Multiple Right-hand Side Vectors Research Articles

A variational Crank–Nicolson ensemble Monte Carlo algorithm for a heat equation under uncertainty

In this paper, a new fully discrete variational Crank–Nicolson ensemble (FVE/CN) scheme is established for the heat equation with uncertainty, then a fully discrete variational Crank–Nicolson ensemble Monte Carlo algorithm (FVEMC/CN) is given. Using the FVEMC/CN algorithm, one just needs to solve a single linear system with multiple right-hand side vectors in a group at each time step. The stability and the error estimates are presented to indicate the proposed algorithm is second-order convergent regarding the time. Finally, numerical experiments are provided which illustrate the convergence theory and the efficiency of the proposed approaches.

On cost-efficient parallel iterative solvers for 3D frequency-domain seismic multisource viscoelastic anisotropic wave modeling

Solving large sparse linear systems in 3D frequency-domain seismic wave modeling, especially in viscoelastic anisotropic media, poses significant challenges due to the increasing number of discrete moduli and nonzero elements in the linear system matrix. The computational load surpasses that of acoustic or viscoacoustic media, making it even more challenging when dealing with multisource problems. Popular scientific tools for solving a linear system, such as multifrontal massively parallel sparse direct solver (MUMPS), structured matrix package (STRUMPACK), and portable extensible toolkit for scientific computation (PETSc), can be used, but their applicability to our specific problem has not been comprehensively evaluated. Our study aims to tackle the challenges in solving large, sparse, complex-valued symmetric linear systems with multiple right-hand side vectors for 3D frequency-domain seismic wave modeling. We have adopted the preconditioned conjugate gradient iterative algorithms as the foundation for our research, introducing two highly cost-effective parallel iterative solvers: the parallel symmetric successive overrelaxation conjugate gradient (P-SSORCG) and the parallel incomplete Cholesky conjugate gradient (P-ICCG). These novel solvers are subjected to a comprehensive comparative analysis against well-established scientific tools, such as MUMPS, STRUMPACK, and PETSc, in the context of 3D frequency-domain seismic wave modeling. We show their promising performances in a practical 3D SEG/EAGE overthrust model and demonstrate that the grouped P-SSORCG offers an efficient alternative to parallel direct solvers, particularly in situations wherein computational resources are limited.

Ritz Vectors-Based Deflation Preconditioner for Linear System with Multiple Right-Hand Sides

Computational mathematics have many tools to solve the large systems of equations which may be linear or nonlinear. Iterative methods are used to solve the nonsymmetric definite system of linear equations like Krylov methods. Linear systems with multiple right-hand sides find application in many areas of engineering and science. Considering generality and indefiniteness, Krylov subspace methods are frequently used for such problems. However, problem with system with multiple right-hand side vectors requires constructing subspace for every right-hand side. GMRES produces Ritz vectors, approximation to eigenvectors during iterations. These Ritz values, recycled vectors are used in Krylov solve while solving system with second and subsequent right-hand side vectors. This is applied as a deflation preconditioner to GMRES. The numerical results show that the computational time, residuals and number of iterations is reduced as compare to simple GMRES. Deflation technique with Ritz vectors is not expensive as compare to the GMRES and as well as exact eigenvectors deflations.

Revisiting performance of BiCGStab methods for solving systems with multiple right-hand sides

The paper discusses the efficiency of the classical BiCGStab method and several of its modifications for solving systems with multiple right-hand side vectors. These iterative methods are widely used for solving systems with large sparse matrices. The paper presents execution time analytical model for the time to solve the systems. The BiCGStab method and several modifications including the Reordered BiCGStab and Pipelined BiCGStab methods are analysed and the range of applicability for each method providing the best execution time is highlighted. The results of the analytical model are validated by the numerical experiments and compared with results of other authors. The presented results demonstrate an increasing role of the vector operations when performing simulations with multiple right-hand side vectors. The proposed merging of vector operations allows to reduce the memory traffic and improve performance of the calculations by about 30%.

