Systolic algorithms for the longest common subsequence problem

Yang, Chang‐Biau; Lee, R. C. T.

doi:10.1080/02533839.1987.9677028

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Introduction3

Tracings In the Alignment Lattice1

Introduction Biosequences Comparison [1]-[5]1

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Cited by 20 publications

(16 citation statements)

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“…O (nm) time, where n and m denote the lengths of the two given strings [15]. Some improved algorithms have also been proposed, they include reducing the required space, considering smaller number of matching elements, using the parallelism and considering other important properties [7,8,13,16].…”

Section: Introductionmentioning

confidence: 98%

A fast and simple algorithm for computing the longest common subsequence of run-length encoded strings

Ann

Yang

Tseng

et al. 2008

Information Processing Letters

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Section: Introductionmentioning

confidence: 98%

A fast and simple algorithm for computing the longest common subsequence of run-length encoded strings

Ann

Yang

Tseng

et al. 2008

Information Processing Letters

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“…Surveys on processor arrays for the LCS problem and related problems can be found in [9,25]. Yang and Lee [30] proposed two linear systolic arrays for the LLCS problem. One of them allow the two strings x and y flow from opposite sides of the array of cells.…”

Section: Introductionmentioning

confidence: 99%

New Processor Array Architectures for the Longest Common Subsequence Problem

Michailidis

Margaritis

2005

J Supercomput

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A longest common subsequence (LCS) of two strings is a common subsequence of two strings of maximal length. The LCS problem is to find an LCS of two given strings and the length of the LCS (LLCS). In this paper, we present a new linear processor array for solving the LCS problem. The array is based on parallelization of a recent LCS algorithm which consists of two phases, i.e. preprocessing and computation. The computation phase is based on bit-level dynamic programming approach. Implementations of the preprocessing and computation phases are discussed on the same processor array architecture for the LCS problem. Further, we propose a block processor array architecture which reduces the overall communication and time requirements. Finally, we develop a performance model for estimating the performance of the processor array architecture on Pentium processors.

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“…The LCS can be applied to many areas, such as file comparison, speech recognition, and especially bioinformatics [2,9]. In bioinformatics, the LCS of a set of sequences can be regarded as the identity of the input sequences and we can reconstruct an alignment based on the LCS.…”

Section: Introductionmentioning

confidence: 99%