Parallel parsing on a one-way array of finite-state machines
IEEE Transactions on Computers
The multiple sequence alignment problem in biology
SIAM Journal on Applied Mathematics
Parallel processing of biological sequence comparison algorithms
International Journal of Parallel Programming
Efficient parallel algorithms for string editing and related problems
SIAM Journal on Computing
Linear array with a reconfigurable pipelined bus system—concepts and applications
Information Sciences: an International Journal - special issue on parallel and distributed processing
Systolic-based parallel architecture for the longest common subsequences problem
Integration, the VLSI Journal
Time-efficient parallel algorithms for the longest common subsequence and related problems
Journal of Parallel and Distributed Computing
Bounds on the Complexity of the Longest Common Subsequence Problem
Journal of the ACM (JACM)
A linear space algorithm for computing maximal common subsequences
Communications of the ACM
Parallel Algorithms for the Longest Common Subsequence Problem
IEEE Transactions on Parallel and Distributed Systems
A Survey of Longest Common Subsequence Algorithms
SPIRE '00 Proceedings of the Seventh International Symposium on String Processing Information Retrieval (SPIRE'00)
Parallel Algorithms for the Longest Common Subsequence Problem
HIPC '97 Proceedings of the Fourth International Conference on High-Performance Computing
Information Processing Letters
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Searching for the longest common substring (LCS) of biosequences is one of the most important tasks in Bioinformatics. A fast algorithm for LCS problem named FAST_LCS is presented. The algorithm first seeks the successors of the initial identical character pairs according to a successor table to obtain all the identical pairs and their levels. Then by tracing back from the identical character pair at the largest level, the result of LCS can be obtained. For two sequences X and Y with lengths n and m, the memory required for FAST_LCS is max{8*(n+1)+8*(m+1),L}, here L is the number of identical character pairs and time complexity of parallel implementation is O(|LCS(X, Y)|), here, |LCS(X, Y)| is the length of the LCS of X, Y. Experimental result on the gene sequences of tigr database shows that our algorithm can get exactly correct result and is faster and more efficient than other LCS algorithms.