Parallel Longest Increasing Subsequences in Scalable Time and Memory

  • Peter Krusche
  • Alexander Tiskin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6067)


The longest increasing subsequence (LIS) problem is a classical problem in theoretical computer science and mathematics. Most existing parallel algorithms for this problem have very restrictive slackness conditions which prevent scalability to large numbers of processors. Other algorithms are scalable, but not work-optimal w.r.t. the fastest sequential algorithm for the LIS problem, which runs in time O(n logn) for n numbers in the comparison-based model. In this paper, we propose a new parallel algorithm for the LIS problem. Our algorithm solves the more general problem of semi-local comparison of permutation strings of length n in time O(n 1.5 / p) on p processors, has scalable communication cost of \(O(n/\sqrt{p})\) and is synchronisation-efficient. Furthermore, we achieve scalable memory cost, requiring \(O(n/\sqrt{p})\) of storage on each processor. When applied to LIS computation, this algorithm is superior to previous approaches since computation, communication, and memory costs are all scalable.


Parallel Algorithm Communication Cost Sequential Algorithm Input String Longe Common Subsequence 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Peter Krusche
    • 1
  • Alexander Tiskin
    • 1
  1. 1.DIMAP and Department of Computer ScienceThe University of WarwickCoventryUK

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