A parallel log barrier-based mesh warping algorithm for distributed memory machines

Parallel dynamic meshes are essential for computational simulations of large-scale scientific applications involving motion. To address this need, we propose parallel LBWARP, a parallel log barrier-based tetrahedral mesh warping algorithm for distributed memory machines. Our algorithm is a general-purpose, geometric mesh warping algorithm that parallelizes the sequential LBWARP algorithm proposed by Shontz and Vavasis. The first step of the algorithm involves computation of a set of local weights for each interior node which describe the relative distances of the node to each of its neighbors. The weight computation step is the most time consuming in the parallel algorithm. Based on our choice of the mesh partition and the corresponding distribution of data and assignment of tasks to processors, communication among processors is avoided in an embarrassingly parallel computation of the weights. Once this representation of the initial mesh is determined, a target deformation of the boundary is applied, also in an embarrassingly parallel manner. Finally, new coordinates of the interior nodes are obtained by solving a system of linear equations with multiple right-hand sides that is based on the weights and boundary deformation. This linear system can be solved using one of three parallel sparse linear solvers, i.e., the distributed block BiCG, block GMRES, or LU algorithm, all of which support the solution of linear systems with multiple right-hand side vectors. Our numerical results demonstrate good efficiency and strong scalability of parallel LBWARP on up to 64 processors, as the experiments show close to linear speedup in all cases. Weak scalability is also demonstrated. The performance of the parallel sparse linear solvers is dependent on factors such as the mesh size, the amount of available memory, and the number of processors. For example, the distributed LU algorithm gives better performance on small meshes, whereas the distributed block BiCG and distributed block GMRES algorithms yield better performance when the amount of available memory is limited. Finally, we demonstrate the parallel LBWARP performance for a sequence of mesh deformations which can significantly reduce the runtime of the overall algorithm. When applied to k deformations, parallel LBWARP reuses the weight matrix, that was computed during the first deformation, when the distributed LU linear solver is employed. This gives close to k-time performance for sufficiently many deformations.

A True Domain Decomposition Procedure Based on Method of Moments to Handle Electrically Large Bodies

A true domain decomposition method to obtain numerical solution involving electrically large bodies using the method of moments (MOM) formulation is the subject matter of the present work. The electrically large body is initially divided into smaller sub-structures. By defining a set of new basis functions adaptively, utilizing the primary basis functions, a solution scheme is developed which completely eliminates the interaction of one substructure with the rest of the body. This procedure enables to solve each substructure independently and involves no iterative solution as a final step. The present method is conceptually simple and applicable to any MOM solution. Further, the method can easily handle multiple right-hand side vectors much the same way as the conventional MOM solution. A few representative numerical examples are presented to illustrate the applicability of the new method.

Nonnegative Matrix Factorization Based on Alternating Nonnegativity Constrained Least Squares and Active Set Method

Nonnegative matrix factorization (NMF) determines a lower rank approximation of a matrix $A \in \mathbb{R}^{m \times n} \approx WH$ where an integer $k \ll \min(m,n)$ is given and nonnegativity is imposed on all components of the factors $W \in \mathbb{R}^{m \times k}$ and $H \in \mathbb{R}^{k \times n}$. NMF has attracted much attention for over a decade and has been successfully applied to numerous data analysis problems. In applications where the components of the data are necessarily nonnegative, such as chemical concentrations in experimental results or pixels in digital images, NMF provides a more relevant interpretation of the results since it gives nonsubtractive combinations of nonnegative basis vectors. In this paper, we introduce an algorithm for NMF based on alternating nonnegativity constrained least squares (NMF/ANLS) and the active set-based fast algorithm for nonnegativity constrained least squares with multiple right-hand side vectors, and we discuss its convergence properties and a rigorous convergence criterion based on the Karush-Kuhn-Tucker (KKT) conditions. In addition, we also describe algorithms for sparse NMFs and regularized NMF. We show how we impose a sparsity constraint on one of the factors by $L_1$-norm minimization and discuss its convergence properties. Our algorithms are compared to other commonly used NMF algorithms in the literature on several test data sets in terms of their convergence behavior.

