In this paper, we present a preconditioning algorithm for sparse, symmetric, diagonally-dominant(SDD) linear systems by using combinatorial preconditioning techniques. Comparing to incomplete LU factorization preconditioners and AMG preconditioners, combinatorial preconditioners have good parallel scalability. Moreover they meet the flux conservation and quivalent resistance principle. As a SDD matrix can be converted to a Laplacian matrix which is isomorphic to an undireceted graph. Based on this fact, we first construct a low stretch spanning tree of the graph corresponding to the SDD coefficient matrix by using Ofer's et al algorithm. Then we partition the tree into subtrees based on a tree-decomposition algorithm and add appropriate edges to get an stretch optimized subgraph. Finally, convert the subgraph to a SDD matrix and take it as a preconditioner. We show experimentally that these combinatorial preconditioners are more efficient than incomplete Cholesky factorization preconditioners with modification and without modification. Moreover, these combinatorial precontioners are insensitive to the matrix ordering and problem boundary.
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