J. Chen
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The research is motivated by the growing computational demands in seismic imaging and other wave propagation applications, where increasing frequencies and larger domains necessitate more efficient solution strategies. Traditional approaches often struggle with the combined challenges of wavenumber-dependent convergence, pollution errors, and substantial memory requirements, particularly for three-dimensional problems in heterogeneous media.
This work presents a comprehensive framework for solving large-scale Helmholtz problems through matrix-free parallel implementations of preconditioned iterative methods. The framework combines Complex Shifted Laplace Preconditioner (CSLP) with advanced deflation techniques, implemented in a manner that eliminates the need for explicit matrix storage while maintaining computational efficiency. A key innovation is the development of matrix-free implementations for higher-order deflation methods combined with the CSLP preconditioner, achieved through carefully designed re-discretization schemes that preserve the advantages of Galerkin coarsening.
The methodology progresses from two-dimensional implementations to fully three-dimensional frameworks, incorporating increasingly sophisticated preconditioning techniques. A significant achievement is the development of a matrix-free parallel multilevel deflation preconditioner that exhibits near wavenumber-independent convergence while maintaining excellent parallel scalability. The implementation utilizes a hybrid MPI+OpenMP parallelization strategy, effectively addressing both computational and memory challenges in extreme-scale scenarios.
Extensive numerical experiments validate the effectiveness of these methods across a range of problem types, from academic test cases to industrial-scale applications. Notably, the framework successfully resolves a challenging seismic model, involving approximately 3.8 billion degrees of freedom, while achieving 86\% parallel efficiency when scaling to 2304 CPU cores. This demonstration of practical viability for large-scale heterogeneous problems represents a significant advance in computational capabilities for seismic imaging applications.
The research makes several fundamental contributions to the field of numerical analysis and scientific computing. First, it establishes new approaches for matrix-free implementation of state-of-the-art preconditioners, significantly reducing memory requirements while maintaining numerical efficiency. Second, it demonstrates the achievement of close-to wavenumber-independent convergence through carefully designed deflation strategies in a parallel computing environment. Third, it provides a comprehensive framework for solving extreme-scale Helmholtz problems that combines numerical robustness with practical applicability.
The methodologies developed in this work contribute to the broader field of scientific computing, demonstrating how careful algorithm design, combined with modern computing architectures, can address previously intractable problems in wave propagation modeling. ...
The research is motivated by the growing computational demands in seismic imaging and other wave propagation applications, where increasing frequencies and larger domains necessitate more efficient solution strategies. Traditional approaches often struggle with the combined challenges of wavenumber-dependent convergence, pollution errors, and substantial memory requirements, particularly for three-dimensional problems in heterogeneous media.
This work presents a comprehensive framework for solving large-scale Helmholtz problems through matrix-free parallel implementations of preconditioned iterative methods. The framework combines Complex Shifted Laplace Preconditioner (CSLP) with advanced deflation techniques, implemented in a manner that eliminates the need for explicit matrix storage while maintaining computational efficiency. A key innovation is the development of matrix-free implementations for higher-order deflation methods combined with the CSLP preconditioner, achieved through carefully designed re-discretization schemes that preserve the advantages of Galerkin coarsening.
The methodology progresses from two-dimensional implementations to fully three-dimensional frameworks, incorporating increasingly sophisticated preconditioning techniques. A significant achievement is the development of a matrix-free parallel multilevel deflation preconditioner that exhibits near wavenumber-independent convergence while maintaining excellent parallel scalability. The implementation utilizes a hybrid MPI+OpenMP parallelization strategy, effectively addressing both computational and memory challenges in extreme-scale scenarios.
Extensive numerical experiments validate the effectiveness of these methods across a range of problem types, from academic test cases to industrial-scale applications. Notably, the framework successfully resolves a challenging seismic model, involving approximately 3.8 billion degrees of freedom, while achieving 86\% parallel efficiency when scaling to 2304 CPU cores. This demonstration of practical viability for large-scale heterogeneous problems represents a significant advance in computational capabilities for seismic imaging applications.
The research makes several fundamental contributions to the field of numerical analysis and scientific computing. First, it establishes new approaches for matrix-free implementation of state-of-the-art preconditioners, significantly reducing memory requirements while maintaining numerical efficiency. Second, it demonstrates the achievement of close-to wavenumber-independent convergence through carefully designed deflation strategies in a parallel computing environment. Third, it provides a comprehensive framework for solving extreme-scale Helmholtz problems that combines numerical robustness with practical applicability.
The methodologies developed in this work contribute to the broader field of scientific computing, demonstrating how careful algorithm design, combined with modern computing architectures, can address previously intractable problems in wave propagation modeling.
