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A single-level implementation for a fast direct method of moments solver on electrically large scattering problems using a GPU based Reduced Singular Value Decomposition block LU factorization

机译:使用基于GPU的奇异值分解块LU分解的电大散射问题的快速直接矩解法的单层实现

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In recent years there has been considerable advancement in solving large-scale electromagnetic scattering problems using fast direct solve techniques with the traditional Rao-Wilton-Glisson (RWG) Method of Moments (MoM) computational framework, and extensions to Higher Order Basis Functions (HOBF) over curvilinear elements. The direct solve techniques are typically formulated with compression algorithms such as Adaptive-Cross-Approximation (ACA/ACA+). Early attempts for PEC bodies on the CPU (J. Shaeffer, IEEE Trans. Ant. and Prop., vol. 56, no. 8, pp. 2306–2313, Aug. 2008) and dielectric composite bodies on both the CPU and GPU (M. A. Horn, T. N. Killian, and D. L. Faircloth, 2014 IEEE Ant. and Prop. Society International Symposium (APSURSI), Memphis, TN, 2014, pp. 1630–1631) implemented a Single-level (SL) block clustering scheme in which ACA/ACA+ is used for compression in both the fill and block LU factorization. More recent attempts implement Multi-level (ML) block clustering schemes utilizing hierarchical matrix (H -Matrix) theory (W. Chai and D. Jiao, 2010 IEEE Ant. and Prop. Society International Symposium (APSURSI), Toronto, ON, 2010, pp. 1–4). ML schemes rely on the compression preserving properties of the Reduced Singular Value Decomposition (rSVD) and thus do not employ ACA/ACA+ during block LU factorization.
机译:近年来,使用快速的直接求解技术和传统的Rao-Wilton-Glisson(RWG)矩量法(MoM)计算框架来解决大规模电磁散射问题有了很大的进步,并且扩展了高阶基函数(HOBF) )在曲线元素上。直接求解技术通常由诸如自适应交叉近似(ACA / ACA +)之类的压缩算法来制定。 CPU上PEC主体的早期尝试(J.Shaeffer,IEEE Trans.Ant。and Prop。,第56卷,第8期,第2306-2313页,2008年8月)以及CPU和GPU上的介电复合材料主体(MA Horn,TN Killian和DL Faircloth,2014 IEEE Ant。and Prop。Society International Symposium(APSURSI),TN,孟菲斯,2014,pp。1630–1631)实现了单级(SL)块聚类方案,其中ACA / ACA +用于填充和块LU分解中的压缩。最近的尝试利用层次矩阵(H -Matrix)理论来实现多级(ML)块聚类方案(W. Chai和D. Jiao,2010年,IEEE Ant。and Prop。Society International Symposium(APSURSI),多伦多,安大略省,2010年,第1-4页)。 ML方案依赖于降奇异值分解(rSVD)的压缩保留属性,因此在块LU分解期间不采用ACA / ACA +。

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