Abstract
In this paper we present a new stable algorithm for the parallel QR-decomposition of "tall and skinny" matrices. The algorithm has been developed for the dense symmetric eigensolver ELPA, where the QR-decomposition of tall and skinny matrices represents an important substep. Our new approach is based on the fast but unstable CholeskyQR algorithm (Stathopoulos and Wu, 2002) [1]. We show the stability of our new algorithm and provide promising results of our MPI-based implementation on a BlueGene/P and a Power6 system.
Original language | English |
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Pages (from-to) | 186-194 |
Number of pages | 9 |
Journal | Parallel Computing |
Volume | 40 |
Issue number | 7 |
DOIs | |
State | Published - Jul 2014 |
Keywords
- Eigenvalue and eigenvector computation
- Parallelization
- QR-decomposition
- Two-step tridiagonalization