We present a matrix-free parallel scalable multilevel deflation preconditioned method for heterogeneous time-harmonic wave problems. Building on the higher-order deflation preconditioning proposed by Dwarka and Vuik (SIAM J. Sci. Comput. 42(2):A901-A928, 2020; J. Comput. Phys. 469:111327, 2022) for highly indefinite time-harmonic waves, we adapt these techniques for parallel implementation in the context of solving large-scale heterogeneous problems with minimal pollution error. Our proposed method integrates the Complex Shifted Laplacian preconditioner with deflation approaches. We employ higher-order deflation vectors and re-discretization schemes derived from the Galerkin coarsening approach for a matrix-free parallel implementation. We suggest a robust and efficient configuration of the matrix-free multilevel deflation method, which yields a close to wavenumber-independent convergence and good time efficiency. Numerical experiments demonstrate the effectiveness of our approach for increasingly complex model problems. The matrix-free implementation of the preconditioned Krylov subspace methods reduces memory consumption, and the parallel framework exhibits satisfactory parallel performance and weak parallel scalability. This work represents a significant step towards developing efficient, scalable, and parallel multilevel deflation preconditioning methods for large-scale real-world applications in wave propagation.
We propose a matrix-free parallel two-level deflation method combined with the Complex Shifted Laplacian Preconditioner (CSLP) for two-dimensional heterogeneous Helmholtz problems encountered in seismic exploration, antennas, and medical imaging. These problems pose challenges in terms of accuracy and convergence due to scalability issues with numerical solvers. Motivated by the limitations imposed by excessive computational time and memory constraints when employing a sequential solver with constructed matrices, we parallelize the two-level deflation method without constructing any matrices. Our approach utilizes preconditioned Krylov subspace methods and approximates the CSLP preconditioner with a parallel geometric multigrid V-cycle. For the two-level deflation, standard inter-grid deflation vectors and further high-order deflation vectors are considered. As another main contribution, the matrix-free Galerkin coarsening approach and a novel re-discretization scheme as well as high-order finite-difference schemes on the coarse grid are studied to obtain wavenumber-independent convergence. The optimal settings for an efficient coarse-grid problem solver are investigated. Numerical experiments of model problems show that the wavenumber independence has been obtained for medium wavenumbers. The matrix-free parallel framework shows satisfactory weak and strong parallel scalability.
In this paper, the trailing edge noise generated by a 2D airfoil around the critical angle of attack for vortex shedding is numerically investigated using an in-house code with high accuracy and efficiency. In the present method, a fourth-order upwind compact finite-difference scheme with dispersion relation preserving (DRP) property is applied for the convection terms, and a fourth-order Runge-Kutta scheme is used for temporal discretization. The reflection of sound on the boundary is suppressed with Navier-Stokes characteristics boundary condition (NSCBC). To improve computational efficiency, a novel parallel computing strategy for the high-order compact schemes is employed. Thus, direct numerical simulation (DNS) can be realized for the flows of low Reynolds number (Re), while implicit large eddy simulation (ILES) would be carried for the flows of high Reynolds number. The present numerical method is validated by comparing the lift coefficient, drag coefficient and Strouhal number (St) to the previous publications. Based on the high accuracy and high-fidelity method, the flow field and sound field of a two-dimensional NACA0012 airfoil around critical angle of attack (AoA) at Re = 1000 are simultaneously solved. The results indicate that sound source is dipole centered at the surface of the airfoil at vortex shedding frequency, and is dipole, quadrupole or more complex sources located at the wake close to the trailing edge at higher order frequencies. These findings will help to improve understanding about the generation and propagation mechanisms of trailing edge noises at low Reynolds number.
In this paper, based on the boundary approximation approach for parallelization of the compact difference schemes, a novel strategy for the sub-domain boundary approximation schemes is proposed to maintain consistent accuracy and dispersion with the compact scheme in the interior points. In this strategy, not only the order of accuracy of the sub-domain boundary scheme is the same as the interior scheme, but the coefficient of the first truncation error term is also equal to that of the internal scheme. Furthermore, to realize the consistent dispersion performance for a class of high order upwind compact schemes, which usually include two expressions, we modify the opposite expression to be the sub-domain boundary scheme. As an example of application, the present strategy is applied to a fourth-order upwind compact scheme, and its accuracy is verified by a numerical test. The resolution and efficiency of the newly proposed parallel method are examined by four numerical examples, including propagation of a wave-packet, convection of isentropic vortex, Rayleigh–Taylor instability problems, and propagation of Gauss pulse. The results obtained demonstrate that the present strategy for compact difference schemes has the feasibility to solve the flow problems with high accuracy, resolution and efficiency in parallel computation.