Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
A linear space algorithm for computing maximal common subsequences
Communications of the ACM
A fast and practical bit-vector algorithm for the longest common subsequence problem
Information Processing Letters
Understanding throughput-oriented architectures
Communications of the ACM
Programming Massively Parallel Processors: A Hands-on Approach
Programming Massively Parallel Processors: A Hands-on Approach
CUDA by Example: An Introduction to General-Purpose GPU Programming
CUDA by Example: An Introduction to General-Purpose GPU Programming
Parallel longest common subsequence using graphics hardware
EG PGV'08 Proceedings of the 8th Eurographics conference on Parallel Graphics and Visualization
Fast Longest Common Subsequence with General Integer Scoring Support on GPUs
Proceedings of Programming Models and Applications on Multicores and Manycores
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The longest common subsequence (LCS for short) for given two strings has various applications, e.g. comparison of DNAs. In this paper, we propose a GPU algorithm to accelerate Hirschberg's CPU LCS algorithm improved using Crochemore et al's bit-parallel CPU algorithm. Crochemore's algorithm includes bitwise logical operators which can be computed in embarrassingly parallel. However, it also includes an operator with less parallelism, i.e. an arithmetic sum. In this paper, we focus on how to implement these operators efficiently in parallel. Our experiments with 2.93GHz Intel Core i3 530 CPU, GeForce 8800 GTX, GTX 285, and GTX 480 GPUs show that the proposed algorithm runs maximum 12.77 times faster than the bit-parallel CPU algorithm and maximum 76.5 times faster than Hirschberg's LCS CPU algorithm. Furthermore, the proposed algorithm runs 10.9 to 18.1 times faster than Kloetzli's existing GPU algorithm.