Abstract
In this paper, we consider the symbolic factorization step in computing the Cholesky factorization of a sparse symmetric positive definite matrix on distributed-memory multiprocessor systems. By exploiting the supernodal structure in the Cholesky factor, the performance of a previous parallel symbolic factorization algorithm is improved. Empirical tests demonstrate that there can be drastic reduction in the execution time required by the new algorithm on an Intel iPSC/2 hypercube.
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