维拉普拉斯方程的解验证了该方法。在本文中,我们将方法扩展到一维修正Helmholtz方程的浮动随机游动解,并且在解析解和浮动随机游动结果之间取得了极好的一致性。该算法已经并行化,并且使用多达32个处理器获得了接近线性的并行化速率。这些结果先前已发表在[1]中。此外,采用4096个并发线程的GPU实现显示出相似的近线性并行化增益,并且与CPU实现相比提高了一到两个数量级。这项研究的直接应用是在磁导和导电物体中的电磁扩散方程的数值解,以及在具有检测地下物体(例如地雷)能力的介电和磁力传感器中的应用。这项研究的最终目标是将这种方法学应用于解决空气动力学流动问题。

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