Abstract
We present the design and analysis of a nearly-linear work parallel algorithm for solving symmetric diagonally dominant (SDD) linear systems. On input an SDD n-by-n matrix A with m nonzero entries and a vector b, our algorithm computes a vector \(\tilde{x}\) such that \(\|\tilde{x} - A^{+}b\|_{A} \leq\varepsilon\cdot\|{A^{+}b}\|_{A}\) in \(O(m\log^{O(1)}{n}\log {\frac{1}{\varepsilon}})\) work and \(O(m^{1/3+\theta}\log\frac{1}{\varepsilon})\) depth for any θ>0, where A + denotes the Moore-Penrose pseudoinverse of A.
The algorithm relies on a parallel algorithm for generating low-stretch spanning trees or spanning subgraphs. To this end, we first develop a parallel decomposition algorithm that in O(mlogO(1) n) work and polylogarithmic depth, partitions a graph with n nodes and m edges into components with polylogarithmic diameter such that only a small fraction of the original edges are between the components. This can be used to generate low-stretch spanning trees with average stretch O(n α) in O(mlogO(1) n) work and O(n α) depth for any α>0. Alternatively, it can be used to generate spanning subgraphs with polylogarithmic average stretch in O(mlogO(1) n) work and polylogarithmic depth. We apply this subgraph construction to derive a parallel linear solver.
By using this solver in known applications, our results imply improved parallel randomized algorithms for several problems, including single-source shortest paths, maximum flow, minimum-cost flow, and approximate maximum flow.
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Notes
The Spielman-Teng solver and all subsequent improvements are randomized algorithms. Consequently, all algorithms relying on the solvers are also randomized. For simplicity, we omit standard complexity factors related to the probability of error.
I.e. linear up to polylogarithmic factors.
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Acknowledgements
This work is partially supported by the National Science Foundation under grant numbers CCF-1018463, CCF-1018188, and CCF-1016799, CCF-1149048, by an Alfred P. Sloan Fellowship, and by generous gifts from IBM, Intel, and Microsoft.
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Blelloch, G.E., Gupta, A., Koutis, I. et al. Nearly-Linear Work Parallel SDD Solvers, Low-Diameter Decomposition, and Low-Stretch Subgraphs. Theory Comput Syst 55, 521–554 (2014). https://doi.org/10.1007/s00224-013-9444-5
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DOI: https://doi.org/10.1007/s00224-013-9444-5