Multifrontral multithreaded rank-revealing sparse QR factorization
Timothy A. Davis · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · 2009
SuiteSparseQR is a sparse multifrontal QR factorization algorithm. Dense matrix methods within each frontal matrix enable the method to obtain high performance on multicore architectures. Parallelism across different frontal matrices is handled with Intel's Threading Building Blocks library. Rank-detection is performed within each frontal matrix using Heath's method, which does not require column pivoting. The resulting sparse QR factorization obtains a substantial fraction of the theoretical peak performance of a multicore computer.