Fast Parallel Solver for the Space-time IgA-DG Discretization of the Diffusion Equation

We consider the space-time discretization of the diffusion equation, using an isogeometric analysis (IgA) approximation in space and a discontinuous Galerkin (DG) approximation in time. Drawing inspiration from a former spectral analysis, we propose for the resulting space-time linear system a multigrid preconditioned GMRES method, which combines a preconditioned GMRES with a standard multigrid acting only in space. The performance of the proposed solver is illustrated through numerical experiments, which show its competitiveness in terms of iteration count, run-time and parallel scaling.

Developing a Multi-GPU-Enabled Preconditioned GMRES with Inexact Triangular Solves for Block Sparse Matrices

Solving triangular systems is the building block for preconditioned GMRES algorithm. Inexact preconditioning becomes attractive because of the feature of high parallelism on accelerators. In this paper, we propose and implement an iterative, inexact block triangular solve on multi-GPUs based on PETSc’s framework. In addition, by developing a distributed block sparse matrix-vector multiplication procedure and investigating the optimized vector operations, we form the multi-GPU-enabled preconditioned GMRES with the block Jacobi preconditioner. In the implementation, the GPU-Direct technique is employed to avoid host-device memory copies. The preconditioning step used by PETSc’s structure and the cuSPARSE library are also investigated for performance comparisons. The experiments show that the developed GMRES with inexact preconditioning on 8 GPUs can achieve up to 4.4x speedup over the CPU-only implementation with exact preconditioning using 8 MPI processes.

Resolution of Time-Dependent Navier-Stokes Equations with a new Boundary Condition

In this work, a numerical solution of the unsteady incompressible Navier- Stokes equations with a new boundary condition is proposed. The method suggested is based on an algorithm of discretization by finite element method in space and the Euler full-implicit scheme in time. The matrix system is solved at each iteration with a preconditioned GMRES method. Also, we proposed two types of a posteriori error indicator, with one being for the time discretization and the other for the space discretization. We prove the equivalence between the sum of the two types of error indicators and the full error. In order to evaluate the performance of the method, the numerical results of two-dimensional backward-facing step flow are compared with some previously published works or with others coming from commercial code like ADINA (Automatic Dynamic Incremental Nonlinear Analysis) system.

New modified shift-splitting preconditioners for non-symmetric saddle point problems

Zhou et al. and Huang et al. have proposed the modified shift-splitting (MSS) preconditioner and the generalized modified shift-splitting (GMSS) for non-symmetric saddle point problems, respectively. They have used symmetric positive definite and skew-symmetric splitting of the (1, 1)-block in a saddle point problem. In this paper, we use positive definite and skew-symmetric splitting instead and present new modified shift-splitting (NMSS) method for solving large sparse linear systems in saddle point form with a dominant positive definite part in (1, 1)-block. We investigate the convergence and semi-convergence properties of this method for nonsingular and singular saddle point problems. We also use the NMSS method as a preconditioner for GMRES method. The numerical results show that if the (1, 1)-block has a positive definite dominant part, the NMSS-preconditioned GMRES method can cause better performance results compared to other preconditioned GMRES methods such as GMSS, MSS, Uzawa-HSS and PU-STS. Meanwhile, the NMSS preconditioner is made for non-symmetric saddle point problems with symmetric and non-symmetric (1, 1)-blocks.