We present a solver for the 2D high-frequency Helmholtz equation in heterogeneous acoustic media, with online parallel complexity that scales optimally as @math , where @math is the number of volume unknowns, and @math is the number of processors, as long as @math grows at most like a small fractional power of @math . The solver decomposes the domain into layers, and uses transmission conditions in boundary integral form to explicitly define "polarized traces", i.e., up- and down-going waves sampled at interfaces. Local direct solvers are used in each layer to precompute traces of local Green's functions in an embarrassingly parallel way (the offline part), and incomplete Green's formulas are used to propagate interface data in a sweeping fashion, as a preconditioner inside a GMRES loop (the online part). Adaptive low-rank partitioning of the integral kernels is used to speed up their application to interface data. The method uses second-order finite differences. The complexity scalings are empirical but motivated by an analysis of ranks of off-diagonal blocks of oscillatory integrals. They continue to hold in the context of standard geophysical community models such as BP and Marmousi 2, where convergence occurs in 5 to 10 GMRES iterations.
, developped an additive Schwarz iteration coupled with coarse grid preconditioner based on an eigenvalue decomposition of the DtN maps inside each subdomain @cite_72 ;
@cite_72
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A new domain decomposition method is presented for the exterior Helmholtz problem. The nonlocal Dirichlet-to-Neumann (DtN) map is used as a nonreflecting condition on the outer computational boundary. The computational domain is divided into nonoverlapping subdomains with Sommerfeld-type conditions on the adjacent subdomain boundaries to ensure uniqueness. An iterative scheme is developed, where independent subdomain boundary-value problems are obtained by applying the DtN operator to values from the previous iteration. The independent problems are then discretized with finite elements and can be solved concurrently. Numerical results are presented for a two-dimensional model problem, and both the solution accuracy and convergence rate are investigated. Accelerating large-scale linear solvers is always crucial for scientific research and industrial applications. In this regard, preconditioners play a key role in improving the performance of iterative linear solvers. This paper presents a summary and review of our work about the development of parallel ILU preconditioners on GPUs. The mechanisms of ILU(0), ILU(k), ILUT, enhanced ILUT, and block-wise ILU(k) are reviewed and analyzed, which give a clear guidance in the development of iterative linear solvers. ILU(0) is the most commonly used preconditioner, and the nonzero pattern of its matrix is exactly the same as the original matrix to be solved. ILU(k) uses k levels to control the pattern of its preconditioner matrix. ILUT selects entries for its preconditioner matrix by setting thresholds without considering its original matrix pattern. In addition to point-wise ILU preconditioners, a block-wise ILU(k) preconditioner is designed delicately in support of block-wise matrices. In implementation, the RAS (Restricted Additive Schwarz) method is adopted to optimize the parallel structure of a preconditioner matrix. Coupling with the configuration parameters of ILU preconditioners, a complex situation appears in the parallel solution process, so decoupled algorithms are adopted. These algorithms are implemented and tested on NVIDIA GPUs. The experiment results show that a single-GPU implementation can speed up an ILU preconditioner by a factor of 10, compared to traditional CPU implementation. The results also show that the ILU(0) has better speedup than ILU(k) but slower convergence than ILU(k). Level k of ILU(k) and threshold (p, t) of ILUT are effective adjustment factors for controlling the equilibrium point between acceleration and convergence for ILU(k) and ILUT, respectively. All these ILU preconditioners are characterized and compared in this work, which shows a clear picture and numerical insights for practitioners in the ILU family. An efficient power flow solution is highly important for power system analyses. Traditionally, LU decomposition-based direct method is dominant in solving linear systems in the power flow analysis. However, with the increasing scale and complexity, direct methods may suffer scalability issues, especially under parallel implementation. Therefore, iterative methods such as Conjugate Gradient (CG) are gaining more attention for their scalability and feasibility for parallelisation. To efficiently apply an iterative solver like CG, a preconditioner is usually required. A polynomial-based Chebyshev preconditioner is discussed in this work. An essential parameter in Chebyshev preconditioning is the maximum eigenvalue of the linear system. However, direct calculation of the eigenvalues is too time-consuming to be practical. Therefore, this work proposes a method to estimate the largest eigenvalue to save the time spent on eigenvalue calculation. Thus, Chebyshev preconditioning will be practical to use in power system analyses. This work first proves that the eigenvalues of power system applications range within (0, 2] after a normalisation step, then demonstrates the eigenvalue estimation accuracy, and finally presents the performance improvement. The test shows an average 98.92 runtime saving for the Chebyshev preconditioner, and a 40.99 runtime saving for precondition and iterative solving process. a b s t r a c t Traditionally, linear equations in power system applications are solved by direct methods based on LU decomposition. With the development of advanced power system controls, the industrial and research community is more interested in simulating larger, interconnected power grids. Iterative methods such as the conjugate gradient method have been applied to power system applications in the literature for its parallelism potential with larger systems. Preconditioner, used for preconditioning the linear system for a better convergence rate in iterative computations, is an indispensable part of iterative solving process. This work implemented a polynomial preconditioner Chebyshev preconditioner with graphic processing unit (GPU), and integrated a GPU-based conjugate gradient solver. Results show that GPU-based Chebyshev preconditioner can reach around 46× speedup for the largest test system, and conjugate gradient can gain more than 4× speedup. This demonstrates great potentials for GPU application in power system