Abstract
An important recent development in the area of solution of general sparse systems of linear equations has been the introduction of new algorithms that allow complete decoupling of symbolic and numerical phases of sparse Gaussian elimination with partial pivoting. This enables efficient solution of a series of sparse systems with the same nonzero pattern but different coefficient values, which is a fairly common situation in practical applications. This paper reports on a shared- and distributed-memory parallel general sparse solver based on these new symbolic and unsymmetric-pattern multifrontal algorithms.
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Gupta, A. A Shared- and distributed-memory parallel general sparse direct solver. AAECC 18, 263–277 (2007). https://doi.org/10.1007/s00200-007-0037-x
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DOI: https://doi.org/10.1007/s00200-007-0037-x