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Finding Strongly Connected Components and Topological Sort in Parallel using O(log² n) reachability queries Warren Schudy Brown University Work done while.

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Presentation on theme: "Finding Strongly Connected Components and Topological Sort in Parallel using O(log² n) reachability queries Warren Schudy Brown University Work done while."— Presentation transcript:

1 Finding Strongly Connected Components and Topological Sort in Parallel using O(log² n) reachability queries Warren Schudy Brown University Work done while interning at Google Research Mountain View

2 A Scheduling Problem To-do List –Topological sort (TS) –Strongly connected components (SCC) –Reachability  SCC application in scientific computing requiring parallelism [McLendon et al. 01]

3 Previous Results for TS, SCC and Reachability Assume sparse graph with n vertices using 1 ≤ p ≤ n 4/3 processors: Question: is reachability fundamentally easier to parallelize than SCC and TS? (In Transactions of Information Processing Society of Japan ’99 ’04, Akio, Masahiro and Ryozo claim runtime n/p for TS & SCC) Coppersmith and Winograd ‘87 T. Spencer ‘97Ullman & Yannakakis ‘91 Runtime (ignore logs) n 2.38… /pn/p 1/3 n/p 1/2 ProblemsAll Reachability

4 Answer: no (up to logs) Our main result: a reduction of SCC and TS to O(log 2 n) reachability queries Remainder of talk focuses on SCC problem This workCoppersmith et al.SpencerUllman et al. Runtimen/p 1/2 n 2.38 /pn/p 1/3 n/p 1/2 ProblemsTS and SCCAll Reachability

5 Desc(s) SCC(s) V \ Desc(s) A simple SCC algorithm Choose random vertex s  V Determine SCC(s) and output it Determine the vertices Desc(s) reachable from s Recurse (in parallel) on: –Desc(s) \ SCC(s) –V \ Desc(s) s (Similar to [Coppersmith, Fleischer, Hendrickson and Pinar ’05])

6 High-runtime instance Desc(s) s

7 Algorithmic Idea If this algorithm divided the graph roughly in two, recursion depth would be log n So pick many source vertices instead of 1 (number chosen later) Desc( )

8 Make one pivot vertex s special, and output its SCC Outputting SCCs s Desc( ) Desc(s) Recurse on blue, green, yellow and unreached subgraphs SCC(s)

9 Our Multipivot Algorithm Permute the vertices randomly Determine the smallest s s.t. {1,2,…s} together reach at least half the edges (binary search) Output SCC(s) Recurse on: –V \ Desc(1…s) –(Desc(1…s-1)  Desc(s)) \ SCC(s) –Desc(1…s-1) \ Desc(s) –Desc(s) \ (Desc(1…s- 1)  SCC(s)) s= Desc(1…s-1) Desc(s) SCC(s)

10 Runtime Analysis k2k2 May contain almost all the edges, but will contain only some of the transitive closure edges (due to random order) s= Desc(1…s-1) Desc(s) Each contains less than half the edges by definition of s SCC(s) k

11 Key Lemma 9 Number of edges in transitive closure of Desc(s) \ (Desc(1…s-1)  SCC(s)) is at most 3/4 that of the parent subgraph V Correction for proof of Lemma 10: "vertices strictly between g(v) and v" other than v after

12 Open question Are there better parallel algorithms for reachability? E.g. can reachability on a 3-regular digraph be computed in o(√n) time using n processors? Ullman & Yannakakis takes O~(√n) time.

13 Acknowledgements & Questions D. Sivakumar Claire Mathieu Maurice Herlihy Glencora Borradaile

14 **Extra slides**

15 Combining Reachability Queries on subgraphs Subgraph 2Subgraph 1 Sources Super-source


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