An implicit, exact dual adjoint solution method for turbulent flows on unstructured grids

An implicit algorithm for solving the discrete adjoint system based on an unstructured-grid discretization of the Navier–Stokes equations is presented. The method is constructed such that an adjoint solution exactly dual to a direct differentiation approach is recovered at each time step, yielding a convergence rate which is asymptotically equivalent to that of the primal system. The new approach is implemented within a three-dimensional unstructured-grid framework and results are presented for inviscid, laminar, and turbulent flows. Improvements to the baseline solution algorithm, such as line-implicit relaxation and a tight coupling of the turbulence model, are also presented. By storing nearest-neighbor terms in the residual computation, the dual scheme is computationally efficient, while requiring twice the memory of the flow solution. The current implementation allows for multiple right-hand side vectors, enabling simultaneous adjoint solutions for several cost functions or constraints with minimal additional storage requirements, while reducing the solution time compared to serial applications of the adjoint solver. The scheme is expected to have a broad impact on computational problems related to design optimization as well as error estimation and grid adaptation efforts.

An implicit, exact dual adjoint solution method for turbulent flows on unstructured grids

An implicit algorithm for solving the discrete adjoint system based on an unstructured-grid discretization of the Navier–Stokes equations is presented. The method is constructed such that an adjoint solution exactly dual to a direct differentiation approach is recovered at each time step, yielding a convergence rate which is asymptotically equivalent to that of the primal system. The new approach is implemented within a three-dimensional unstructured-grid framework and results are presented for inviscid, laminar, and turbulent flows. Improvements to the baseline solution algorithm, such as line-implicit relaxation and a tight coupling of the turbulence model, are also presented. By storing nearest-neighbor terms in the residual computation, the dual scheme is computationally efficient, while requiring twice the memory of the flow solution. The current implementation allows for multiple right-hand side vectors, enabling simultaneous adjoint solutions for several cost functions or constraints with minimal additional storage requirements, while reducing the solution time compared to serial applications of the adjoint solver. The scheme is expected to have a broad impact on computational problems related to design optimization as well as error estimation and grid adaptation efforts.

TRIDIAGONAL SOLVERS WITH MULTIPLE RIGHT HAND SIDES ON k-DIMENSIONAL MESH AND TORUS INTERCONNECTION NETWORKS

We consider the problem of designing optimal and efficient algorithms for solving tridiagonal linear systems with multiple right-hand side vectors on k-dimensional mesh and torus interconnection networks. We derive asymptotic upper and lower bounds for these solvers using odd-even cyclic reduction. We present various important bounds on execution time including general lower bounds which are independent of initial data assignment, and lower bounds based on classifying assignments via the proportion of initial data assigned amongst processors. Finally, different algorithms are designed in order to achieve running times that are within a small constant factor of the lower bounds provided.

Complexity Issues on Designing Tridiagonal Solvers on 2-Dimensional Mesh Interconnection Networks

We consider the problem of designing optimal and efficient algorithms for solving tridiagonal linear systems with multiple right-hand side vectors on two-dimensional mesh interconnection networks. We derive asymptotic upper and lower bounds for these solvers using odd-even cyclic reduction. We present various important lower bounds on execution time for solving these systems including general lower bounds which are independent of initial data assignment, and lower bounds based on classifications of initial data assignments which classify assignments via the proportion of initial data assigned amongst processors. Finally, different algorithms are designed in order to achieve running times that are within a small constant factor of the lower bounds provided.

Application of a block modified Chebyshev algorithm to the iterative solution of symmetric linear systems with multiple right hand side vectors

An adaptive Richardson iteration method is described for the solution of large sparse symmetric positive definite linear systems of equations with multiple right-hand side vectors. This scheme ``learns'' about the linear system to be solved by computing inner products of residual matrices during the iterations. These inner products are interpreted as block modified moments. A block version of the modified Chebyshev algorithm is presented which yields a block tridiagonal matrix from the block modified moments and the recursion coefficients of the residual polynomials. The eigenvalues of this block tridiagonal matrix define an interval, which determines the choice of relaxation parameters for Richardson iteration. Only minor modifications are necessary in order to obtain a scheme for the solution of symmetric indefinite linear systems with multiple right-hand side vectors. We outline the changes required.