论文信息 - A Parallel Sparse QR-Factorization Algorithm

A Parallel Sparse QR-Factorization Algorithm

A sparse QR-factorization algorithm for coarse-grain parallel computations is described. Initially the coefficient matrix, which is assumed to be general sparse, is reordered properly in an attempt to bring as many zero elements in the lower left corner as possible. Then the matrix is partitioned into large blocks of rows and Givens rotations are applied in each block. These are independent tasks and can be done in parallel. Row and column permutations are carried out within the blocks to exploit the sparsity of the matrix.

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