Designing LU-QR hybrid solvers for performance and stabilityReport as inadecuate

Designing LU-QR hybrid solvers for performance and stability - Download this document for free, or read online. Document in PDF available to download.

1 HiePACS - High-End Parallel Algorithms for Challenging Numerical Simulations LaBRI - Laboratoire Bordelais de Recherche en Informatique, Inria Bordeaux - Sud-Ouest 2 LaBRI - Laboratoire Bordelais de Recherche en Informatique 3 LIP - Laboratoire de l-Informatique du Parallélisme 4 ROMA - Optimisation des ressources : modèles, algorithmes et ordonnancement Inria Grenoble - Rhône-Alpes, LIP - Laboratoire de l-Informatique du Parallélisme 5 Department of Mathematical and Statistical Sciences 6 ICL - Innovative Computing Laboratory Knoxville

Abstract : This paper introduces hybrid LU-QR al- gorithms for solving dense linear systems of the form Ax = b. Throughout a matrix factorization, these al- gorithms dynamically alternate LU with local pivoting and QR elimination steps, based upon some robustness criterion. LU elimination steps can be very efficiently parallelized, and are twice as cheap in terms of floating- point operations, as QR steps. However, LU steps are not necessarily stable, while QR steps are always stable. The hybrid algorithms execute a QR step when a robustness criterion detects some risk for instability, and they execute an LU step otherwise. Ideally, the choice between LU and QR steps must have a small computational overhead and must provide a satisfactory level of stability with as few QR steps as possible. In this paper, we introduce several robustness criteria and we establish upper bounds on the growth factor of the norm of the updated matrix incurred by each of these criteria. In addition, we describe the implementation of the hybrid algorithms through an exten- sion of the PaRSEC software to allow for dynamic choices during execution. Finally, we analyze both stability and performance results compared to state-of-the-art linear solvers on parallel distributed multicore platforms.

Author: Mathieu Faverge - Julien Herrmann - Julien Langou - Bradley Lowery - Yves Robert - Jack Dongarra -



Related documents