Nearly-Linear Work Parallel SDD Solvers, Low-Diameter Decomposition, and Low-Stretch Subgraphs

Guy E. Blelloch, Anupam Gupta, Ioannis Koutis, Gary L. Miller, Richard Peng, Kanat Tangwongsan

Research output: Contribution to journalArticlepeer-review

28 Scopus citations


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 x̃ such that (Formula Persented) work and (Formula Persented) 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.

Original languageEnglish (US)
Pages (from-to)521-554
Number of pages34
JournalTheory of Computing Systems
Issue number3
StatePublished - Oct 2014
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computational Theory and Mathematics


  • Linear systems
  • Low-diameter decomposition
  • Low-stretch spanning trees
  • Low-stretch subgraphs
  • Parallel algorithms


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