Mixing LU and QR factorization algorithms to design high-performance dense linear algebra solvers

This paper introduces hybrid LU–QR algorithms for solving dense linear systems of the form Ax = b. Throughout a matrix factorization, these algorithms 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 stepwhen a robustness criterion detects some risk for instability, and they execute an LU step otherwise. The choice between LU andQR stepsmust have a small computational overhead andmust provide a satisfactory level of stability with as fewQR steps as possible. In this paper, we introduce several robustness criteria andwe 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 extension 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. A comprehensive set of experiments shows that hybrid LU–QR algorithmsprovide a continuous range of trade-offs between stability and performances. © 2015 Published by Elsevier Inc.