162,431 research outputs found
Parallel minimum norm solution of sparse block diagonal column overlapped underdetermined systems
Underdetermined systems of equations in which the minimum norm solution needs to be computed arise in many applications, such as geophysics, signal processing, and biomedical engineering. In this article, we introduce a new parallel algorithm for obtaining the minimum 2-norm solution of an underdetermined system of equations. The proposed algorithm is based on the Balance scheme, which was originally developed for the parallel solution of banded linear systems. The proposed scheme assumes a generalized banded form where the coefficient matrix has column overlapped block structure in which the blocks could be dense or sparse. In this article, we implement the more general sparse case. The blocks can be handled independently by any existing sequential or parallel QR factorization library. A smaller reduced system is formed and solved before obtaining the minimum norm solution of the original system in parallel. We experimentally compare and confirm the error bound of the proposed method against the QR factorization based techniques by using true single-precision arithmetic. We implement the proposed algorithm by using the message passing paradigm. We demonstrate numerical effectiveness as well as parallel scalability of the proposed algorithm on both shared and distributed memory architectures for solving various types of problems. © 2017 ACM
An efficient parallel immersed boundary algorithm using a pseudo-compressible fluid solver
We propose an efficient algorithm for the immersed boundary method on
distributed-memory architectures, with the computational complexity of a
completely explicit method and excellent parallel scaling. The algorithm
utilizes the pseudo-compressibility method recently proposed by Guermond and
Minev [Comptes Rendus Mathematique, 348:581-585, 2010] that uses a directional
splitting strategy to discretize the incompressible Navier-Stokes equations,
thereby reducing the linear systems to a series of one-dimensional tridiagonal
systems. We perform numerical simulations of several fluid-structure
interaction problems in two and three dimensions and study the accuracy and
convergence rates of the proposed algorithm. For these problems, we compare the
proposed algorithm against other second-order projection-based fluid solvers.
Lastly, the strong and weak scaling properties of the proposed algorithm are
investigated
A Parallel Riccati Factorization Algorithm with Applications to Model Predictive Control
Model Predictive Control (MPC) is increasing in popularity in industry as
more efficient algorithms for solving the related optimization problem are
developed. The main computational bottle-neck in on-line MPC is often the
computation of the search step direction, i.e. the Newton step, which is often
done using generic sparsity exploiting algorithms or Riccati recursions.
However, as parallel hardware is becoming increasingly popular the demand for
efficient parallel algorithms for solving the Newton step is increasing. In
this paper a tailored, non-iterative parallel algorithm for computing the
Riccati factorization is presented. The algorithm exploits the special
structure in the MPC problem, and when sufficiently many processing units are
available, the complexity of the algorithm scales logarithmically in the
prediction horizon. Computing the Newton step is the main computational
bottle-neck in many MPC algorithms and the algorithm can significantly reduce
the computation cost for popular state-of-the-art MPC algorithms
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