【摘 要】
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We present an efficient code for solving large sparse linear systems using the multifrontal method with hierarchically semi-separable(HSS)matrices.The low rank compression in HSS limits fill-in and re
【机 构】
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Lawrence Berkeley National Lab Lawrence Berkeley
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We present an efficient code for solving large sparse linear systems using the multifrontal method with hierarchically semi-separable(HSS)matrices.The low rank compression in HSS limits fill-in and reduces complexity of the solver.The HSS matrices are constructed using randomized sampling and rank-revealing QR.ULV decomposition replaces the traditional dense LU.The factorization acts as solver or preconditioner.Shared and distributed memory parallel results are presented for a range of applications
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