122 research outputs found

    Shared Memory Pipelined Parareal

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    For the parallel-in-time integration method Parareal, pipelining can be used to hide some of the cost of the serial correction step and improve its efficiency. The paper introduces an OpenMP implementation of pipelined Parareal and compares it to a standard MPI-based variant. Both versions yield almost identical runtimes, but, depending on the compiler, the OpenMP variant consumes about 7% less energy and has a significantly smaller memory footprint. However, its higher implementation complexity might make it difficult to use in legacy codes and in combination with spatial parallelisation

    Block Jacobi relaxation for plane wave discontinuous Galerkin methods

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    In recent years plane wave approximation methods have become popular for the solution of Helmholtz problems, where instead of standard polynomial basis functions plane waves are used on each element to approximate the solution. One possibility to enforce inter-element continuity conditions for these basis functions is to use the plane wave discontinuous Galerkin Method (PWDG). In this paper we investigate block Jacobi relaxation methods for the PWDG. We show that for a certain choice of flux parameters in the PWDG block Jacobi is identical to a Schwarz method with standard impedance boundary conditions. This result motivates a simple algebraic decomposition method whose numerical performance is demonstrated for various wavenumbers. For high-frequency problem it is important to choose optimized transmission conditions between subdomains, and a first result of how to modify fluxes to incorporate optimized transmission conditions is presented

    Fast interior point solution of quadratic programming problems arising from PDE-constrained optimization

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    Interior point methods provide an attractive class of approaches for solving linear, quadratic and nonlinear programming problems, due to their excellent efficiency and wide applicability. In this paper, we consider PDE-constrained optimization problems with bound constraints on the state and control variables, and their representation on the discrete level as quadratic programming problems. To tackle complex problems and achieve high accuracy in the solution, one is required to solve matrix systems of huge scale resulting from Newton iteration, and hence fast and robust methods for these systems are required. We present preconditioned iterative techniques for solving a number of these problems using Krylov subspace methods, considering in what circumstances one may predict rapid convergence of the solvers in theory, as well as the solutions observed from practical computations

    Scaling up genetic circuit design for cellular computing:advances and prospects

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